From qa-debug
This skill should be used when the user asks to "classify an error", "triage this stack trace", "archive this bug", "generate an error fingerprint", "identify the type of bug", "what kind of error is this", "is this a regression", or needs to parse, categorize, structure, or deduplicate information from error logs, stack traces, exception reports, or incident summaries. Also activate when running /triage or /archive-error commands.
How this skill is triggered — by the user, by Claude, or both
Slash command
/qa-debug:error-taxonomyThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
| Type | Primary Signal | Common Root Cause | Fastest First Diagnostic |
| Type | Primary Signal | Common Root Cause | Fastest First Diagnostic |
|---|---|---|---|
| Build | Compile error, missing module, import failure | Dependency version mismatch, broken import path, lock file drift | pip install / npm ci, check lock file diff |
| Runtime | NullPointerException, TypeError, AttributeError, KeyError | Unguarded nullable, wrong type assumption, missing key | Stack trace → first non-library frame |
| Performance | p99 spike, request timeout, slow query log entry | N+1 query, missing DB index, unbounded loop, memory leak | Query plan, profiler output, heap snapshot |
| Network | Connection refused, ECONNRESET, DNS resolution failure | Service down, firewall rule, certificate expiry, port mismatch | ping, curl -v, check service health endpoint |
| Flaky | Passes 9/10 CI runs, fails non-deterministically | Race condition, hardcoded timing, shared/polluted test state | Run 20× in complete CI isolation; add timing instrumentation |
| Config | 500 on deploy, works in local/staging | Missing env var in production, secret rotation gap, feature flag state | Diff env vars between local and production environments |
| Regression | Worked before this commit/PR, broke on deploy | Unintended side effect of refactor, dependency bump | git bisect on failure window; compare behavior before/after PR |
| Data | Corrupt state, partial write, unexpected null in DB | Missing transaction boundary, write race condition, migration gap | Inspect DB state at failure timestamp; check migration log |
A stable fingerprint enables deduplication — the same error recurring 50 times should produce one ticket, not fifty.
Normalization steps before hashing:
0x7f3a... → <addr>)"user 1234 not found" → "user <id> not found")error_hashRequired fingerprint fields for /archive-error:
| Field | How to Populate |
|---|---|
error_hash | SHA-256 of normalized stack trace (after steps above) |
component | service-name::module::function — first non-library frame |
first_seen | ISO 8601 timestamp of the earliest known occurrence |
last_seen | ISO 8601 timestamp of most recent occurrence |
frequency | Occurrence count in rolling 24h window |
status | new | known | regression | resolved |
| Severity | Condition | Expected Response |
|---|---|---|
| P0 | Production down, active data loss, security breach, >10% users affected | Immediate all-hands; escalate out-of-hours |
| P1 | Major feature broken, no workaround exists, SLO breach imminent | On-call response within 15 minutes |
| P2 | Degraded functionality, workaround exists, <5% users affected | Next-business-day response |
| P3 | Minor issue, cosmetic, or low-traffic code path | Backlog — prioritize in next sprint |
P0 vs P1 discriminator: Is there a workaround that unblocks the user? If yes, P1. If no, P0.
See references/taxonomy.md for regex patterns for common error formats across Python, Node.js, Java, and Go.
npx claudepluginhub luxcordia/qa-debug --plugin qa-debugClassifies errors into 7 taxonomy categories (syntax, logic, design, performance, etc.) and routes to deterministic resolution strategies. Use when root cause is unclear or bug fix attempts fail.
Parses error logs, stack traces, and crash reports into a structured root cause diagnosis with confidence level, affected code path, and concrete fix suggestions. Best for debugging exceptions, crashes, or unexpected errors.
Analyzes production errors in distributed systems, performs root-cause analysis on incidents, and recommends observability, logging, and tracing improvements.