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From api-expert
Systematically diagnose API bugs — latency spikes, 5xx errors, timeouts, intermittent failures, connection pool exhaustion, N+1 queries, cascading failures, retry storms, rate limit false positives, auth drift, CORS issues, webhook delivery failures, and cross-service correctness bugs. Triggers on "my api is slow", "intermittent 500s", "timeout cascade", "n+1 queries", "api returning wrong data", "webhook not firing", "cors error", "429 errors when there shouldn't be", "my service keeps crashing", "debug my api". Uses systematic debugging (reproduce → isolate → hypothesize → test → confirm → fix → verify) — never guess.
How this skill is triggered — by the user, by Claude, or both
Slash command
/api-expert:debug-apiThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Dispatches the api-expert agent with a systematic debugging briefing. No guessing — evidence drives every step.
Dispatches the api-expert agent with a systematic debugging briefing. No guessing — evidence drives every step.
Before dispatching, extract or ask:
| Question | Why |
|---|---|
| Exact error message or status code | Filter by severity/type |
| Reproduction steps | Isolate trigger |
| Frequency (every request / 1% / random spikes) | Narrow hypothesis space |
| When it started (deploy correlation?) | Link to recent changes |
| Affected endpoints (one? many?) | Scope of issue |
| Logs / traces available? | Primary evidence |
| Environment (prod / staging / local) | Infra constraints |
Ask only the 1-2 critical questions you can't infer. If user says "just go", proceed with what's given.
Agent({
description: "API debug: <symptom>",
subagent_type: "api-expert:api-expert",
model: "opus",
prompt: "<see briefing below>"
})
ORIGINAL USER REQUEST: <verbatim>
WORKFLOW: debug
SYMPTOM: <exact error/behavior>
REPRODUCTION: <steps>
FREQUENCY: <every / N% / random>
ONSET: <when started / deploy correlation>
AFFECTED ENDPOINTS: <list>
LOGS/TRACES: <paths or excerpts if provided>
ENVIRONMENT: <prod/staging/local>
Read ${CLAUDE_PLUGIN_ROOT}/references/performance-caching.md and observability.md, plus any other relevant reference files.
FOLLOW SYSTEMATIC DEBUGGING METHOD:
Step 1 — Reproduce
Step 2 — Isolate (grep recent commits, check if request-scoped or global)
Step 3 — Hypothesize (3-5 specific hypotheses ranked by likelihood x testability)
Step 4 — Test hypothesis (smallest test that confirms or refutes)
Step 5 — Confirm root cause (evidence-backed, do NOT fix until confirmed)
Step 6 — Fix (minimal change, preserve behavioral contracts, grep callers)
Step 7 — Verify (reproduce original symptom — should no longer occur, run full test suite)
DELIVERABLES:
1. Reproduction (successful or note that it's prod-only)
2. Evidence collected (logs, traces, query plans, stack traces)
3. Hypothesis list with likelihood ranking
4. Test results per hypothesis
5. Confirmed root cause (with [P]/[V] grade)
6. Fix (runnable code change)
7. Verification steps the user runs
PROCEED.
Present the root cause + fix + verification steps. Offer to apply the fix immediately or stage it for review.
Provides CDSS development patterns for drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), and alert classification integrated into EMR workflows.
npx claudepluginhub themizeguy/api-expert-public