Telemetry canary
Nine quality-canary skills for AI coding agents - code health, world-class rule completeness, grounding, supply chain, resilience, drift & more - plus the auto-cadence hooks that run them unprompted at session start and session end. Cross-agent, consent-gated, token-lean.
npx -y skills add HetCreep/CoalMine --skill telemetry-canaryAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 11 stars11 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Observability and structured logging canary — checks for structured logs (JSON), OpenTelemetry metrics/traces, proper error stack traces, and flags empty catches or silent log swallowing. Triggers on keywords: "/telemetry-canary", "telemetry-canary", "observability audit", "structured logging". Use when adding or changing logging, metrics, tracing, or error-handling code.
SKILL.md
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Telemetry Canary (Observability & Logging Audit)
Language: Generate EVERYTHING at runtime in the user's language — questions, answer options, menu labels, recommendations, report narrative. Detect from their messages; never default to English just because this file is English. English is allowed only for technical terms: commands, paths, code identifiers, severity labels (CRITICAL/HIGH/MEDIUM/LOW), and tier names (Light/Standard/Heavy).
Audit code for proper telemetry instrumentation — ensure the app is not a black box in production.
Auditing Categories
- Empty / Silent Catch — catch blocks that swallow exceptions without logging a stack trace or forwarding the error.
- Unstructured Logs — plain-string logging in server code (prefer JSON / structured key-value for cloud queries).
- No Correlation ID — operations crossing boundaries (HTTP/gRPC/threads) without propagating a trace/correlation ID.
- Missing Metrics — critical transactions (checkout, auth, errors) lacking counter/histogram instrumentation.
- No Stack Traces — errors logged without stack context (
logger.error(e.message)instead oflogger.error(e)).
Per-stack grep patterns and right/wrong shapes per category: read references/checks.md before scanning.
Fix mode (choice-gated)
In Agent Context, after the report, present via ask_question:
- Apply safe logs: insert error logging into empty catch blocks (standard logger template) + stack-trace mapping. Each fix: checkpoint (git stash/commit in a git repo; else copy the file aside — never assume git) → apply → build + tests → auto-revert if newly red.
- Let me pick: user selects which telemetry gaps to resolve.
- Report only: exit unchanged.
Output
| file:line | category | severity | finding | recommendation |
Severity: CRITICAL (swallowed error with state mutation) · HIGH (missing stack trace in error logs) · MEDIUM (unstructured log in API boundary) · LOW (minor trace gaps)
Escalation — Scope & Model Quality
Tiers are capability targets, not platform commands — resolve each to your host's nearest lever. No lever for one? Degrade gracefully — never fake parallelism you can't do; escalate via model tier + reasoning depth instead.
| Level | Intent | Capability target | Cost |
|---|---|---|---|
| Light | Spot telemetry check, key paths only | Cheapest model · single agent, no sub-agents. | Low |
| Standard | Balanced observability audit, multi-category | Balanced model · raised reasoning · sub-agents per category only if your platform runs concurrent workers (else single-agent). | Balanced |
| Heavy | Full 5-category audit + adversarial verify | Most capable model + largest context · deepest reasoning · max sub-agent fan-out if supported · adversarial cross-check where available. | High |
Per-platform Heavy levers + Heavy-run durability: read references/escalation.md before a Heavy run. No concurrent fan-out on your host → escalate by model + reasoning only.
Agent Context (interactive): score the tier rubric, then call ask_question once with the 3 tiers — the pick marked ✓, score shown, labels localized — and wait for the choice before starting. ask_question = your platform's question tool: Claude Code AskUserQuestion · Cline ask_question · Copilot askQuestions · Gemini CLI ask_user (business-tier product; individual tiers ended 2026-06-18 → Antigravity CLI) · Codex request_user_input · Cursor/Devin Desktop (ex-Windsurf)/Antigravity built-in prompts; none → numbered text menu.
Tier rubric (deterministic): +1 each — ① >20 files or whole-repo/cross-module reach ② >2 of this skill's categories relevant ③ release/security/pre-ship context ④ findings will drive code changes. 0–1 Light · 2–3 Standard · 4 Heavy. Freshness cap: scope already audited ≥Standard this session → cap at Light (re-auditing fresh ground wastes tokens; scope to what changed). Default tier: honor .coalmine.json defaultTier unless the user requests a tier for that run — an explicit request overrides everything.
Hook Context (auto-triggered): auto-Light, no tier question, no sub-agents — report first. Interactive session (a user is present) → offer the fix menu after the report; non-interactive → report-only. Never fix without a chosen option.
Entanglement: after the report, if confirmed findings fall in another canary's domain, offer it once via ask_question (one line, max one offer): perf/N+1 → scale-canary · contract/serialization/config → drift-canary · failure-path/retry → resilience-audit · logging/metrics → telemetry-canary · coupling/DI → testability-canary · dependency/CVE → supply-chain-audit · unverified version-sensitive claim → source-grounding · missing/stale rule → gold-standard.
Self error-report: if this skill misbehaves (contradictory instruction, broken procedure, wrong finding class), OFFER to file it at https://github.com/HetCreep/CoalMine/issues/new/choose with a user-reviewed summary — never auto-submit, never include unapproved code or paths.