Deepagent Clinic — API

Paste the agent code, get a structured production-readiness review.

API tokens Open the app

Review your Deep Agents or LangGraph code from your own scripts

Send agent source — one file or several, each preceded by a # file: agent.py comment — and get back one JSON object: a production-readiness posture, the inventory of every agent construct with its role, prioritized findings across correctness, context, tools, safety, reliability and hygiene, each with a corrected Python/TS fragment, quick wins, and the focus areas to work through first. Everything this app does goes through the SkillSafe App API — plain JSON over HTTPS — so you can hang a review off any pull request that touches the agent. Wire it into whatever produces or reviews your agents: a pre-merge check on agents/, a scheduled safety audit, or an editor command. Pick a language once and the whole page follows.

Basics

Base URL: https://api.skillsafe.ai/v1/app-api, app slug deepagent-clinic. Every request sends Authorization: Bearer <token> and JSON bodies with Content-Type: application/json. Responses are wrapped in an envelope: {"data": …} on success, {"error": {"code", "message"}} on failure. The review itself is produced by the gpt-terra model. Estimates are free; runs are metered against your credit balance. There is a single run task — one bundle of agent code in, one review out, no follow-up calls and no session state to carry.

StatusMeaning
401Missing or expired token — create a new session.
402Not enough credits — top up at skillsafe.ai/account/credits.
403The token isn't allowed to do this (e.g. a guest reviewing a very large agent bundle).
404Unknown job or record id.
5xxTransient platform error — retry with backoff.

Browsers enforce CORS for this API, so run these examples from a server, script or terminal — not from another website's frontend.

Step 0 — A tiny client

Every task below is a single HTTP call, so start with a short helper that adds the auth header, sends JSON and unwraps the data envelope. The later steps reuse it.

export API="https://api.skillsafe.ai/v1/app-api"
export TOKEN="YOUR_TOKEN"      # see step 1

# every call looks like:
#   curl -s "$API/..." -H "Authorization: Bearer $TOKEN" [-d '{json}']
# jq is used below to pull fields out of the {"data": ...} envelope
import json, requests

API = "https://api.skillsafe.ai/v1/app-api"
TOKEN = "YOUR_TOKEN"  # see step 1 — read it from your shell environment in real code

def api(method, path, body=None, **headers):
    res = requests.request(method, API + path, json=body,
                           headers={"Authorization": f"Bearer {TOKEN}", **headers})
    payload = res.json()
    if not res.ok:
        raise RuntimeError(payload.get("error", {}).get("message", res.reason))
    return payload["data"]
// Node 18+ (built-in fetch)
const API = "https://api.skillsafe.ai/v1/app-api";
const TOKEN = "YOUR_TOKEN"; // see step 1 — read it from your shell environment in real code

async function api(method, path, body, extraHeaders = {}) {
  const res = await fetch(API + path, {
    method,
    headers: { Authorization: `Bearer ${TOKEN}`, "Content-Type": "application/json", ...extraHeaders },
    body: body === undefined ? undefined : JSON.stringify(body),
  });
  const json = await res.json();
  if (!res.ok) throw new Error(json.error?.message ?? res.statusText);
  return json.data;
}
package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"net/http"
	"os"
)

const API = "https://api.skillsafe.ai/v1/app-api"

var token = os.Getenv("SKILLSAFE_TOKEN") // see step 1

func call(method, path string, body, out any) error {
	var buf bytes.Buffer
	if body != nil {
		json.NewEncoder(&buf).Encode(body)
	}
	req, _ := http.NewRequest(method, API+path, &buf)
	req.Header.Set("Authorization", "Bearer "+token)
	req.Header.Set("Content-Type", "application/json")
	res, err := http.DefaultClient.Do(req)
	if err != nil {
		return err
	}
	defer res.Body.Close()
	var env struct {
		Data  json.RawMessage `json:"data"`
		Error *struct{ Message string `json:"message"` } `json:"error"`
	}
	json.NewDecoder(res.Body).Decode(&env)
	if res.StatusCode >= 400 {
		return fmt.Errorf("api %s %s: %s", method, path, env.Error.Message)
	}
	if out == nil {
		return nil
	}
	return json.Unmarshal(env.Data, out)
}
// Java 17+, no dependencies. Pair with your JSON library (Jackson, Gson…)
// to read fields out of the returned envelope.
import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;

public class SkillSafe {
    static final String API = "https://api.skillsafe.ai/v1/app-api";
    static final String TOKEN = System.getenv("SKILLSAFE_TOKEN"); // see step 1
    static final HttpClient HTTP = HttpClient.newHttpClient();

    static String api(String method, String path, String jsonBody) throws Exception {
        var req = HttpRequest.newBuilder(URI.create(API + path))
            .header("Authorization", "Bearer " + TOKEN)
            .header("Content-Type", "application/json")
            .method(method, jsonBody == null
                ? HttpRequest.BodyPublishers.noBody()
                : HttpRequest.BodyPublishers.ofString(jsonBody))
            .build();
        var res = HTTP.send(req, HttpResponse.BodyHandlers.ofString());
        if (res.statusCode() >= 400) throw new RuntimeException(res.body());
        return res.body(); // envelope: {"data": …}
    }
}
require "net/http"
require "json"

API = "https://api.skillsafe.ai/v1/app-api"
TOKEN = ENV.fetch("SKILLSAFE_TOKEN") # see step 1

def api(method, path, body = nil)
  uri = URI(API + path)
  req = Net::HTTP.const_get(method.capitalize).new(uri)
  req["Authorization"] = "Bearer #{TOKEN}"
  req["Content-Type"] = "application/json"
  req.body = body.to_json if body
  res = Net::HTTP.start(uri.host, uri.port, use_ssl: true) { |h| h.request(req) }
  payload = JSON.parse(res.body)
  raise (payload.dig("error", "message") || res.message) unless res.is_a?(Net::HTTPSuccess)
  payload["data"]
end
<?php
const API = "https://api.skillsafe.ai/v1/app-api";
$TOKEN = getenv("SKILLSAFE_TOKEN"); // see step 1

function api(string $method, string $path, ?array $body = null): mixed {
    global $TOKEN;
    $ch = curl_init(API . $path);
    curl_setopt_array($ch, [
        CURLOPT_CUSTOMREQUEST  => $method,
        CURLOPT_RETURNTRANSFER => true,
        CURLOPT_HTTPHEADER     => [
            "Authorization: Bearer $TOKEN",
            "Content-Type: application/json",
        ],
        CURLOPT_POSTFIELDS     => $body === null ? null : json_encode($body),
    ]);
    $payload = json_decode(curl_exec($ch), true);
    $status  = curl_getinfo($ch, CURLINFO_RESPONSE_CODE);
    curl_close($ch);
    if ($status >= 400) {
        throw new Exception($payload["error"]["message"] ?? "HTTP $status");
    }
    return $payload["data"];
}
// .NET 8+
using System.Net.Http.Json;
using System.Text.Json;

static class SkillSafe
{
    const string Api = "https://api.skillsafe.ai/v1/app-api";
    static readonly HttpClient Http = new();

    static SkillSafe() =>
        Http.DefaultRequestHeaders.Authorization =
            new("Bearer", Environment.GetEnvironmentVariable("SKILLSAFE_TOKEN")); // see step 1

    public static async Task<JsonElement> ApiAsync(HttpMethod method, string path, object? body = null)
    {
        var req = new HttpRequestMessage(method, Api + path);
        if (body != null) req.Content = JsonContent.Create(body);
        var res = await Http.SendAsync(req);
        var json = await res.Content.ReadFromJsonAsync<JsonElement>();
        if (!res.IsSuccessStatusCode)
            throw new Exception(json.GetProperty("error").GetProperty("message").GetString());
        return json.GetProperty("data");
    }
}

Step 1 — Get a token

POST /guest

A guest token lets you check balances and estimate costs for free. For metered review runs billed to your own account, use your personal token: open the token page, sign in with SkillSafe, and press Copy shell export — it puts export SKILLSAFE_TOKEN="…" on your clipboard, which every example below reads. Treat the token like a password: it can spend your credits. For fully headless scripts, POST /guest mints a guest token with no browser involved.

curl -s -X POST "$API/guest" \
  -H "Content-Type: application/json" \
  -d '{"slug":"deepagent-clinic"}' | jq -r '.data.token'
token = api("POST", "/guest", {"slug": "deepagent-clinic"})["token"]
const { token } = await api("POST", "/guest", { slug: "deepagent-clinic" });
var guest struct{ Token string `json:"token"` }
err := call("POST", "/guest", map[string]string{"slug": "deepagent-clinic"}, &guest)
String envelope = api("POST", "/guest", """
    {"slug":"deepagent-clinic"}""");
// token is at data.token in the returned JSON
token = api("POST", "/guest", { slug: "deepagent-clinic" })["token"]
$token = api("POST", "/guest", ["slug" => "deepagent-clinic"])["token"];
var guest = await SkillSafe.ApiAsync(HttpMethod.Post, "/guest",
    new { slug = "deepagent-clinic" });
var token = guest.GetProperty("token").GetString();

The app stores this browser's token under the localStorage key skillsafe_app_token:deepagent-clinic, on the app's own origin. The token page reads and manages it for you — you never need to open developer tools.

Step 2 — Check who you are and your balance

GET /me

Returns subject_type ("user" or "guest"), subject_id and your credits balance. Check this before reviewing a large bundle.

curl -s "$API/me" -H "Authorization: Bearer $TOKEN" | jq '.data'
me = api("GET", "/me")
print(me["subject_type"], me["credits"])
const me = await api("GET", "/me");
console.log(me.subject_type, me.credits);
var me struct {
	SubjectType string `json:"subject_type"`
	Credits     int64  `json:"credits"`
}
err := call("GET", "/me", nil, &me)
String envelope = api("GET", "/me", null);
// data.subject_type, data.credits
me = api("GET", "/me")
puts "#{me["subject_type"]}: #{me["credits"]} credits"
$me = api("GET", "/me");
echo "{$me['subject_type']}: {$me['credits']} credits\n";
var me = await SkillSafe.ApiAsync(HttpMethod.Get, "/me");
Console.WriteLine($"{me.GetProperty("subject_type")}: {me.GetProperty("credits")} credits");

Step 3 — Estimate the cost

POST /estimate

Send exactly the input you would send to /run; the response's hold_credits is the worst-case cost. Nothing is charged and no job is created, so estimating is free — useful when you are piping a whole agents/ directory in and want a ceiling before spending credits.

Input fieldTypeNotes
codestring, requiredThe agent source: agent construction, tool definitions, sub-agent configs, prompts, middleware, graph wiring, config. One file or several concatenated, each preceded by a # file: agent.py or // file: agent.ts comment. This is the model's only evidence — nothing is executed and no agent is run. Inputs longer than 100,000 characters are clipped middle-out, with a # [... clipped ...] comment showing where. At least 60 characters are needed for a review.
frameworkstringpython | typescript | mixed | unknown — changes what is idiomatic: Python deepagents/LangGraph uses decorators and dict configs; deepagents.js uses object literals and zod schemas. Every corrected snippet comes back in the language you name.
concernstringgeneral | correctness | context | safety | production — the review emphasis. It weights the findings and the summary, but it is emphasis and not exclusivity: a high-severity finding from another category is never suppressed.
contextstring, optionalExtra context: deepagents/LangGraph version, where the agent runs and what traffic it sees, which tools hit real systems versus sandboxes, what is already handled outside the pasted files, and any gap you have already chosen to accept. Clipped at 20,000 characters.
prescan_factsobject, optionalWhat a client-side scanner mechanically matched in the code before the run: {"resources": [], "flags": []}. Each entry is {id, label}. resources is the construct inventory it parsed — ids look like res:agent/research-agent, res:tool/send-report, res:subagent/critic, res:prompt/system_prompt, res:middleware/..., res:checkpointer/memorysaver, res:store/..., res:graph/stategraph. flags are the deterministic checks that fired, with ids <check>:<name>secret-literal:..., undocumented-tool:send-report, no-hitl:send-report, no-checkpointer:agent.py, memory-persistence:memorysaver, bare-shell:31, silent-except:44, unbounded-loop:52, prompt-interpolation:..., sync-sleep:18, giant-tool-output:26, model-pinned:claude-sonnet-4-5-20250929. Every flag id you send comes back reconciled in coverage_check. The web UI fills this from its own prescan; API callers may omit the field or send the two empty arrays.
retry_notestring, optionalOnly set by the app's automatic reformat retry when a first reply was not valid JSON. Leave it out.
cat > agent.py <<'CODE'
from deepagents import create_deep_agent
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver

@tool
def send_report(to: str, body: str) -> str:
    return mailer.send(to, body)

agent = create_deep_agent(
    tools=[send_report],
    system_prompt="You are a research assistant.",
    model="claude-sonnet-4-5-20250929",
    checkpointer=MemorySaver(),
)
result = agent.invoke(
    {"messages": [{"role": "user", "content": task}]},
    {"configurable": {"thread_id": "t1"}},
)
CODE

jq -n --rawfile code agent.py \
  '{code: $code,
    framework: "python",
    concern: "production",
    context: "deepagents 0.2 on Python 3.12, runs as a long-lived internal service.",
    prescan_facts: {resources: [], flags: []}}' > input.json

curl -s -X POST "$API/estimate" \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -d @input.json | jq '.data.hold_credits'
CODE = """from deepagents import create_deep_agent
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver

@tool
def send_report(to: str, body: str) -> str:
    return mailer.send(to, body)

agent = create_deep_agent(
    tools=[send_report],
    system_prompt="You are a research assistant.",
    model="claude-sonnet-4-5-20250929",
    checkpointer=MemorySaver(),
)
result = agent.invoke(
    {"messages": [{"role": "user", "content": task}]},
    {"configurable": {"thread_id": "t1"}},
)
"""

payload = {
    "code": CODE,
    "framework": "python",
    "concern": "production",
    "context": "deepagents 0.2 on Python 3.12, runs as a long-lived internal service.",
    "prescan_facts": {"resources": [], "flags": []},
}

est = api("POST", "/estimate", payload)
print("worst case:", est.get("hold_credits", est.get("credits")), "credits")
const code = [
  'from deepagents import create_deep_agent',
  'from langchain_core.tools import tool',
  'from langgraph.checkpoint.memory import MemorySaver',
  '',
  '@tool',
  'def send_report(to: str, body: str) -> str:',
  '    return mailer.send(to, body)',
  '',
  'agent = create_deep_agent(',
  '    tools=[send_report],',
  '    system_prompt="You are a research assistant.",',
  '    model="claude-sonnet-4-5-20250929",',
  '    checkpointer=MemorySaver(),',
  ')',
  'result = agent.invoke(',
  '    {"messages": [{"role": "user", "content": task}]},',
  '    {"configurable": {"thread_id": "t1"}},',
  ')',
].join("\n");

const payload = {
  code,
  framework: "python",
  concern: "production",
  context: "deepagents 0.2 on Python 3.12, runs as a long-lived internal service.",
  prescan_facts: { resources: [], flags: [] },
};

const est = await api("POST", "/estimate", payload);
console.log("worst case:", est.hold_credits ?? est.credits, "credits");
const code = `from deepagents import create_deep_agent
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver

@tool
def send_report(to: str, body: str) -> str:
    return mailer.send(to, body)

agent = create_deep_agent(
    tools=[send_report],
    system_prompt="You are a research assistant.",
    model="claude-sonnet-4-5-20250929",
    checkpointer=MemorySaver(),
)
result = agent.invoke(
    {"messages": [{"role": "user", "content": task}]},
    {"configurable": {"thread_id": "t1"}},
)`

payload := map[string]any{
	"code":    code,
	"framework": "python",
	"concern":     "production",
	"context":     "deepagents 0.2 on Python 3.12, runs as a long-lived internal service.",
	"prescan_facts": map[string]any{
		"resources": []any{}, "flags": []any{},
	},
}

var est struct{ HoldCredits int64 `json:"hold_credits"` }
err := call("POST", "/estimate", payload, &est)
String code = """
    from deepagents import create_deep_agent
    from langchain_core.tools import tool
    from langgraph.checkpoint.memory import MemorySaver

    @tool
    def send_report(to: str, body: str) -> str:
        return mailer.send(to, body)

    agent = create_deep_agent(
        tools=[send_report],
        system_prompt="You are a research assistant.",
        model="claude-sonnet-4-5-20250929",
        checkpointer=MemorySaver(),
    )
    result = agent.invoke(
        {"messages": [{"role": "user", "content": task}]},
        {"configurable": {"thread_id": "t1"}},
    )
    """;

String jsonPayload = """
    {"code": %s,
     "framework": "python",
     "concern": "production",
     "context": "deepagents 0.2 on Python 3.12, runs as a long-lived internal service.",
     "prescan_facts": {"resources": [], "flags": []}}
    """.formatted(toJsonString(code));

String envelope = api("POST", "/estimate", jsonPayload);
// worst-case cost is at data.hold_credits
CODE = <<~'PY'
  from deepagents import create_deep_agent
  from langchain_core.tools import tool
  from langgraph.checkpoint.memory import MemorySaver

  @tool
  def send_report(to: str, body: str) -> str:
      return mailer.send(to, body)

  agent = create_deep_agent(
      tools=[send_report],
      system_prompt="You are a research assistant.",
      model="claude-sonnet-4-5-20250929",
      checkpointer=MemorySaver(),
  )
  result = agent.invoke(
      {"messages": [{"role": "user", "content": task}]},
      {"configurable": {"thread_id": "t1"}},
  )
PY

payload = { code: CODE,
            framework: "python",
            concern: "production",
            context: "deepagents 0.2 on Python 3.12, runs as a long-lived internal service.",
            prescan_facts: { resources: [], flags: [] } }

est = api("POST", "/estimate", payload)
puts "worst case: #{est["hold_credits"] || est["credits"]} credits"
$code = <<<'PY'
from deepagents import create_deep_agent
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver

@tool
def send_report(to: str, body: str) -> str:
    return mailer.send(to, body)

agent = create_deep_agent(
    tools=[send_report],
    system_prompt="You are a research assistant.",
    model="claude-sonnet-4-5-20250929",
    checkpointer=MemorySaver(),
)
result = agent.invoke(
    {"messages": [{"role": "user", "content": task}]},
    {"configurable": {"thread_id": "t1"}},
)
PY;

$payload = [
    "code"          => $code,
    "framework"     => "python",
    "concern"       => "production",
    "context"       => "deepagents 0.2 on Python 3.12, runs as a long-lived internal service.",
    "prescan_facts" => ["resources" => [], "flags" => []],
];

$est = api("POST", "/estimate", $payload);
echo "worst case: " . ($est["hold_credits"] ?? $est["credits"]) . " credits\n";
var code = """
    from deepagents import create_deep_agent
    from langchain_core.tools import tool
    from langgraph.checkpoint.memory import MemorySaver

    @tool
    def send_report(to: str, body: str) -> str:
        return mailer.send(to, body)

    agent = create_deep_agent(
        tools=[send_report],
        system_prompt="You are a research assistant.",
        model="claude-sonnet-4-5-20250929",
        checkpointer=MemorySaver(),
    )
    result = agent.invoke(
        {"messages": [{"role": "user", "content": task}]},
        {"configurable": {"thread_id": "t1"}},
    )
    """;

var payload = new {
    code,
    framework = "python",
    concern = "production",
    context = "deepagents 0.2 on Python 3.12, runs as a long-lived internal service.",
    prescan_facts = new {
        resources = Array.Empty<object>(), flags = Array.Empty<object>(),
    },
};

var est = await SkillSafe.ApiAsync(HttpMethod.Post, "/estimate", payload);
Console.WriteLine($"worst case: {est.GetProperty("hold_credits")} credits");

prescan_facts.flags is how you make the review answer for things you already know about. Send {"resources": [{"id": "res:tool/send-report", "label": "Tool/send_report"}], "flags": [{"id": "memory-persistence:memorysaver", "label": "MemorySaver is process-local"}]} and every flag id comes back in coverage_check — addressed by a finding, or set aside with the reason. Nothing you flag is silently dropped, which makes it the field to assert on in a CI check.

Step 4 — Run the review and wait for the result

POST /run
GET /jobs/{job_id}

/run takes the same input as /estimate, places a credit hold and returns a job_id. Poll /jobs/{job_id} every 1–2 seconds until status is succeeded or failed (a run typically takes 30–90 s, since every finding carries a corrected code fragment). Always send an Idempotency-Key header so a network retry can't start a second, double-charged run. The review is in output — usually nested as output.output, and as a JSON string, so parse defensively. The samples below print the posture, the inventory, the prioritized findings and the focus areas, then save the whole object to review.json.

JOB_ID=$(curl -s -X POST "$API/run" \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -H "Idempotency-Key: da-$(date +%s)" \
  -d @input.json | jq -r '.data.job_id')

while :; do
  JOB=$(curl -s "$API/jobs/$JOB_ID" -H "Authorization: Bearer $TOKEN")
  STATUS=$(echo "$JOB" | jq -r '.data.status')
  [ "$STATUS" = "succeeded" ] || [ "$STATUS" = "failed" ] && break
  sleep 2
done

# unwrap the review once, then read it
echo "$JOB" | jq -r '.data.output.output' > review.json

jq -r '
  "\(.review_name) [\(.posture)]: \(.verdict)",
  "",
  "INVENTORY",
  (.inventory[] | "  \(.kind)/\(.name) in \(.scope) - \(.role)"),
  "",
  "FINDINGS",
  (.findings[] | "  [\(.priority)] \(.id) \(.category) \(.resource): \(.problem)"),
  "",
  "QUICK WINS",
  (.quick_wins[] | "  - \(.)"),
  "",
  "FOCUS AREAS",
  (.focus_areas[] | "  \(.area) - \(.why)"),
  "",
  "COVERAGE",
  (.coverage_check[] | "  \(.id): \(if .addressed then "ok" else "SET ASIDE" end) - \(.note)")' \
  review.json

# fail the pipeline on anything critical
jq -e '[.findings[] | select(.priority == "critical")] | length == 0' review.json > /dev/null \
  || { echo "critical findings present"; exit 1; }
import time

job_id = api("POST", "/run", payload,
             **{"Idempotency-Key": "da-001"})["job_id"]

while True:
    job = api("GET", f"/jobs/{job_id}")
    if job["status"] in ("succeeded", "failed"):
        break
    time.sleep(1.5)

if job["status"] == "failed":
    raise RuntimeError(job.get("error", "run failed"))

raw = job["output"]
if isinstance(raw, dict) and "output" in raw:
    raw = raw["output"]
review = json.loads(raw) if isinstance(raw, str) else raw

print(f'{review["review_name"]} [{review["posture"]}]: {review["verdict"]}')
for r in review["inventory"]:
    print(f'  {r["kind"]}/{r["name"]:<24} in={r["scope"] or "-":<16} {r["role"]}')
for f in review["findings"]:
    print(f'  [{f["priority"]:>8}] {f["id"]} {f["category"]} {f["resource"]}')
    print(f'      L:{f["likelihood"]}/S:{f["severity"]} {f["problem"]}')
    print(f'      fix: {f["fix"]}')
    if f["snippet"]:
        print("      snippet:", f["snippet"].splitlines()[0], "...")
for w in review["quick_wins"]:
    print("  win:", w)
for a in review["focus_areas"]:
    print(f'  focus {a["area"]} {a["finding_ids"]} - {a["why"]}')
for c in review["coverage_check"]:
    print(f'  {c["id"]}: {"ok" if c["addressed"] else "SET ASIDE"} - {c["note"]}')

with open("review.json", "w", encoding="utf-8") as fh:
    json.dump(review, fh, indent=2)

critical = [f for f in review["findings"] if f["priority"] == "critical"]
if critical:
    raise SystemExit(f"{len(critical)} critical finding(s)")
import { writeFileSync } from "node:fs";

const { job_id } = await api("POST", "/run", payload,
  { "Idempotency-Key": crypto.randomUUID() });

let job;
do {
  await new Promise((r) => setTimeout(r, 1500));
  job = await api("GET", `/jobs/${job_id}`);
} while (job.status !== "succeeded" && job.status !== "failed");

if (job.status === "failed") throw new Error(job.error ?? "run failed");

const raw = job.output?.output ?? job.output;
const review = typeof raw === "string" ? JSON.parse(raw) : raw;

console.log(`${review.review_name} [${review.posture}]: ${review.verdict}`);
for (const r of review.inventory) {
  console.log(`  ${r.kind}/${r.name} (${r.scope || "-"}): ${r.role}`);
}
for (const f of review.findings) {
  console.log(`  [${f.priority}] ${f.id} ${f.category} ${f.resource}`);
  console.log(`      L:${f.likelihood}/S:${f.severity} - ${f.fix}`);
}
for (const w of review.quick_wins) console.log(`  win: ${w}`);
for (const a of review.focus_areas) {
  console.log(`  focus ${a.area} (${a.finding_ids.join(", ")}): ${a.why}`);
}
for (const c of review.coverage_check) {
  console.log(`  ${c.id}: ${c.addressed ? "ok" : "SET ASIDE"} - ${c.note}`);
}

writeFileSync("review.json", JSON.stringify(review, null, 2));

const critical = review.findings.filter((f) => f.priority === "critical");
if (critical.length) process.exitCode = 1;
var started struct{ JobID string `json:"job_id"` }
if err := call("POST", "/run", payload, &started); err != nil {
	log.Fatal(err)
}

var job struct {
	Status string          `json:"status"`
	Error  string          `json:"error"`
	Output json.RawMessage `json:"output"`
}
for {
	if err := call("GET", "/jobs/"+started.JobID, nil, &job); err != nil {
		log.Fatal(err)
	}
	if job.Status == "succeeded" || job.Status == "failed" {
		break
	}
	time.Sleep(1500 * time.Millisecond)
}

// job.Output is {"output": "<json string>"} — unwrap, then unmarshal:
type Review struct {
	ReviewName string `json:"review_name"`
	Posture    string `json:"posture"`
	Verdict    string `json:"verdict"`
	ExecSummary string `json:"exec_summary"`
	Assumptions   []string `json:"assumptions"`
	OpenQuestions []string `json:"open_questions"`
	Inventory []struct {
		Kind, Name, Scope, Role string
	} `json:"inventory"`
	Findings []struct {
		ID, Category, Severity, Likelihood, Priority string
		Resource, Problem, Impact, Fix, Snippet      string
	} `json:"findings"`
	CoverageCheck []struct {
		ID, Note  string
		Addressed bool
	} `json:"coverage_check"`
	QuickWins  []string `json:"quick_wins"`
	FocusAreas []struct {
		Area, Why  string
		FindingIDs []string `json:"finding_ids"`
	} `json:"focus_areas"`
	Summary string `json:"summary"`
}
var wrapper struct{ Output string `json:"output"` }
json.Unmarshal(job.Output, &wrapper)
var review Review
json.Unmarshal([]byte(wrapper.Output), &review)

fmt.Printf("%s [%s]: %s\n", review.ReviewName, review.Posture, review.Verdict)
for _, r := range review.Inventory {
	fmt.Printf("  %s/%s (%s): %s\n", r.Kind, r.Name, r.Scope, r.Role)
}
for _, f := range review.Findings {
	fmt.Printf("  [%s] %s %s %s: %s\n", f.Priority, f.ID, f.Category, f.Resource, f.Problem)
}
for _, a := range review.FocusAreas {
	fmt.Printf("  focus %s %v: %s\n", a.Area, a.FindingIDs, a.Why)
}
os.WriteFile("review.json", []byte(wrapper.Output), 0o644)
String envelope = api("POST", "/run", jsonPayload);
String jobId = /* data.job_id via your JSON library */;

while (true) {
    String job = api("GET", "/jobs/" + jobId, null);
    String status = /* data.status */;
    if (status.equals("succeeded") || status.equals("failed")) break;
    Thread.sleep(1500);
}
// The review is at data.output.output as a JSON string — parse it again, then read
// review_name, posture, verdict, exec_summary, assumptions[], open_questions[],
// inventory[] (kind/name/scope/role),
// findings[] (id/category/severity/likelihood/priority/resource/problem/impact/fix/snippet),
// coverage_check[] (id/addressed/note), quick_wins[],
// focus_areas[] (area/why/finding_ids[]) and summary.
// Finally keep the review on disk:
//   Files.writeString(Path.of("review.json"), reviewJson);
started = api("POST", "/run", payload)

job = nil
loop do
  job = api("GET", "/jobs/#{started["job_id"]}")
  break if %w[succeeded failed].include?(job["status"])
  sleep 1.5
end
raise (job["error"] || "run failed") if job["status"] == "failed"

raw = job["output"].is_a?(Hash) ? job["output"].fetch("output", job["output"]) : job["output"]
review = raw.is_a?(String) ? JSON.parse(raw) : raw

puts "#{review["review_name"]} [#{review["posture"]}]: #{review["verdict"]}"
review["inventory"].each { |r| puts "  #{r["kind"]}/#{r["name"]} (#{r["scope"]}): #{r["role"]}" }
review["findings"].each do |f|
  puts "  [#{f["priority"]}] #{f["id"]} #{f["category"]} #{f["resource"]}"
  puts "      L:#{f["likelihood"]}/S:#{f["severity"]} - #{f["fix"]}"
end
review["quick_wins"].each { |w| puts "  win: #{w}" }
review["focus_areas"].each { |a| puts "  focus #{a["area"]} #{a["finding_ids"].join(", ")}" }
review["coverage_check"].each { |c| puts "  #{c["id"]}: #{c["addressed"] ? "ok" : "SET ASIDE"}" }

File.write("review.json", JSON.pretty_generate(review))
exit 1 if review["findings"].any? { |f| f["priority"] == "critical" }
$started = api("POST", "/run", $payload);

do {
    sleep(2);
    $job = api("GET", "/jobs/" . $started["job_id"]);
} while (!in_array($job["status"], ["succeeded", "failed"]));

if ($job["status"] === "failed") {
    throw new Exception($job["error"] ?? "run failed");
}

$raw = is_array($job["output"]) ? ($job["output"]["output"] ?? $job["output"]) : $job["output"];
$review = is_string($raw) ? json_decode($raw, true) : $raw;

echo "{$review['review_name']} [{$review['posture']}]: {$review['verdict']}\n";
foreach ($review["inventory"] as $r) {
    echo "  {$r['kind']}/{$r['name']} ({$r['scope']}): {$r['role']}\n";
}
foreach ($review["findings"] as $f) {
    echo "  [{$f['priority']}] {$f['id']} {$f['category']} {$f['resource']}\n";
    echo "      L:{$f['likelihood']}/S:{$f['severity']} - {$f['fix']}\n";
}
foreach ($review["quick_wins"] as $w) {
    echo "  win: $w\n";
}
foreach ($review["focus_areas"] as $a) {
    echo "  focus {$a['area']}: " . implode(", ", $a["finding_ids"]) . "\n";
}
foreach ($review["coverage_check"] as $c) {
    echo "  {$c['id']}: " . ($c["addressed"] ? "ok" : "SET ASIDE") . "\n";
}

file_put_contents("review.json", json_encode($review, JSON_PRETTY_PRINT));
var started = await SkillSafe.ApiAsync(HttpMethod.Post, "/run", payload);
var jobId = started.GetProperty("job_id").GetString();

JsonElement job;
while (true)
{
    job = await SkillSafe.ApiAsync(HttpMethod.Get, $"/jobs/{jobId}");
    var status = job.GetProperty("status").GetString();
    if (status is "succeeded" or "failed") break;
    await Task.Delay(1500);
}

var rawText = job.GetProperty("output").GetProperty("output").GetString();
using var doc = JsonDocument.Parse(rawText!);
var review = doc.RootElement;

Console.WriteLine($"{review.GetProperty("review_name")} " +
                  $"[{review.GetProperty("posture")}]: {review.GetProperty("verdict")}");
foreach (var r in review.GetProperty("inventory").EnumerateArray())
{
    Console.WriteLine($"  {r.GetProperty("kind")}/{r.GetProperty("name")}: {r.GetProperty("role")}");
}
foreach (var f in review.GetProperty("findings").EnumerateArray())
{
    Console.WriteLine($"  [{f.GetProperty("priority")}] {f.GetProperty("id")} " +
                      $"{f.GetProperty("category")} {f.GetProperty("resource")} " +
                      $"(L:{f.GetProperty("likelihood")}/S:{f.GetProperty("severity")})");
}
foreach (var a in review.GetProperty("focus_areas").EnumerateArray())
{
    Console.WriteLine($"  focus {a.GetProperty("area")}: {a.GetProperty("why")}");
}

await File.WriteAllTextAsync("review.json", rawText!);

The model is asked for one JSON object and nothing else, but a stray code fence or preamble is always possible. Strip a leading ```json fence, take the text between the first { and the last }, and only then parse — that is what the app does before it falls back to a retry_note reformat run.

The review object — output schema

One JSON object, always the same shape. Every array is present, and the review is grounded in the pasted source alone: findings cite only agents, tools, sub-agents, prompts and files that actually appear in code, and a construct that is simply absent (no checkpointer, no recursion limit, no approval gate on a destructive tool) is reported against the closest real construct or against (missing from the agent). Where the code is silent on something that changes the verdict you get an entry in assumptions and, if it would change the ranking, in open_questions. Expect five to fifteen findings on a typical agent — a well-built one may honestly yield two or three, and findings is never empty.

FieldTypeMeaning
review_namestringA short title naming the agent, taken from the code's own naming — e.g. support-triage agent — harness review.
posturestringship-ready | hardening-recommended | not-production-ready. See the table below.
verdictstringOne sentence justifying the posture and naming the single most important change.
exec_summarystringTwo or three paragraphs, separated by blank lines, on the dominant themes across the agent.
assumptionsstring[]Explicit assumptions filling gaps the code left open. Read these first — a wrong assumption invalidates the findings built on it.
open_questionsstring[]Questions whose answers would change the ranking.
inventoryarray{kind, name, scope, role} — every Agent, SubAgent, Tool, Prompt, Middleware, Checkpointer, Store, Graph and Config the review parsed out of the code and the part it plays. scope is the file it is defined in.
findingsarrayThe prioritized findings table — ids DA-001, DA-002, … in sequence, at least one entry. Columns are listed below.
coverage_checkarray{id, addressed, note} — one entry per prescan_facts.flags id you sent, each appearing exactly once. See the semantics below.
quick_winsstring[]One-line changes worth doing immediately, ahead of any planning. May be empty when nothing here is a one-liner.
focus_areasarray{area, why, finding_ids} — what to work through first, one sentence tied to the review, and the finding ids that motivate it. Every id in finding_ids exists in findings.
summarystringClosing paragraph: what to fix first, and what risk remains after that.

The three posture values:

postureWhat it means
ship-readyThe agent holds up as written: a grounded system prompt, documented tools with bounded output, sub-agents whose context is genuinely isolated, durable memory where the design needs it, approval gates on irreversible actions. Findings still exist, but they are additions — evals, tracing, cheaper sub-agent models — not blockers. Genuinely well-built agents land here rather than having severity manufactured for them.
hardening-recommendedThe shape is right, but named gaps should be closed before this agent takes real traffic — an in-process checkpointer, an undocumented tool, a tool returning an unbounded payload into the context window.
not-production-readyAt least one pattern will lose state, spend without bound, or take an unsafe action as written: a destructive tool reachable with no approval gate, raw host shell access the model can compose commands for, a secret literal in the agent file, while True around .invoke() with no recursion limit.

Each entry in findings:

ColumnMeaning
idSequential DA-001, DA-002, … — the stable handle referenced from focus_areas[].finding_ids.
categorycorrectness | context | tools | safety | reliability | hygiene. Weighted by the concern you sent, but never restricted to it.
severitylow | medium | high — how bad it is when it bites.
likelihoodlow | medium | high — how likely it is to bite.
prioritycritical | high | medium | low — severity by likelihood. critical is reserved for something that can take an unsafe or irreversible action as written (a destructive tool with no approval gate on an autonomous loop, raw host shell access reachable by the model, a secret shipped in the code) or that will predictably lose user state or spend without bound in normal use, so sort on this field and work top-down. This is also the field to gate a pipeline on.
resourceThe Agent/name, Tool/name, SubAgent/name, Prompt/system_prompt or file this is about — always something that appears in code, or the literal (missing from the agent) when the finding is about an absent construct.
problemWhat is wrong, in this code specifically.
impactWhat happens in production and to the team because of it.
fixThe concrete change to make — not "add validation".
snippetA corrected Python or TypeScript fragment you can paste: the fixed block, correctly indented, matching the framework you sent, not the whole file. Empty string when a snippet would add nothing. Secret values are never echoed — a placeholder appears instead.

coverage_check semantics:

CaseWhat you get
Every flag id you sentEach prescan_facts.flags id appears in coverage_check exactly once. Nothing you flagged is silently dropped, which makes this the field to assert on in a CI check. Ids in prescan_facts.resources are not reconciled here — they shape the inventory instead.
addressed: trueThe flag is covered by the review; note names the finding id that covers it.
addressed: falseThe flag was deliberately set aside; note gives the reason — a check that fired but is not a real problem for this agent (a MemorySaver in a script that is only ever run once, a shell tool that the pasted config already routes to a sandbox backend).
Nothing sentOmit prescan_facts, or send the two empty arrays, and coverage_check comes back empty. The rest of the review is unaffected.

A small, realistic result for the snippet above, trimmed for length:

{
  "review_name": "research assistant — harness review",
  "posture": "not-production-ready",
  "verdict": "send_report can put mail on the wire with no approval gate and no contract the
              model can read; put it behind an interrupt before this agent runs unattended.",
  "exec_summary": "A single deep agent with one tool, a one-line system prompt and an in-process
                   checkpointer. The dominant theme is that nothing bounds the irreversible
                   action: send_report is reachable on every turn, has no docstring telling the
                   model when it is appropriate, and there is no interrupt anywhere in the paste.

                   The second theme is durability. MemorySaver keeps threads in process memory,
                   so the thread_id passed on invoke buys nothing across a restart, and the model
                   id is pinned at the call site rather than coming from configuration.",
  "assumptions": [
    "mailer.send really sends mail, rather than writing to a local outbox in this environment.",
    "The agent runs as a long-lived service, since a thread_id is supplied on invoke."
  ],
  "open_questions": [
    "Does a human see the report before it goes out, somewhere outside the pasted file?",
    "How long do user threads need to survive — one request, or across deploys?"
  ],
  "inventory": [
    { "kind": "Agent", "name": "agent", "scope": "agent.py",
      "role": "The single deep agent; orchestrates the research turn and may send the report." },
    { "kind": "Tool", "name": "send_report", "scope": "agent.py",
      "role": "Sends the finished report by email to an address the model chooses." },
    { "kind": "Prompt", "name": "system_prompt", "scope": "agent.py",
      "role": "One line of role framing; sets no scope, stop condition or output shape." },
    { "kind": "Checkpointer", "name": "MemorySaver", "scope": "agent.py",
      "role": "Holds conversation threads in process memory." }
  ],
  "findings": [
    { "id": "DA-001", "category": "safety",
      "severity": "high", "likelihood": "high", "priority": "critical",
      "resource": "Tool/send_report",
      "problem": "send_report is an irreversible action and no interrupt, approval or
                 human-in-the-loop mechanism appears anywhere in the paste.",
      "impact": "A single bad turn mails the wrong content to an address the model picked, and
                 there is no point at which anyone can stop it. Sent mail cannot be recalled.",
      "fix": "Gate the tool with interrupt_on so the run pauses for approval before the call —
              which also requires the checkpointer fixed in DA-002.",
      "snippet": "agent = create_deep_agent(\n    tools=[send_report],\n    interrupt_on={\"send_report\": True},\n    checkpointer=checkpointer,\n)" },
    { "id": "DA-002", "category": "reliability",
      "severity": "medium", "likelihood": "high", "priority": "high",
      "resource": "Checkpointer/MemorySaver",
      "problem": "Threads are checkpointed to MemorySaver, which lives in process memory only,
                 while invoke passes a thread_id as if the thread were durable.",
      "impact": "Every restart or deploy drops all in-flight conversations with no error shown,
                 and an interrupt-and-resume approval flow has nowhere to resume from.",
      "fix": "Swap MemorySaver for a durable checkpointer — SqliteSaver locally, PostgresSaver
              in production.",
      "snippet": "from langgraph.checkpoint.sqlite import SqliteSaver\n\ncheckpointer = SqliteSaver.from_conn_string(\"threads.db\")" },
    { "id": "DA-003", "category": "tools",
      "severity": "medium", "likelihood": "medium", "priority": "medium",
      "resource": "Tool/send_report",
      "problem": "The tool has no docstring, so its description is empty — the model has no
                 contract describing when to call it or what the arguments mean.",
      "impact": "The tool is called with a guessed recipient and an unbounded body, or skipped
                 entirely, and neither failure is visible from the transcript.",
      "fix": "Add a docstring naming the arguments, the intended moment to call it and what the
              tool returns on failure, and return errors as data rather than raising.",
      "snippet": "@tool\ndef send_report(to: str, body: str) -> str:\n    \"\"\"Email a finished report. Call only after the user approves the draft.\n\n    to: a full email address. body: the final report, plain text.\n    Returns \"sent\" or a readable error string.\n    \"\"\"\n    try:\n        mailer.send(to, body)\n    except MailError as exc:\n        return f\"send failed: {exc}\"\n    return \"sent\"" }
  ],
  "coverage_check": [
    { "id": "no-hitl:send-report", "addressed": true, "note": "DA-001." },
    { "id": "memory-persistence:memorysaver", "addressed": true, "note": "DA-002." },
    { "id": "undocumented-tool:send-report", "addressed": true, "note": "DA-003." },
    { "id": "model-pinned:claude-sonnet-4-5-20250929", "addressed": false,
      "note": "Set aside: pinning is defensible for a single-agent service, but move the id into
               configuration when a second agent needs a different model." }
  ],
  "quick_wins": [
    "Add a docstring to send_report — it is the model's only contract for the tool.",
    "Swap MemorySaver for SqliteSaver.from_conn_string so threads survive a restart."
  ],
  "focus_areas": [
    { "area": "Gate the irreversible action",
      "why": "The one thing this agent can do to the outside world has no approval step.",
      "finding_ids": ["DA-001", "DA-002"] },
    { "area": "Give the model a real contract",
      "why": "A one-line prompt and an undocumented tool leave routing entirely to guesswork.",
      "finding_ids": ["DA-003"] }
  ],
  "summary": "Put send_report behind an interrupt on a durable checkpointer, then document the
              tool and widen the system prompt into a real brief. After that this agent is
              hardening-recommended; add tracing and a small eval set so the next prompt change
              is measurable."
}

This is AI-generated review from source text, not a security or production sign-off: it sees only what you sent, never a real agent run, the real traces or the rest of the codebase. Check assumptions and open_questions before you act on the rankings, run every snippet through your own tests and linter, and keep a human reviewer in the loop.

Step 5 — Stream the review as it is written

POST /run-stream

/run-stream takes exactly the same body as /run but answers with server-sent events, so you can show progress instead of a spinner — useful here because a full findings table with corrected code makes for a long reply. This app's own progress panel is this endpoint. Events are separated by a blank line; each has an event: line and a data: line carrying JSON.

EventPayloadMeaning
job{job_id, status}Sent once, when the job is accepted — show "starting".
delta{text}A chunk of the reply, in order. Append it; the accumulated length is your only progress signal (the total is not known in advance). The app advances its step list by watching for the "review_name", "inventory", "findings", "coverage_check" and "focus_areas" keys as they arrive.
done{job_id, status, charged_credits, output}The final, authoritative result — read the review from output.output rather than trusting concatenated deltas, and the settled price from charged_credits.
error{code, message}Replaces done when the run fails.
# -N disables buffering so events print as they arrive
curl -N -s -X POST "$API/run-stream" \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -H "Idempotency-Key: da-$(date +%s)" \
  -d @input.json

# event: job
# data: {"job_id":"job_...","status":"running"}
#
# event: delta
# data: {"text":"{\"review_name\":\"rese"}
# ...
# event: done
# data: {"job_id":"job_...","status":"succeeded","charged_credits":612,"output":{"output":"{...}"}}
import json, requests

result = None
with requests.post(
    API + "/run-stream",
    headers={"Authorization": f"Bearer {TOKEN}",
             "Idempotency-Key": "da-001"},
    json=payload,
    stream=True,
) as r:
    r.raise_for_status()
    event = None
    for line in r.iter_lines(decode_unicode=True):
        if not line:
            continue
        if line.startswith("event:"):
            event = line[len("event:"):].strip()
        elif line.startswith("data:"):
            data = json.loads(line[len("data:"):].strip())
            if event == "delta":
                print(".", end="", flush=True)          # live progress
            elif event == "done":
                result = data
            elif event == "error":
                raise RuntimeError(data.get("message", "run failed"))

review = json.loads(result["output"]["output"])          # authoritative
print("charged:", result["charged_credits"], "-", review["review_name"])
print("posture:", review["posture"])
for f in review["findings"]:
    print(f'  [{f["priority"]}] {f["id"]} {f["resource"]}: {f["problem"]}')
with open("review.json", "w", encoding="utf-8") as fh:
    json.dump(review, fh, indent=2)
const res = await fetch(API + "/run-stream", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${TOKEN}`,
    "Content-Type": "application/json",
    "Idempotency-Key": crypto.randomUUID(),
  },
  body: JSON.stringify(payload),
});

const reader = res.body.getReader();
const decoder = new TextDecoder();
let buf = "", done = null;

for (;;) {
  const chunk = await reader.read();
  if (chunk.done) break;
  buf += decoder.decode(chunk.value, { stream: true });
  const frames = buf.split("\n\n");
  buf = frames.pop();
  for (const frame of frames) {
    const name = /^event:\s*(.+)$/m.exec(frame)?.[1];
    const body = /^data:\s*(.+)$/m.exec(frame)?.[1];
    if (!name || !body) continue;
    const data = JSON.parse(body);
    if (name === "delta") process.stdout.write(".");   // live progress
    if (name === "done") done = data;
    if (name === "error") throw new Error(data.message ?? "run failed");
  }
}

const review = JSON.parse(done.output.output);
console.log(`\n${done.charged_credits} credits - ${review.review_name} [${review.posture}]`);
for (const f of review.findings) console.log(`  [${f.priority}] ${f.id} ${f.resource}`);
writeFileSync("review.json", JSON.stringify(review, null, 2));
body, _ := json.Marshal(payload)
req, _ := http.NewRequest("POST", API+"/run-stream", bytes.NewReader(body))
req.Header.Set("Authorization", "Bearer "+token)
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Idempotency-Key", "da-001")

res, err := http.DefaultClient.Do(req)
if err != nil {
	log.Fatal(err)
}
defer res.Body.Close()

var event string
var final map[string]any
sc := bufio.NewScanner(res.Body)
sc.Buffer(make([]byte, 0, 64*1024), 4*1024*1024)
for sc.Scan() {
	line := sc.Text()
	switch {
	case strings.HasPrefix(line, "event:"):
		event = strings.TrimSpace(strings.TrimPrefix(line, "event:"))
	case strings.HasPrefix(line, "data:"):
		var data map[string]any
		json.Unmarshal([]byte(strings.TrimPrefix(line, "data:")), &data)
		switch event {
		case "delta":
			fmt.Print(".") // live progress
		case "done":
			final = data
		case "error":
			log.Fatal(data["message"])
		}
	}
}
// final["output"].(map[string]any)["output"].(string) is the review JSON —
// unmarshal it into the Review struct from step 4, then write it to review.json.
// Java 17+ — read the stream line by line instead of buffering the body.
var req = HttpRequest.newBuilder(URI.create(API + "/run-stream"))
    .header("Authorization", "Bearer " + TOKEN)
    .header("Content-Type", "application/json")
    .header("Idempotency-Key", "da-001")
    .POST(HttpRequest.BodyPublishers.ofString(jsonPayload))
    .build();

var res = HTTP.send(req, HttpResponse.BodyHandlers.ofLines());
String event = null, done = null;
for (String line : (Iterable<String>) res.body()::iterator) {
    if (line.startsWith("event:")) {
        event = line.substring(6).trim();
    } else if (line.startsWith("data:")) {
        String data = line.substring(5).trim();
        if ("delta".equals(event)) System.out.print(".");   // live progress
        else if ("done".equals(event)) done = data;
        else if ("error".equals(event)) throw new RuntimeException(data);
    }
}
// parse `done`, then parse data.output.output again — it is a JSON string holding
// review_name, posture, verdict, inventory[], findings[], coverage_check[],
// quick_wins[], focus_areas[] and the rest.
require "net/http"
require "json"

uri = URI(API + "/run-stream")
req = Net::HTTP::Post.new(uri)
req["Authorization"] = "Bearer #{TOKEN}"
req["Content-Type"] = "application/json"
req["Idempotency-Key"] = "da-001"
req.body = payload.to_json

event = nil
done = nil
Net::HTTP.start(uri.host, uri.port, use_ssl: true) do |http|
  http.request(req) do |res|
    res.read_body do |chunk|
      chunk.each_line do |line|
        line = line.strip
        if line.start_with?("event:")
          event = line.delete_prefix("event:").strip
        elsif line.start_with?("data:")
          data = JSON.parse(line.delete_prefix("data:").strip)
          case event
          when "delta" then print "."           # live progress
          when "done"  then done = data
          when "error" then raise (data["message"] || "run failed")
          end
        end
      end
    end
  end
end

review = JSON.parse(done["output"]["output"])
puts "\n#{done["charged_credits"]} credits - #{review["review_name"]} [#{review["posture"]}]"
review["findings"].each { |f| puts "  [#{f["priority"]}] #{f["id"]} #{f["resource"]}" }
File.write("review.json", JSON.pretty_generate(review))
$event = null;
$done  = null;

$ch = curl_init(API . "/run-stream");
curl_setopt_array($ch, [
    CURLOPT_POST       => true,
    CURLOPT_HTTPHEADER => [
        "Authorization: Bearer $TOKEN",
        "Content-Type: application/json",
        "Idempotency-Key: da-001",
    ],
    CURLOPT_POSTFIELDS => json_encode($payload),
    CURLOPT_WRITEFUNCTION => function ($ch, $chunk) use (&$event, &$done) {
        foreach (explode("\n", $chunk) as $line) {
            $line = trim($line);
            if (str_starts_with($line, "event:")) {
                $event = trim(substr($line, 6));
            } elseif (str_starts_with($line, "data:")) {
                $data = json_decode(trim(substr($line, 5)), true);
                if ($event === "delta") { echo "."; }        // live progress
                elseif ($event === "done") { $done = $data; }
                elseif ($event === "error") { throw new Exception($data["message"] ?? "run failed"); }
            }
        }
        return strlen($chunk);
    },
]);
curl_exec($ch);
curl_close($ch);

$review = json_decode($done["output"]["output"], true);
echo "\n{$done['charged_credits']} credits - {$review['review_name']} [{$review['posture']}]\n";
foreach ($review["findings"] as $f) {
    echo "  [{$f['priority']}] {$f['id']} {$f['resource']}\n";
}
file_put_contents("review.json", json_encode($review, JSON_PRETTY_PRINT));
var req = new HttpRequestMessage(HttpMethod.Post, Api + "/run-stream") {
    Content = JsonContent.Create(payload),
};
req.Headers.Add("Idempotency-Key", "da-001");

using var res = await Http.SendAsync(req, HttpCompletionOption.ResponseHeadersRead);
using var reader = new StreamReader(await res.Content.ReadAsStreamAsync());

string? evt = null, done = null;
while (await reader.ReadLineAsync() is { } line)
{
    if (line.StartsWith("event:")) evt = line[6..].Trim();
    else if (line.StartsWith("data:"))
    {
        var data = line[5..].Trim();
        if (evt == "delta") Console.Write(".");            // live progress
        else if (evt == "done") done = data;
        else if (evt == "error") throw new Exception(data);
    }
}

using var final = JsonDocument.Parse(done!);
var text = final.RootElement.GetProperty("output").GetProperty("output").GetString();
using var reviewDoc = JsonDocument.Parse(text!);
var review = reviewDoc.RootElement;
Console.WriteLine($"{review.GetProperty("review_name")} [{review.GetProperty("posture")}]");
foreach (var f in review.GetProperty("findings").EnumerateArray())
    Console.WriteLine($"  [{f.GetProperty("priority")}] {f.GetProperty("id")} {f.GetProperty("resource")}");
await File.WriteAllTextAsync("review.json", text!);

In a browser, the native EventSource only speaks GET, and this endpoint is a POST — read the fetch response body incrementally, as the JavaScript sample above does. On an idempotent replay the server may answer with a plain JSON envelope instead of an event stream; check the Content-Type before you start parsing frames.