Agent skill · magnus919
agent-evals-and-observability
Design, run, review, or release framework- and vendor-neutral evaluations and observability for AI agents. Use when defining agent evals, datasets, graders, trajectory review, regression analysis, release gates, production traces, or privacy-aware telemetry. Covers task and trajectory contracts, statistical comparisons, and incident-to-case learning; route framework implementation to pydanticai or langgraph when needed.
What it needs
About 3k tokens when loaded.
What this skill does
Agent Evals and Observability Evaluation asks whether behavior meets a defined criterion on a declared dataset or production sample. Observability supplies traces, logs, metrics, correlations, and diagnostic context. Use both; neither proves what the other does. Workflow 1. Define the decision, risk, task contract, trajectory contract, and unacceptable outcomes. Select evidence by harm, reversibility, and deployment stage, not a staged completeness scale. 2. Create an immutable dataset version and manifest before comparing versions. Declare provenance, rights/consent, slices, fixtures, expected side effects, contamination risk, limitations, retention, and changelog. 3. Select complementary graders that observe the claimed property. Use deterministic checks for observable mechanics; use execution/environment checks for state and side effects; use human, model-judge, pairwise/ranking, domain, and safety review where appropriate. 4. Run the candidate and baseline under comparable conditions. Preserve run configuration, stochastic repeats where variability affects the decision, failures/timeouts, and trajectory evidence. 5. Report a multidimensional profile and uncertainty. Use paired comparisons where possible; inspect slices, missingness, base rates, confounders, effect sizes, and multiple comparisons. Non-significance is not equivalence. 6. Apply a risk-tiered release gate: hard safety, privacy, authorization, and side-effect invariants cannot be averaged away. Record authority, insufficient-evidence outcomes, rollback, and follow-up. Load release-engineering when this evidence must be incorporated into artifact promotion, deployment, rollback, or a broader release train. 7. Instrument production with minimized, redacted telemetry. Feed verified incidents and near misses into cases after consent, transformation, and contamination review. Stop when the supported decision, evidence gaps, residual risks, and responsible owner are recorded. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills magnus919/agent-evals-and-observability