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choose-metric

Recommend primary/guardrail metrics for an experiment or rollout, judged against a good-metric checklist. Checks existing metrics and event health, surfaces auto-created guardrails. Doesn't create or attach metrics — see create-metric / instrument-metric / manage-experiments. Trigger phrases: choose/pick a metric, primary metric, guardrail metric, what to monitor.

Quality

93%

Does it follow best practices?

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SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-engineered procedural skill: concrete tool invocations and CLI commands, a well-sequenced 8-phase workflow with stop conditions and validation checkpoints, and clean offloading of domain detail to anchored external references. The only notable weakness is minor redundancy (health-check skipping guidance repeated in Phase 6 and Performance Notes).

DimensionReasoningScore

Conciseness

The body is dense and almost entirely Harness-specific knowledge Claude would not have (spread: PER vs ACROSS semantics, 404 window/scope caveats, pagination totality behavior, attribution key rule), assuming competence on generic concepts. It sits at anchor 4 rather than 5 due to minor tightening opportunities, e.g., "Don't health-check metrics you're explicitly ruling out" is stated both at the end of Phase 6 and again in Performance Notes.

4 / 5

Actionability

Guidance is fully executable for an instruction-only skill: exact MCP tool names and params ("harness_get · fme_experiment · params: { experiment_id }"), CLI equivalents, specific fields to read ("keyMetrics / supportingMetrics", "baseEventTypes[].eventTypeId"), decision rules with reasons, and a copy-ready output template with a gap-handling section. Matches anchor 5.

5 / 5

Workflow Clarity

Eight numbered phases with a clear sequence, an explicit stop condition ("if it's an experiment and the user hasn't stated a hypothesis, ask for one before recommending a primary metric"), a validation checkpoint (Phase 6 requires exact Get event type for every event reference before recommending), and error-recovery feedback loops (the Troubleshooting table and the 404 → check scope/spelling → confirm-idle-before-reinstrumenting path). Matches anchor 5; no destructive/batch cap applies since the skill is read-only.

5 / 5

Progressive Disclosure

The SKILL.md keeps decision logic inline and pushes detail to one-level-deep, well-signaled references with section anchors (metric-design.md, tool-map.md, concepts.md, scope-establishment.md) cited at the point of need; no bundle files exist alongside it, and the small inline Tools table is load-bearing for every run. This fits anchor 5's clear-overview, one-level-deep reference structure rather than anchor 4, which requires organizational gaps.

5 / 5

Total

19

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: concrete capabilities, explicit scope boundary against sibling skills, and explicit trigger phrases for both experiment and rollout contexts. The only weakness is trigger-term coverage missing a few natural variants users might say.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — "Recommend primary/guardrail metrics", "Checks existing metrics and event health", "surfaces auto-created guardrails" — plus an explicit non-goal boundary ("Doesn't create or attach metrics"), giving comprehensive coverage of the skill's capabilities. It exceeds anchor 4 because there are no meaningful gaps within the metric-selection domain it claims.

5 / 5

Completeness

Both questions are explicitly answered: a concrete "what" (recommend, check, surface, and an explicit exclusion) and a concrete "when" in the form of explicit trigger phrases. This matches anchor 5 exactly and is not anchor 4, since the "when" clause is fully explicit rather than merely present.

5 / 5

Trigger Term Quality

"Trigger phrases: choose/pick a metric, primary metric, guardrail metric, what to monitor" provides good natural-keyword coverage with a synonym pair and a question phrasing. It falls short of anchor 5 because common variations like "which metric should I use", "what should we measure", or "A/B test metric" are missing.

4 / 5

Distinctiveness Conflict Risk

"Doesn't create or attach metrics — see create-metric / instrument-metric / manage-experiments" explicitly fences off the sibling skills, and the trigger phrases (primary/guardrail metric, what to monitor) form a clear niche. Minimal conflict risk, matching anchor 5.

5 / 5

Total

19

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 9 suspicious

Warning

Total

15

/

16

Passed

Repository

harness/harness-ai

Path

plugins/claude/skills/choose-metric/SKILL.md

Reviewed

Table of Contents

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