steel-dev/skills

steel-session-debugging

Use this skill when the user provides a failed Steel session ID, failed automation run, timeout, browser error, blocked page, or unexpected result and wants evidence-backed diagnosis from session metadata, browser logs, raw agent logs, semantic traces, repl…

Voir la source

Mode d’emploi

  1. Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
  2. Consultez le document original pour les outils, identifiants et environnements requis.
  3. Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/steel-dev/skills --skill steel-session-debugging

Avant de commencer

  • L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
  • Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original

Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.

Steel Session Debugging

Skill Boundary

Use this skill to explain what happened in a failed Steel session and what to change next. Start from evidence, not guesses.

Do not use this skill as the primary place for bot-detection mitigation, proxy strategy, CAPTCHA strategy, login reputation, or retry policy. If the evidence points there, collect enough proof and hand off to steel-reliability.

Setup Check

  1. Confirm the user provided a Steel session ID or enough context to identify one.
  2. Run steel doctor --preflight if CLI auth or API access is uncertain.
  3. Prefer Steel CLI diagnostics before raw API calls.
  4. Create local artifacts under .steel-debug/; do not paste raw logs into chat unless the user asks.

Investigation Workflow

  1. Collect session metadata, browser logs, raw agent logs, semantic agent traces, and viewer/replay links.
  2. Redact cookies, bearer tokens, API keys, passwords, form values, and obvious PII before summarizing.
  3. Build a timeline: session creation, navigations, actions, waits, console errors, failed requests, CAPTCHA/proxy signals, and final state.
  4. Classify the failure using the taxonomy in references/failure-taxonomy.md.
  5. Produce an evidence-backed diagnosis with a smallest useful verification step.
  6. If the fix involves bot detection, proxy, CAPTCHA, identity, pacing, or login persistence, route to steel-reliability.

Output Format

text
Diagnosis:
- What happened:
- Most likely cause:
- Evidence:
- What to change:
- Verification step:
- Confidence:

Useful Commands

bash
steel --json sessions get <session-id>
steel --json sessions logs <session-id>
steel --json sessions agent-logs <session-id>
steel --json sessions traces <session-id>
node scripts/collect-session-debug.mjs <session-id>
node scripts/redact-session-debug.mjs .steel-debug/<session-id>/bundle.json
node scripts/summarize-session-debug.mjs .steel-debug/<session-id>/redacted-bundle.json

References

  • references/investigation-workflow.md: detailed collection and timeline process.
  • references/log-event-types.md: how to separate metadata, browser logs, raw agent logs, and traces.
  • references/agent-traces.md: how to read semantic agent trace events.
  • references/network-forensics.md: request failures, redirects, and blocked-page evidence.
  • references/replay-and-screenshots.md: viewer, player, HLS, and screenshot usage.
  • references/failure-taxonomy.md: standard failure classes.
  • references/reliability-handoff.md: when to load steel-reliability.

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