monitor
Skill4 starsChanged 6 months agoApache-2.0View on GitHub
Use when the user asks to monitor, health-check, watch, or check status of services, pipelines, code, teams, builds, deployments, or CI. Also triggers for "is X healthy", "check on Y", "watch Z". Runs one-shot or recurring, parameterized by data source.
30 other projects use a version of this.
Adopted by
Most recently taken up by
- Mahiler1909/burnlogtheir own version, first seen Nov 8, 2026
- cihantopac/searvextheir own version, first seen Nov 8, 2026
- gspencergoog/skillstheir own version, first seen Nov 1, 2026
- neurospin-projects/2026_bdrabczuk_champollion_mcptheir own version, first seen Nov 1, 2026
- GuanceCloud/ai-skillstheir own version, first seen Oct 25, 2026
- osteel/agent-skillstheir own version, first seen Oct 13, 2026
Used with
Projects with it also use debug and research.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
AnthropicSkilldebug
Structured debugging session — reproduce, isolate, diagnose, and fix. Trigger with an error message or stack trace, "this works in staging but not prod", "something broke after the deploy", or when behavior diverges from expected and the cause isn't obvious.
Also in 3 projects that use this
NVIDIASkillresearch
Before designing a feature, research how comparable libraries solve the problem, check best practices, known pitfalls, and upstream changes. Use when starting a new feature, investigating an unfamiliar area, or the user asks to research something.
Also in 3 projects that use this
Other versions30 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 30
- NVIDIA/TensorRT-Model-Optimizer★ 5.6ksame name
- NVIDIA/Model-Optimizer★ 5.6ksame name
- H-mmer/pentest-agents★ 815same name
- elitan/frost★ 324same name
- k1p1l0/claude-telegram-supercharged★ 144same name
- block/builderbot★ 108same name
- no-session/pstack★ 96same name
- joelhooks/joelclaw★ 65same name
- rainday/smart-blog-skills★ 35same name
- freemty/labmate★ 25same name
20 more not listed
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 repository57 other files it gives its agents.
- autopilotSkill
- build-python-librarySkill
- build-temporalSkill
- canon-design-reviewSkill
- canon-pr-reviewSkill
- canon-reviewSkill
- ccr-modelsSkill
- ccr-runSkill
- chartSkill
- ci-health-reportSkill
- code-quality-trendsSkill
- competitive-matrixSkill
- councilSkill
- create-skillSkill
- deep-debug-ensemble-v1Skill
- deep-debugSkill
- deep-designSkill
- deep-ideaSkill
- deep-planSkill
- deep-qa-ensemble-v1Skill
- deep-qaSkill
- deep-researchSkill
- dependency-auditSkill
- deploy-status-reportSkill
- develop-board-gameSkill
- diagramSkill
- docxSkill
- dora-lite-reportSkill
- find-skillSkill
- flaky-test-diagnoserSkill
- gap-finderSkill
- gen-crash-testSkill
- gen-smokeSkill
- goal-managerSkill
- init-github-repoSkill
- loop-until-doneSkill
- magic-fetchSkill
- ml-pipeline-reportSkill
- napSkill
- pipeline-health-reportSkill
- platform-friction-detectorSkill
- pptxSkill
- prod-readinessSkill
- prompt-clash-ensembleSkill
- prompt-clashSkill
- retrospectSkill
- slack-digestSkill
- slidesSkill
- specSkill
- sprint-retroSkill
- tableSkill
- team-temporalSkill
- timelineSkill
- update-stop-gateSkill
- user-activity-reportSkill
- vc-gauntletSkill
- xlsxSkill
The file
We split this skill into the rules it gives an agent
5 rules. 0 are common, 2 are distinctive, and 3 are very specific to this project.
0
Common
Most of the 26 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 21
If --recurring: schedule next run
- Distinctive: line 90
When cross-provider tools are available, run verification on a non-Claude model in parallel for maximum blind-spot diversity.
- Very specific: line 115
If one-shot health check (not --recurring) → produce report directly in-session.
- Distinctive: line 117
If healthy → launch via sagaflow below.
- Very specific: line 118
If unhealthy → log SAGAFLOW_UNAVAILABLE, proceed with in-session fallback.
“Common” is measured: kept by at least 6 in 10 of the 26 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Monitor
Strategy
- Select preset (determines data source and key metrics)
- Gather health data from the source
- Compare against baseline/thresholds
- Rate: healthy / degraded / critical
- Format report
- Alert if critical
- If
--recurring: schedule next run
Exit: report delivered. For recurring: runs until cancelled.
Report requirements (every report MUST include)
- Specific metric values: exact numbers, not just "high" or "degraded"
- Trend direction: is each metric improving, stable, or worsening vs baseline?
- Actionable items: concrete next steps ranked by urgency, not just observations
- Anomaly callouts: flag anything outside expected range with the specific threshold breached
Note: Placeholders like
{user_question}in Agent prompts are filled by you (Claude) from the current task context. They are not template variables — read the user input, gather the relevant context, and substitute before spawning the agent.
Agents
GATHER phase
Agent(subagent_type="Explore", model="haiku", prompt="""
Gather health data for: {target}
Preset: {preset}
Data sources to check:
{preset_data_sources}
Key metrics to collect:
{preset_metrics}
Output: raw metrics with timestamps.
""")ASSESS + REPORT phase
Agent(model="sonnet", prompt="""
Health data:
{gathered_metrics}
Baseline (last {baseline_days} days):
{baseline_data}
1. Compare current vs baseline
2. Flag anomalies (>2 stddev from baseline)
3. Rate overall: HEALTHY / DEGRADED / CRITICAL
4. Format as a concise health report
""")Presets
| Preset | Sources | Metrics |
--service NAME | Observability platform, tracing | Latency p50/p99, error rate, throughput, instance count |
--pipeline NAME | Pipeline orchestrator | Success rate, SLA compliance, last failure |
--ci | CI/CD platform | Build success rate, flaky test %, avg build time |
--deploy APP | Deployment platform | Deploy state, canary score, pending constraints |
--ml FLOW | ML platform | Run status, latest metrics, accuracy trend |
--code REPO | Git history | TODO/FIXME count, test coverage, PR merge rate |
--deps REPO | pip/npm audit, CVE DBs | Outdated count, critical CVEs, last updated |
--docs | Documentation platform | Pages not updated in 90d, broken links |
--team NAME | GitHub, Slack, Jira | PR velocity, open issues, Slack activity |
--oncall | PagerDuty, incident log | Open incidents, MTTR, handoff notes |
Cross-provider review
When cross-provider tools are available, run verification on a non-Claude model in parallel for maximum blind-spot diversity.
Flags
--recurring=INTERVAL— repeat on schedule (daily, hourly, etc.)--alert=CHANNEL— notification channel for critical alerts--baseline=N— days of baseline data (default: 14)
Examples
/monitor --service my-api
/monitor --pipeline etl-daily --recurring=daily
/monitor --ci --alert=#builds
/monitor --team platform-eng
/monitor --code --baseline=30
/monitor --oncallExecution routing (sagaflow-first)
Sagaflow is the default execution path. The in-session workflow above is the FALLBACK, used only when the sagaflow worker is confirmed unavailable.
Routing sequence (mandatory before any in-session work):
0. If one-shot health check (not --recurring) → produce report directly in-session. Do NOT route to sagaflow.
- Run
sagaflow doctor - If healthy → launch via sagaflow below. Stop. Do not run in-session.
- If unhealthy → log
SAGAFLOW_UNAVAILABLE, proceed with in-session fallback.
Launch command:
Bash(
run_in_background=true,
command="sagaflow launch monitor --arg target='<TARGET>' --arg preset=auto --arg recurring=false --await"
)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.

