Scale 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 scale-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
Performance complexity and resource allocation canary — checks for O(N^2) loops, database N+1 query patterns, memory leaks (unbounded collections), and blocking calls in main event loop. Triggers on keywords: "/scale-canary", "scale-canary", "performance audit", "scale audit". Use when writing loops over growing data, DB queries, caches, or async/event-loop code.
SKILL.md
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Scale Canary (Performance & Resource Allocation Audit)
<!-- SHARED:LANGUAGE_HEADER -->Audit code for scalability issues, performance bottlenecks, and resource leaks.
Auditing Categories
- O(N^2) Complexity — nested loops over growable collections without indexing or caching (crashes at scale).
- N+1 Database Queries — querying records in a loop instead of a batch JOIN or bulk prefetch.
- Memory Bloat / Leaks — appending to global arrays/maps without clearing them → unbounded growth.
- Blocking Main Loop — synchronous FS ops or CPU-heavy work on the main event thread (lag/hangs).
- Resource Leakage — streams, connections, or handles left open without a
finallyclose.
Per-ORM N+1 shapes, per-stack blocking patterns, and what NOT to flag: read references/checks.md before scanning.
Fix mode (choice-gated)
In Agent Context, after the report, present via ask_question:
- Apply safe optimizations: async-ify synchronous file ops; insert
finallyblocks for stream closing. 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 specific optimizations.
- Report only: exit unchanged.
Output
| file:line | bottleneck | severity | finding | optimization plan |
Severity: CRITICAL (O(N^2) on user-facing API / unclosed file handles) · HIGH (N+1 query pattern / blocking main loop) · MEDIUM (unbounded cache growth) · LOW (minor efficiency suggestions)
<!-- SHARED:ORCHESTRATION --> <!-- SHARED:ESCALATION_FOOTER -->