debug-skill

name	debug-skill
description	Diagnoses and recovers from complex REST and GraphQL API failures. Enforces reproduction before fixing and verifies via runtime inspection.
hints	- Systematically isolate the failing networking layer from DNS to application semantics before patching. - Inspect GraphQL response payloads closely for inner "errors" arrays even when status is 200 OK. - Differentiate between connection timeouts (firewalls/network) and read timeouts (slow server compute). - Capture and log vendor-specific request tracking IDs (e.g. X-Request-Id) to streamline external support. - Build reproducer curl commands or Vitest regression scripts to isolate and confirm fixed behaviors.

API Testing & Debugging

Drive REST and GraphQL diagnosis through standard terminal tools (curl, nslookup, openssl) and script execution. Isolate the failing layer before guessing at the fix.

When to Use

• API returns unexpected status or body
• Auth fails (401/403 after token refresh, OAuth, API key)
• Works in Postman but fails in code
• Webhook / callback integration debugging
• Building or reviewing API integration tests
• Rate limiting or pagination issues

Skip for UI rendering, DB query tuning, or DNS/firewall infra (escalate).

Core Principle

Isolate the layer, then fix. A 200 OK can hide broken data. A 500 can mask a one-character auth typo. Walk the chain in order; never skip a step.

1. Connectivity   → can we reach the host at all?
1.5 Timeouts      → connect-slow vs read-slow?
2. TLS/SSL        → cert valid and trusted?
3. Auth           → credentials correct and unexpired?
4. Request format → payload shape match server expectations?
5. Response parse → does our code accept what came back?
6. Semantics      → does the data mean what we assume?

5-Minute Quickstart

REST via terminal

# Verbose request/response exchange
terminal('curl -v https://api.example.com/users/1')

# POST with JSON
terminal("""curl -X POST https://api.example.com/users \\
  -H 'Content-Type: application/json' \\
  -H "Authorization: Bearer $TOKEN" \\
  -d '{"name":"test","email":"[e-mail]"}'""")

# Headers only
terminal('curl -sI https://api.example.com/health')

# Pretty-print JSON
terminal('curl -s https://api.example.com/users | python3 -m json.tool')

GraphQL via terminal

terminal("""curl -X POST https://api.example.com/graphql \\
  -H 'Content-Type: application/json' \\
  -H "Authorization: Bearer $TOKEN" \\
  -d '{"query":"{ user(id: 1) { name email } }"}'""")

GraphQL gotcha: servers often return HTTP 200 even when the query failed. Always inspect the errors field regardless of status code:

execute_code('''
import os, requests
resp = requests.post(
    "https://api.example.com/graphql",
    json={"query": "{ user(id: 1) { name email } }"},
    headers={"Authorization": f"Bearer {os.environ['TOKEN']}"},
    timeout=10,
)
data = resp.json()
if data.get("errors"):
    for err in data["errors"]:
        print(f"GraphQL error: {err['message']} (path: {err.get('path')})")
print(data.get("data"))
''')

Python (requests) via execute_code

execute_code('''
import requests
resp = requests.get(
    "https://api.example.com/users/1",
    headers={"Authorization": "Bearer <TOKEN>"},
    timeout=(3.05, 30),  # (connect, read)
)
print(resp.status_code, dict(resp.headers))
print(resp.text[:500])
''')

Layered Debug Flow

Step 1 — Connectivity

terminal('nslookup api.example.com')
terminal('curl -v --connect-timeout 5 https://api.example.com/health')

Failures: DNS not resolving, firewall, VPN required, proxy missing.

Step 1.5 — Timeouts

Distinguish can't reach from reaches but slow:

terminal('''curl -w "dns:%{time_namelookup}s connect:%{time_connect}s tls:%{time_appconnect}s ttfb:%{time_starttransfer}s total:%{time_total}s\\n" \\
  -o /dev/null -s https://api.example.com/endpoint''')

In Python, always pass a tuple timeout — requests has no default and will hang forever:

execute_code('''
import requests
from requests.exceptions import ConnectTimeout, ReadTimeout
try:
    requests.get(url, timeout=(3.05, 30))
except ConnectTimeout:
    print("Cannot reach host — DNS, firewall, VPN")
except ReadTimeout:
    print("Connected but server is slow")
''')

Diagnosis: high time_connect is network/firewall; high time_starttransfer with low time_connect is a slow server.

Step 2 — TLS/SSL

terminal('curl -vI https://api.example.com 2>&1 | grep -E "SSL|subject|expire|issuer"')

Failures: expired cert, self-signed, hostname mismatch, missing CA bundle. Use -k only for ad-hoc debug, never in code.

Step 3 — Authentication

# Token validity check
terminal('curl -s -o /dev/null -w "%{http_code}\\n" -H "Authorization: Bearer $TOKEN" https://api.example.com/me')

# Decode JWT exp claim — handles base64url padding correctly
execute_code('''
import json, base64, os
tok = os.environ["TOKEN"]
payload = tok.split(".")[1]
payload += "=" * (-len(payload) % 4)
print(json.dumps(json.loads(base64.urlsafe_b64decode(payload)), indent=2))
''')

Checklist:

• Token expired? (exp claim in JWT)
• Right scheme? Bearer vs Basic vs Token vs X-Api-Key
• Right environment? Staging key on prod is a classic
• API key in header vs query param (?api_key=…)?

Step 4 — Request Format

terminal("""curl -v -X POST https://api.example.com/endpoint \\
  -H 'Content-Type: application/json' \\
  -d '{"key":"value"}' 2>&1""")

Content-Type / body mismatch — the silent 415/400:

# WRONG — data= sends form-encoded, header lies
requests.post(url, data='{"k":"v"}', headers={"Content-Type": "application/json"})

# RIGHT — json= auto-sets header AND serializes
requests.post(url, json={"k": "v"})

# WRONG — Accept says XML, code calls .json()
requests.get(url, headers={"Accept": "text/xml"})

# RIGHT — let requests build multipart with boundary
requests.post(url, files={"file": open("doc.pdf", "rb")})

Common: form-encoded vs JSON, missing required fields, wrong HTTP method, unencoded query params.

Step 5 — Response Parsing

Always inspect content-type before calling .json():

execute_code('''
import requests
resp = requests.post(url, json=payload, timeout=10)
print(f"status={resp.status_code}")
print(f"headers={dict(resp.headers)}")
ct = resp.headers.get("Content-Type", "")
if "application/json" in ct:
    print(resp.json())
else:
    print(f"unexpected content-type {ct!r}, body={resp.text[:500]!r}")
''')

Failures: HTML error page where JSON expected, empty body, wrong charset.

Step 6 — Semantic Validation

Parsed cleanly — but is the data correct?

• Does "status": "active" mean what your code thinks?
• ID in response matches the one requested?
• Timestamps in expected timezone?
• Pagination returning all results, or just page 1?

HTTP Status Playbook

401 Unauthorized — credentials missing or invalid

1. Authorization header actually present? (curl -v to confirm)
2. Token correct and unexpired?
3. Right auth scheme? (Bearer vs Basic vs Token)
4. Some APIs use query param (?api_key=…) instead of header.

403 Forbidden — authenticated but not authorized

1. Token has the required scopes/permissions?
2. Resource owned by a different account?
3. IP allowlist blocking you?
4. CORS in browser? (check Access-Control-Allow-Origin)

404 Not Found — resource doesn't exist or URL is wrong

1. Path correct? (trailing slash, typo, version prefix)
2. Resource ID exists?
3. Right API version (/v1/ vs /v2/)?
4. Right base URL (staging vs prod)?

409 Conflict — state collision

1. Resource already exists (duplicate create)?
2. Stale ETag / If-Match?
3. Concurrent modification by another process?

422 Unprocessable Entity — valid JSON, invalid data

The error body usually names the bad fields. Check:

• Field types (string vs int, date format)
• Required vs optional
• Enum values inside the allowed set

429 Too Many Requests — rate limited

Check Retry-After and X-RateLimit-* headers. Exponential backoff:

execute_code('''
import time, requests

def with_backoff(method, url, **kwargs):
    for attempt in range(5):
        resp = requests.request(method, url, **kwargs)
        if resp.status_code != 429:
            return resp
        wait = int(resp.headers.get("Retry-After", 2 ** attempt))
        time.sleep(wait)
    return resp
''')

5xx — server-side, usually not your fault

• 500 — server bug. Capture correlation ID, file with provider.
• 502 — upstream down. Backoff + retry.
• 503 — overloaded / maintenance. Check status page.
• 504 — upstream timeout. Reduce payload or raise timeout.

For all 5xx: backoff with jitter, alert on persistence.

Pagination & Idempotency

Pagination. Verify you're getting all results. Look for next_cursor, next_page, total_count. Two patterns:

• Offset (?limit=100&offset=200) — simple, can skip items if data shifts.
• Cursor (?cursor=abc123) — preferred for live or large datasets.

Idempotency. For non-idempotent operations (POST), send Idempotency-Key: <uuid> so retries don't double-charge / double-create. Mandatory for payments and orders.

Contract Validation

Catch schema drift before it hits production:

execute_code('''
import requests

def validate_user(data: dict) -> list[str]:
    errors = []
    required = {"id": int, "email": str, "created_at": str}
    for field, expected in required.items():
        if field not in data:
            errors.append(f"missing field: {field}")
        elif not isinstance(data[field], expected):
            errors.append(f"{field}: want {expected.__name__}, got {type(data[field]).__name__}")
    return errors

resp = requests.get(f"{BASE}/users/1", headers=HEADERS, timeout=10)
issues = validate_user(resp.json())
if issues:
    print(f"contract violations: {issues}")
''')

Run after API upgrades, when integrating new third parties, or in CI smoke tests.

Correlation IDs

Always capture the provider's request ID — fastest path to vendor support:

execute_code('''
import requests
resp = requests.post(url, json=payload, headers=headers, timeout=10)
request_id = (
    resp.headers.get("X-Request-Id")
    or resp.headers.get("X-Trace-Id")
    or resp.headers.get("CF-Ray")  # Cloudflare
)
if resp.status_code >= 400:
    print(f"failed status={resp.status_code} req_id={request_id} ts={resp.headers.get('Date')}")
''')

Vendor bug-report template:

Endpoint:    POST /api/v1/orders
Request ID:  req_abc123xyz
Timestamp:   2026-03-17T14:30:00Z
Status:      500
Expected:    201 with order object
Actual:      500 {"error":"internal server error"}
Repro:       curl -X POST … (auth: <REDACTED>)

Regression Test Template

Drop this into tests/ and run via terminal('pytest tests/test_api_smoke.py -v'):

import os, requests, pytest

BASE_URL = os.environ.get("API_BASE_URL", "https://api.example.com")
TOKEN    = os.environ.get("API_TOKEN", "")
HEADERS  = {"Authorization": f"Bearer {TOKEN}"}

class TestAPISmoke:
    def test_health(self):
        resp = requests.get(f"{BASE_URL}/health", timeout=5)
        assert resp.status_code == 200

    def test_list_users_returns_array(self):
        resp = requests.get(f"{BASE_URL}/users", headers=HEADERS, timeout=10)
        assert resp.status_code == 200
        data = resp.json()
        assert isinstance(data.get("data", data), list)

    def test_get_user_required_fields(self):
        resp = requests.get(f"{BASE_URL}/users/1", headers=HEADERS, timeout=10)
        assert resp.status_code in (200, 404)
        if resp.status_code == 200:
            user = resp.json()
            assert "id" in user and "email" in user

     ():
        resp = requests.get(
            ,
            headers={: },
            timeout=,
        )
         resp.status_code == 

Security

Token handling

• Never log full tokens. Redact: Bearer <REDACTED>.
• Never hardcode tokens in scripts. Read from env (os.environ["API_TOKEN"]) or ~/.hermes/.env.
• Rotate immediately if a token surfaces in logs, error messages, or git history.

Safe logging

def redact_auth(headers: dict) -> dict:
    sensitive = {"authorization", "x-api-key", "cookie", "set-cookie"}
    return {k: ("<REDACTED>" if k.lower() in sensitive else v) for k, v in headers.items()}

Leak checklist

• Credentials in URLs. API keys in query strings end up in server logs, browser history, referrer headers — use headers.
• PII in error responses. 404 on /users/123 shouldn't reveal whether the user exists (enumeration).
• Stack traces in prod. 500s shouldn't leak file paths, framework versions.
• Internal hostnames/IPs. 10.x.x.x, internal-api.corp.local in error bodies.
• Tokens echoed back. Some APIs include the auth token in error details. Verify they don't.
• Verbose Server / X-Powered-By. Stack-info leaks. Note for security review.

Hermes Tool Patterns

terminal — for curl, dig, openssl

terminal('curl -sI https://api.example.com')
terminal('openssl s_client -connect api.example.com:443 -servername api.example.com </dev/null 2>/dev/null | openssl x509 -noout -dates')

execute_code — for multi-step Python flows

When debugging spans auth → fetch → paginate → validate, use execute_code. Variables persist for the script, results print to stdout, no risk of token spam in your context:

execute_code('''
import os, requests

token = os.environ["API_TOKEN"]
base  = "https://api.example.com"
H     = {"Authorization": f"Bearer {token}"}

# 1. auth
me = requests.get(f"{base}/me", headers=H, timeout=10)
print(f"auth {me.status_code}")

# 2. paginate
all_users, cursor = [], None
while True:
    params = {"cursor": cursor} if cursor else {}
    r = requests.get(f"{base}/users", headers=H, params=params, timeout=10)
    body = r.json()
    all_users.extend(body["data"])
    cursor = body.get("next_cursor")
    if not cursor:
        break
print(f"users={len(all_users)}")
''')

web_extract — for vendor API docs

Pull the spec for the endpoint you're debugging instead of guessing:

web_extract(urls=["https://docs.example.com/api/v1/users"])

delegate_task — for full CRUD test sweeps

delegate_task(
    goal="Test all CRUD endpoints for /api/v1/users",
    context="""
Follow the rest-graphql-debug skill (optional-skills/software-development/rest-graphql-debug).
Base URL: https://api.example.com
Auth: Bearer token from API_TOKEN env var.

For each verb (POST, GET, PATCH, DELETE):
  - happy path: assert status + response schema
  - error cases: 400, 404, 422
  - log a repro curl for any failure (redact tokens)

Output: pass/fail per endpoint + correlation IDs for failures.
""",
    toolsets=["terminal", "file"],
)

Output Format

When reporting findings:

## Finding
Endpoint: POST /api/v1/users
Status:   422 Unprocessable Entity
Req ID:   req_abc123xyz

## Repro
curl -X POST https://api.example.com/api/v1/users \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer <REDACTED>' \
  -d '{"name":"test"}'

## Root Cause
Missing required field `email`. Server validation rejects before processing.

## Fix
-d '{"name":"test","email":"[e-mail]"}'

Related

• systematic-debugging — once the failing API layer is isolated, root-cause your code
• test-driven-development — write the regression test before shipping the fix

Common Rationalizations

Rationalization	Reality
"I'll guess the fix based on the exception text."	Guessing leads to trial-and-error changes that bloat the git history and introduce side effects. Always isolate the network layer first.
"A 200 OK status means the API request succeeded."	Many APIs (especially GraphQL) return 200 OK but include structured exceptions or partial data. Always parse and validate payload bodies.
"We don't need a formal reproducer for simple bugs."	If you can't reliably reproduce a failure, you can't verify that your change was the active fix rather than transient environmental behavior.

Red Flags

• Guessing or modifying source code without replicating the bug locally or via curl first.
• Blindly ignoring SSL certificate warnings (-k or NODE_TLS_REJECT_UNAUTHORIZED=0) in production code.
• Swallowing internal exception messages inside catch blocks without mapping, logging, or reporting them.
• Sharing full API auth headers, Bearer tokens, or passwords inside plain text logs or vendor bug reports.

Verification

After completing the debugging process, verify:

• Network layer is successfully isolated and localized (Connectivity, TLS, Auth, Payload, or Application logic).
• A reproducible test scenario (curl command, script, or test case) predictably fails.
• The identified fix is applied and verified as passing using the same reproducer criteria.
• All sensitive credentials, tokens, or PII are redacted from diagnostics, curl statements, and debug scripts.