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 byerlikaya/claude-starter-kit --skill observabilitygit clone --depth 1 https://github.com/byerlikaya/claude-starter-kitWrote 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/byerlikaya/claude-starter-kit/observability)<a href="https://agentmods.dev/skills/byerlikaya/claude-starter-kit/observability"><img src="https://agentmods.dev/badge/skills/byerlikaya/claude-starter-kit/observability/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/byerlikaya/claude-starter-kit/observability"><img src="https://agentmods.dev/badge/skills/byerlikaya/claude-starter-kit/observability.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 | $0.00062 | $0.00877 |
| Opus 5 | $0.00031 | $0.00439 |
| Sonnet 5 | $0.00012 | $0.00175 |
| Haiku 4.5 | $0.00006 | $0.00088 |
Grade A, and why
observability 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability
Trigger phrases: "observability", "structured logging", "structured log", "add a trace", "add a metric", "correlation id", "add logging"
Goal: to be able to answer "what happened, where, why" during a production incident by looking at the logs. It is stack-agnostic; when you need a framework-specific library/format, do a web search.
Three signals
- Log — event record (structured/JSON, leveled).
- Metric — numeric time series (request count, latency, error rate, resource usage).
- Trace — a request's journey across services (spans + correlation id).
Checklist
- Logs are structured (JSON/key-value), not string interpolation
- Every log line carries a correlation id (request/trace id)
- Levels are correct: the DEBUG/INFO/WARN/ERROR distinction is meaningful
- No PII/secret is logged (password, token, card, national/ID number, email body)
- Error logs carry context (input summary, user/resource id — not PII); the stack trace does not leak to the user
- Critical business metric + infrastructure metric are emitted (where applicable)
- The correlation id is propagated across service-to-service calls (header/propagation)
How
- Structured logger — set up/use one whose output is machine-readable (JSON). Search for the library the framework recommends.
- Correlation id: generate it at the entry point (HTTP middleware / message consumer) or take it from the incoming
X-Request-Id/trace header; put it in the log context; propagate it to downstream calls. - Level discipline: INFO = business event, WARN = expected-but-noteworthy, ERROR = needs intervention. DEBUG is off/sampled in production.
- Context fields:
event,correlation_id,user_id(not PII, an opaque id),duration_ms,outcome. Do not embed them in free text. - Metrics: at minimum RED (Rate, Errors, Duration) or USE; plus business-critical counters. Search for the framework's metrics library.
- Trace (in a distributed system): start/end spans, bind the correlation id to the trace id.
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.
- 9d ago First seen · 53 lines · 62 tokens per session scan A fc4af1c0fc2a
observability is a skill published in the GitHub repository byerlikaya/claude-starter-kit (22 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 877 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
Other skills, from other repositories
manage-skills
A maintenance workflow for checking whether project verification skills still cover the code and rules that changed during a session.
review-loop
Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified"…
systematic-debugging
Structured debugging methodology — use before proposing fixes for any error or failure. Covers: code bugs, build errors, deploy failures, config conflicts, dependency issues, infra problems. Also use when previous fix attempts failed or root cause is unclear.
debug-systematic
Systematic 4-phase debugging methodology for complex, intermittent, or mysterious issues. Use when investigating bugs, race conditions, or unexplained failures.
debugging-strategies
Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.
diagnose
Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.