Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add lfyxhappy/lfcode --skill observability-debuggergit clone --depth 1 https://github.com/lfyxhappy/lfcodeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lfyxhappy/lfcode/observability-debugger)<a href="https://agentmods.dev/skills/lfyxhappy/lfcode/observability-debugger"><img src="https://agentmods.dev/badge/skills/lfyxhappy/lfcode/observability-debugger/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lfyxhappy/lfcode/observability-debugger"><img src="https://agentmods.dev/badge/skills/lfyxhappy/lfcode/observability-debugger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00061 | $0.00351 |
| Opus 5.5 | $0.00024 | $0.00140 |
| Sonnet 5.5 | $0.00012 | $0.00070 |
| Haiku 4.5 | $0.00006 | $0.00035 |
Grade A, and why
observability-debugger scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Observability Debugger
Use time-correlated evidence to narrow an operational problem safely.
Workflow
- Define the symptom, affected target, time window and timezone, request or session correlation identifier, impact, and authorization.
- Check the actual process, ports, health response, logs, metrics, traces, recent deploys, configuration changes, queue state, and dependency failures that are available locally.
- Build a timeline and compare a healthy interval or control path. Group failures by first cause, retries, saturation, and downstream symptoms.
- State confirmed evidence, competing hypotheses, confidence, and the next least-invasive diagnostic. Capture exact source locations and timestamps.
- If remediation is explicitly requested, make one controlled change at a time, preserve evidence, and verify recovery plus error-path behavior.
- Recheck alert conditions, user-visible behavior, and recurrence after the change. Keep incident notes concise and recoverable.
Boundaries
- Read-only diagnostics are the default. Do not restart, scale, purge queues, clear logs, disable alerts, or alter production configuration without authorization.
- Redact tokens, cookies, request bodies, personal data, and secrets before storing or reporting evidence.
- Do not infer root cause from correlated timing alone; identify the missing observation.
Completion check
Report the timeline, evidence, root cause or bounded hypothesis, remediation status, verification, and remaining monitoring need.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 28 lines · 61 tokens per session scan A a9db63c625b5
observability-debugger is a skill published in the GitHub repository lfyxhappy/lfcode (2 stars, last pushed 26d ago), licensed MIT. It adds 61 tokens to every session and 351 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
Debug (reproduce -> isolate -> fix)
Systematically diagnose and fix a bug instead of guessing.
feishu-troubleshoot
A Feishu troubleshooting tool with FAQs and diagnostic commands. Feishu is a collaboration platform with online documents and workplace services.
investigate
A structured root-cause investigation method for software bugs. It moves from history and reproduction through tested hypotheses, a focused fix, verification, and a report.
bugfix-triage
A method for locating the cause of a bug before changing code, using a minimal reproduction and step-by-step narrowing of the failing input and code path.
Review a GitHub PR (via gh)
Review a specific GitHub pull request with gh — fetch the diff, fan out reviewers, consolidate, and optionally post the review. Requires the gh CLI or the GitHub MCP server.
business-ops-runbook-generator
Build an operational runbook with rollback paths for a recurring task or incident: capture the brief, infer failure modes, draft the runbook steps and checks, then define a rollback path for each failure mode. Use when the user wants a step-by-step runbook that an operator can follow under pressure, including what to…