monitor-evals
Skill304 starsChanged 2 months agoMITView on GitHub
Review evaluation history, distinguish system, data, suite, and evaluator changes, and apply an operating response. Use for regressions, eval monitoring, release gates, or trend questions.
In this repository65 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- always-compareSkill
- analysis-designSkill
- analyst-coreSkill
- archaeologySkill
- archive-analysisSkill
- auth-preflightSkill
- businessSkill
- causalSkill
- chart-to-driveSkill
- close-the-loopSkill
- codex-reviewSkill
- compare-datasetsSkill
- connect-dataSkill
- connect-snowflakeSkill
- context-compareSkill
- context-traceSkill
- data-inspectSkill
- data-mapSkill
- data-profilingSkill
- data-quality-checkSkill
- datasetsSkill
- distribution-profilerSkill
- evalSkill
- evaluate-graderSkill
- experiment-briefSkill
- experimentSkill
- exploreSkill
- exportSkill
- forecastSkill
- google-doc-exportSkill
- google-slides-exportSkill
- guardrailsSkill
- historySkill
- improve-contextSkill
- knowledge-bootstrapSkill
- log-correctionSkill
- maintain-contextSkill
- metric-specSkill
- metricsSkill
- notion-exportSkill
- paceSkill
- patternsSkill
- question-framingSkill
- question-routerSkill
- reliabilitySkill
- resume-pipelineSkill
- run-pipelineSkill
- runsSkill
- score-analysisSkill
- session-handoffSkill
- setup-dev-contextSkill
- setup-notionSkill
- setupSkill
- setup-slackSkill
- setup-snowflakeSkill
- srm-checkSkill
- stakeholder-communicationSkill
- stress-testSkill
- switch-datasetSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 8
Read versioned run manifests with helpers.evals.monitoring.load_history.
≈ “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
Monitor evaluation history
Read versioned run manifests with helpers.evals.monitoring.load_history. Use classify_changes before comparing scores.
Separate:
- system behavior changes;
- data changes;
- task-mix or suite-version changes;
- evaluator changes; and
- operational failures such as blocked tools or expired connections.
Compare only compatible runs. Show per-case and slice movement, not only the aggregate. Run frozen sentinel examples for model graders so evaluator drift does not look like system drift.
Apply the named operating rule:
- continue when the intended change improved the target slice without a blocking regression;
- investigate when the cause is unclear or several inputs changed;
- rollback when a blocking regression follows a controlled system change; or
- escalate when the evaluator, data, or authorization boundary may be invalid.
Record the owner and next action. Monitoring is an operating practice, not a dashboard someone passively observes.
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Your trail
Files you open will collect here, so you can find them again.
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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.