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investigate-metric

Diagnose why a product metric changed (dropped, spiked, or plateaued) by orchestrating breakdowns, actors, paths, lifecycle, retention, and annotations queries. Use when the user reports an anomaly, asks "why did X change?", or needs root-cause analysis for a trend, funnel, retention, stickiness, or lifecycle metric.

65

Quality

82%

Does it follow best practices?

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The risk profile of this skill

SKILL.md
Quality
Evals
Security

Quality

Content

63%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.

The orchestrator body is lean, well-sequenced, and rich with concrete tool/field references and validation checkpoints. Its main defect is packaging: every detailed playbook and helper script it points to is missing from the bundle, so the progressive-disclosure skeleton leads nowhere and the executable script commands cannot run.

Suggestions

Ship the referenced bundle files (references/*.md playbooks, shared-patterns.md, common-causes.md, scripts/*.py) so the one-level-deep progressive-disclosure links resolve and the skill is functional as a complete artifact.

Since the helper scripts are currently absent, either inline a minimal fallback for compare_to_prior_periods.py / breakdown_attribution.py or note in the body that they must be present before running the quoted commands.

Add a one-line note in Step 3 on what a playbook provides (numbered diagnostic steps + confirming queries) so the orchestration flow is not a black box when a playbook file is missing.

DimensionReasoningScore

Conciseness

The body is information-dense with no padding over concepts Claude already knows (tables for tools, terse step descriptions), though the findings template plus the confidence rule-of-thumb add length that could be trimmed slightly.

4 / 5

Actionability

Concrete in-text guidance exists (exact tool names, fields like 'kind', 'series', 'dateRange', 'compareFilter: {"compare": true}', and script invocation syntax), but the two referenced helper scripts and the per-kind playbook steps that hold the bulk of executable detail are absent from the bundle, so the script commands are not actually runnable and key steps are missing.

3 / 5

Workflow Clarity

Steps 1–5 are clearly sequenced with sub-steps and explicit validation checkpoints (2.2 'If the movement is normal variance, report that and stop'; Step 4 control-segment cross-check) plus a confidence checklist, but Step 3 delegates the detailed procedure to playbooks that are not present, leaving a gap in the flow.

4 / 5

Progressive Disclosure

The overview is well organized with a dedicated Reference files section and clearly signaled, one-level-deep links to 11 files, but none of those files (7 playbooks, shared-patterns.md, common-causes.md, 2 scripts) exist in the bundle, so the disclosed content cannot be reached or verified — capping the structure at mid-range.

3 / 5

Total

14

/

20

Passed

Description

96%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: third-person voice, explicit what-and-when, concrete query types, and a verbatim natural trigger phrase. The only weakness is that the tool isn't named, leaving slight overlap risk with general analytics RCA skills.

DimensionReasoningScore

Specificity

Names the domain (product metrics) and lists multiple concrete actions — 'orchestrating breakdowns, actors, paths, lifecycle, retention, and annotations queries' — giving comprehensive coverage of the diagnostic query types rather than vague verbs.

5 / 5

Completeness

Explicitly answers both 'what' ('Diagnose why a product metric changed ... by orchestrating ... queries') and 'when' ('Use when the user reports an anomaly, asks "why did X change?", or needs root-cause analysis ...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes the verbatim natural phrase a user would say ('why did X change?') plus 'anomaly', 'root-cause analysis', and 'dropped, spiked, or plateaued', covering the common trigger variations comprehensively.

5 / 5

Distinctiveness Conflict Risk

The named query orchestration (lifecycle, stickiness, paths, retention) signals a clear niche, but the description never names the tool (PostHog) and 'product metric root-cause analysis' is broad enough to risk minor overlap with a generic analytics-investigation skill.

4 / 5

Total

19

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 20 missing, 1 suspicious

Warning

referenced_paths_exist

Referenced path issues: 2 missing

Warning

Total

14

/

16

Passed

Repository

PostHog/posthog

Path

products/product_analytics/skills/investigate-metric/SKILL.md

Reviewed

Table of Contents

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