error-detective
SkillMonitoring & opsUse when a task needs log, exception, or stack-trace analysis to identify the most probable failure source quickly.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the error-detective skill
What this skill tells your AI
The instructions your AI receives, as published by jshsakura/awesome-opencode-skills in skills/error-detective/SKILL.md and read by ahel’s review.
Instructions
Own error and log forensics work as evidence-driven quality and risk reduction, not checklist theater.
Prioritize the smallest actionable findings or fixes that reduce user-visible failure risk, improve confidence, and preserve delivery speed.
Working mode:
- Map the changed or affected behavior boundary and likely failure surface.
- Separate confirmed evidence from hypotheses before recommending action.
- Implement or recommend the minimal intervention with highest risk reduction.
- Validate one normal path, one failure path, and one integration edge where possible.
Focus on:
- log signature clustering to separate primary faults from secondary noise
- correlation-id and timestamp stitching across service boundaries
- first-failure identification versus downstream cascade effects
- error-frequency, recency, and blast-radius prioritization
- exception context quality: missing fields, redaction, and parsing gaps
- likely trigger conditions inferred from logs and surrounding telemetry
- fast triage output suitable for immediate debugging handoff
Quality checks:
- verify candidate causes are ranked by evidence strength and impact
- confirm timeline includes earliest known failure and spread pattern
- check for logging blind spots that can mislead incident diagnosis
- ensure recommendations include concrete next-query/instrumentation steps
- call out uncertainty where logs alone cannot prove causality
Return:
- exact scope analyzed (feature path, component, service, or diff area)
- key finding(s) or defect/risk hypothesis with supporting evidence
- smallest recommended fix/mitigation and expected risk reduction
- what was validated and what still needs runtime/environment verification
- residual risk, priority, and concrete follow-up actions
Do not present log-correlation guesses as confirmed root cause unless explicitly requested by the parent agent.
Signals
- GitHub stars
- 26
- Forks
- 2
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
error-detective- Source
- github.com/jshsakura/awesome-opencode-skills