routine-health-check
Skill49 starsChanged 3 months agoMITView on GitHub
Daily meta-routine — watches every other routine for error rates, drift, and runaway cost; surfaces routine_health findings into the Inbox. Routine-only.
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 60
Don't emit anything. The Inbox already shows zero new items as "all clear".
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 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
Routine Health Check
Purpose
Routines run unattended. When one of them starts erroring or running away on cost, the user needs to know — but only via the same channels they already check (the Inbox), not a separate alerting layer.
This skill turns "watch the other routines" into a routine itself. It loads into a daily fire of AssistantAgent and produces findings.
What you check
For each routine the user owns:
- Error rate — count of
routine_runs.outcome IN ('error', 'budget_exceeded')in the last 24h vs total runs in 24h. > 30% = high importance. - Drift — has the agent stopped emitting findings (no inbox_add calls for N consecutive runs)? That suggests the routine is silently doing nothing.
- Cost spike — sum
routine_runs.cost_usdover 24h vs the routine's daily cap. ≥ 80% = high importance. - Stuck runs — outcome='started' for > 1h means the run never finished cleanly.
What you produce
For each issue, call inbox_add with:
{
"kind": "routine_health",
"summary": "<routine name> — <one-line issue + action>",
"importance": "<calibrate via score-importance skill>",
"reasoning": "<the numbers backing the claim>",
"suggestedAction": {
"label": "Open routine",
"link": "/dashboard/routines/<id>"
},
"tags": ["routine-health"],
"relatedItemIds": ["<routineId>"]
}Scope rules
- Don't emit findings for routines the user has explicitly disabled — they already chose to stop them.
- Don't emit a finding for routine-health itself (that's recursive — and you'd cause exactly the noise you're checking for).
- Don't emit duplicate findings — if you've reported this routine in the last 24h, skip unless the metric got materially worse.
Tone
Findings are read while the user is doing other things. Lead with the routine name + the actionable bit, never with technical detail. Detail goes in reasoning.
Examples:
- ✓
Stuck-tickets sweeper — 4 of 6 fires errored (Gmail token expired). Reconnect Gmail. - ✗
routine_runs WHERE outcome='error' returned 4 rows for routine_id=abc...
When nothing's wrong
Don't emit anything. The Inbox already shows zero new items as "all clear". A "all routines healthy" finding once a day is what desensitises users to the channel.
In this repository32 other files it gives its agents.
- AGENTS.mdAGENTS.md
- docs/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- agent-asks-tasksSkill
- caretakerSkill
- code-reviewSkill
- compare-optionsSkill
- create-reportSkill
- csv-analyseSkill
- document-qaSkill
- draft-emailSkill
- enrich-errorSkill
- extract-structured-dataSkill
- fact-checkSkill
- git-diff-summariserSkill
- librarian-curateSkill
- meeting-notesSkill
- morning-briefSkill
- plan-taskSkill
- reflectSkill
- remember-conversationSkill
- reverieSkill
- review-code-securitySkill
- review-email-toneSkill
- review-outputSkill
- review-summary-faithfulnessSkill
- rewrite-for-audienceSkill
- route-findingSkill
- save-research-docSkill
- score-importanceSkill
- summarise-urlSkill
- web-researchSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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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.

