aio-observe
Skill0 starsChanged 32 days agoMITView on GitHub
Establish backend and agent telemetry, operational checks and actionable recovery signals.
In this repository54 other files it gives its agents.
- agent-workflowsSkill
- aio-agentsSkill
- aio-algorithmsSkill
- aio-apiSkill
- aio-architectureSkill
- aio-autoplanSkill
- aio-benchmarkSkill
- aio-brainstormSkill
- aio-canarySkill
- aio-checkpointSkill
- aio-ciSkill
- aio-commitSkill
- aio-compoundSkill
- aio-dataSkill
- aio-debugSkill
- aio-devex-reviewSkill
- aio-doc-reviewSkill
- aio-explainSkill
- aio-feedbackSkill
- aio-handoffSkill
- aio-ideateSkill
- aio-optimizeSkill
- aio-pipelinesSkill
- aio-plan-reviewSkill
- aio-planSkill
- aio-proofSkill
- aio-prototypeSkill
- aio-qa-onlySkill
- aio-qaSkill
- aio-releaseSkill
- aio-resumeSkill
- aio-reviewSkill
- aio-securitySkill
- aio-shipSkill
- aio-simplifySkill
- aio-specSkill
- aio-tddSkill
- aio-test-auditSkill
- aio-testSkill
- aio-verifySkill
- aio-workSkill
- aio-worktreeSkill
- all-in-oneSkill
- apple-appstore-reviewerSkill
- backend-algorithmsSkill
- backend-debugSkill
- backend-pipelinesSkill
- backend-planSkill
- backend-reviewSkill
- backend-verifySkill
- complete-destructionSkill
- launch-readinessSkill
- play-policy-insightsSkill
- reverse-engineer-anythingSkill
The file
Files
AIO Observe
Establish backend and agent telemetry, operational checks and actionable recovery signals.
Protocol
- Map user/API/job outcomes to service targets and failure boundaries. Inspect existing instrumentation and pick signals that answer operational questions.
- Correlate request/tenant/job/run/operation IDs without logging unrestricted prompts, secrets or personal data. Measure queue age, attempts, terminal failures, model/tool budgets and external effects where relevant.
- Define metrics/traces/logs and alerts with thresholds, owner and recovery action. Avoid high-cardinality labels or instrumentation that creates unbounded storage/cost.
- Exercise a controlled failure and verify a signal appears, identifies the affected boundary and leads to a working runbook. Validate normal traffic and noise/sensitive-data behavior.
- For requested ongoing monitoring use the host automation/scheduler capability with explicit user scope. A one-time observation must not claim future continuous monitoring.
Return and handoff
An instrumentation/runbook change or observed health report with actual signals and operational limits.
In a pipeline, use the stage contract and return to the integrating controller. Run only this specialist's owned scope; preserve the user's accepted decisions and existing authorization.
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.

