agent-triage
Skill0 starsChanged 7 months agoMITView on GitHub
Classify failed tasks from an autonomous coding agent, recommend disposition (retry / dead-letter / fix-and-retry / wontfix), and offer bulk action. Replaces reading hundreds of failure logs by hand.
- Sends data out
2 other projects use a version of this.
Adopted by
Most recently taken up by
- 2lab-ai/llmuxtheir own version, first seen Oct 9, 2026
- HermeticOrmus/ormus-agent-opsword for word, first seen Apr 25, 2026
Other versions2 projects use a version of this.
Word for word · 1
- HermeticOrmus/ormus-agent-ops★ 0100% same
Edited · 0
None found.
Wrote their own version · 1
- 2lab-ai/llmux★ 9same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository6 other files it gives its agents.
- agent-cleanSkill
- agent-evolveSkill
- agent-inspectSkill
- agent-opsSkill
- agent-statusSkill
- agent-sweep-orphansSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 2 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 84
After presenting, offer bulk operations.
- Very specific: line 108
When in doubt, classify as unknown and recommend /agent-inspect.
- Very specific: line 112
Default window is 24h. For longer windows (/agent-triage 7d), sweep is heavier — paginate accordingly.
- Distinctive: line 113
Don't auto-classify based on task slug or project name — read the actual error.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
/agent-triage — Smart Failure Triage
Failed tasks fall into a small number of buckets. Classify, then act.
Prerequisites
| Var | Purpose |
AGENT_API_URL | Base URL of the agent's HTTP API |
AGENT_API_TOKEN | Bearer token |
Endpoints used:
GET /api/tasks?status=failed&since=<window>— list failed tasksGET /api/tasks/:id/log— full log for one taskPOST /api/tasks/:id/requeue— requeuePOST /api/tasks/:id/dead-letter— dead-letter
Workflow
Phase 1: Pull failures
curl -s -H "Authorization: Bearer $AGENT_API_TOKEN" \
"$AGENT_API_URL/api/tasks?status=failed&since=24h"Phase 2: Classify each failure
For each failed task, fetch its log/last error and assign one classification:
| Class | Pattern | Recommended action |
infra | API rate limit, credit exhausted, network timeout, 5xx from upstream | requeue — not the task's fault |
auth | 401/403 from upstream, expired token, missing credential | fix-and-retry — operator must rotate or refresh |
code | Compile error, test failure, lint failure that the agent could fix | requeue with prompt amendment, or dead-letter if 3+ retries already |
prompt | Agent looped, hit max iterations without converging, hallucinated nonexistent file | fix-and-retry — operator amends the prompt |
env | Missing dep, wrong Node version, path doesn't exist on worker | fix-and-retry — operator fixes the env |
wontfix | Task is no longer needed, superseded by another, or scope was wrong | dead-letter |
unknown | Doesn't match any pattern above | inspect — drill in with /agent-inspect <task-id> |
Phase 3: Present grouped summary
TRIAGE — <N failures> in last 24h
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
INFRA <count> → bulk requeue
• <task-id> <slug> — <one-line cause>
• <task-id> <slug> — <one-line cause>
AUTH <count> → fix credentials, then requeue
• <task-id> <slug> — <missing/expired creds>
CODE <count> → requeue with amendment OR dead-letter
• <task-id> <slug> — <error type> — <attempt N/3>
PROMPT <count> → operator amends prompt
• <task-id> <slug> — <pattern: looped / hallucinated / max-iter>
ENV <count> → fix worker env, then requeue
• <task-id> <slug> — <missing dep / wrong version>
WONTFIX <count> → dead-letter
• <task-id> <slug> — <reason>
UNKNOWN <count> → /agent-inspect each
• <task-id> <slug>
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
PROPOSED ACTIONS
Bulk requeue: <N> tasks (infra)
Bulk dead-letter: <N> tasks (wontfix)
Operator action: <N> tasks (auth/prompt/env)
Inspect deeper: <N> tasks (unknown)Phase 4: Bulk action
After presenting, offer bulk operations. Do NOT execute without explicit user approval.
# Bulk requeue infra failures (after user confirms)
for tid in $INFRA_TASK_IDS; do
curl -s -X POST -H "Authorization: Bearer $AGENT_API_TOKEN" \
"$AGENT_API_URL/api/tasks/$tid/requeue"
doneHeuristics for log → classification
Common patterns to grep in logs:
| Pattern | Class |
rate.limit|429|credit.balance.exhausted|insufficient.credits | infra |
401|403|invalid.api.key|expired.token|unauthor | auth |
Module not found|Cannot find package|node_modules | env |
iteration.limit|max.iterations|loop.detected|too many turns | prompt |
ENOENT|file.not.found|path.does.not.exist | env (probably) |
tsc.*error|test.*failed|lint | code |
superseded|cancelled.by.user|scope.changed | wontfix |
When in doubt, classify as unknown and recommend /agent-inspect.
Notes
- Default window is 24h. For longer windows (
/agent-triage 7d), sweep is heavier — paginate accordingly. - Don't auto-classify based on task slug or project name — read the actual error.
- A single task may match multiple patterns. Pick the most specific (auth beats code, env beats prompt).
- Never bulk-act without showing the user the count + class breakdown first. A misclassification that bulk-requeues 50 prompt failures will create 50 more failures.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

