Diagnose why a GAIA question failed — extract trace, classify failure mode, and propose a fix. Use when a GAIA benchmark run reports a failed/incorrect task_id and you need to root-cause it before resubmitting.
Install
npx skills add https://github.com/ruvnet/ruflo --skill gaia-debuggingSKILL.md
GAIA Debugging Skill
When a GAIA question fails, systematically diagnose the root cause and propose a targeted fix.
When to use
- A specific
task_idreturns the wrong answer or times out - Pass-rate dropped between two runs and you need to find the regression
- You want to understand why a particular question class is consistently failing
Failure mode taxonomy
| Code | Mode | Symptom | Fix direction |
|---|---|---|---|
| TG | Tool Gap | Agent lacks a required tool (no image OCR, no PDF reader) | Add tool to catalogue |
| RM | Reasoning Miss | Agent has the right data but draws wrong conclusion | Improve system prompt, add CoT instruction |
| EB | Extraction Bug | Answer is in the trace but FINAL_ANSWER: regex fails |
Fix answer extraction pattern |
| LI | Loop Issue | Agent loops (re-asks same tool call) and hits turn limit | Increase max-turns or add loop-detection |
| DS | Dataset Shift | Ground truth differs from what web currently shows | Flag for HAL dataset audit |
| AT | API Timeout | Tool call times out; agent never gets the result | Increase per-turn timeout |
Diagnostic workflow
Step 1 — Load the question trace
# Find the result for the task_id in the latest run
RESULTS=~/.cache/ruflo/gaia/results-latest.json
node -e "
const r = JSON.parse(require('fs').readFileSync('$RESULTS'));
const q = r.results.find(x => x.task_id === '$TASK_ID');
console.log(JSON.stringify(q, null, 2));
"
Step 2 — Classify the failure
Look at the trace output:
- No tools called at all → RM or configuration issue
- Tool called but returned error → TG or AT
- Tool returned data, wrong answer → RM or EB
- Correct answer in trace but marked wrong → EB
- max-turns hit → LI or question too hard for current model
Step 3 — Re-run with extended logging
node v3/@claude-flow/cli/bin/cli.js gaia-bench run \
--level 1 --limit 1 \
--task-id $TASK_ID \
--models claude-sonnet-4-6 \
--max-turns 20 \
--output json
Step 4 — Apply targeted fix
| Failure | Action |
|---|---|
| TG — missing web_browse | Verify gaia-tools/index.ts exports web_browse; check tool registration |
| TG — missing image OCR | Add image_describe tool call; verify GOOGLE_AI_API_KEY |
| RM — reasoning | Add a system prompt instruction: "Before answering, list all facts you have gathered" |
| EB — extraction | Test the FINAL_ANSWER_RE regex against the trace manually |
| LI — loop | Add a tool-call deduplication guard in gaia-agent.ts |
| AT — timeout | Set DEFAULT_PER_TURN_TIMEOUT_MS higher or use --max-turns flag |
Step 5 — Verify fix and store pattern
# Re-run the single question
node … gaia-bench run --task-id $TASK_ID --models $MODEL --output json
# If now passing, store the pattern
npx @claude-flow/cli@latest memory store \
--namespace gaia-debug-patterns \
--key "fix-$FAILURE_CODE-$(date +%Y%m%d)" \
--value "task_id=$TASK_ID, mode=$FAILURE_CODE, fix=$FIX_DESCRIPTION"
Quick reference: tool catalogue check
node -e "
const { createDefaultToolCatalogue } = require('./v3/@claude-flow/cli/src/benchmarks/gaia-tools/index.js');
const cat = createDefaultToolCatalogue({});
console.log('Tools registered:', cat.definitions.map(t => t.name));
"
Expected: web_search, file_read, web_browse, image_describe, python_exec
Pattern storage
After resolving a debugging session, store the finding:
npx @claude-flow/cli@latest memory store \
--namespace gaia-debug-patterns \
--key "session-$(date +%Y%m%d-%H%M)" \
--value '{"task_id":"$TASK_ID","failure_mode":"$CODE","fix":"$FIX","verified":true}'
Search for similar past failures:
npx @claude-flow/cli@latest memory search \
--namespace gaia-debug-patterns \
--query "extraction bug final answer regex"
Related skills
find-skillsvercel-labs3.6MHelps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.handoffmattpocock883KCompact the current conversation into a handoff document for another agent to pick up.microsoft-foundrymicrosoft618KBuild, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agcavemanjuliusbrussee544KUltra-compressed communication mode that cuts output tokens while keeping technical accuracy. Levels: lite, full, ultra and the wenyan variants. Use for /caveman, "caveman mode", "talk like caveman", "be brief" or "less tokens".