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SKILL verified MIT Self-run

Agent Observability

skill-arnie016-codex-prompt-templates-agent-observability · by Arnie016

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Install

$ agentstack add skill-arnie016-codex-prompt-templates-agent-observability

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Agent Observability

Use lightweight local observability before adopting a full platform.

Minimal Run Ledger

Create:

.agent-runs/
  runs.jsonl
  evals.md
  costs.md

Each runs.jsonl entry should include:

{"ts":"","task":"","model":"","inputs":"","commands":[],"result":"","tokens_est":"","cost_est":"","status":""}

What To Track

  • task type and outcome
  • commands run
  • tests passed/failed
  • estimated tokens and cost
  • repeated failure causes
  • user corrections
  • guardrail or safety decisions

Eval Loop

  1. Define 3-10 realistic tasks.
  2. Run the same task across workflow variants.
  3. Score correctness, time, tokens, and human intervention.
  4. Keep the cheaper path only if quality is comparable.

Skip Or Escalate

When observability needs gateway policy, staged guardrails, shadow traffic, trace-to-regression feedback, or model budgets, switch to $auto-skill-build-agent-reliability-loop. Keep this skill for local ledgers and lightweight eval/cost tracking.

Use full platforms like Future AGI when you need hosted traces, simulations, gateway routing, or production guardrails. Use local ledgers for personal workflows.

For appending or summarizing .agent-runs/runs.jsonl, use $auto-skill-build-agent-run-ledger-dashboard.

For Codex Supercharge artifact grades and subagent/automation handoff status, use $auto-skill-build-supercharge-feedback-loop; it writes a separate local feedback stream under plugins/codex-supercharge/.agent-runs/.

Validation

  • Each tracked run has a task, status, commands/tests, and outcome.
  • Cost/token notes are marked as estimates unless measured.
  • Repeated failures are summarized into a follow-up or eval case.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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