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gtm-metrics

When the user wants to define GTM metrics, build a metrics dashboard, measure pipeline efficiency, or track AI product performance. Also use when the user mentions 'GTM metrics,' 'revenue latency,' 'pipeline metrics,' 'TTFV,' 'time-to-first-value,' 'data health,' 'attribution,' 'conversion rate,' 'CAC,' 'LTV,' 'NRR,' 'GTM dashboard,' 'magic number,' 'pipeline velocity,' or 'funnel metrics.' This skill covers GTM measurement from metric selection through dashboard design, including AI-specific cost metrics, attribution models, and weekly review cadences. Do NOT use for technical implementation, code review, or software architecture.

66

Quality

79%

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tessl review fix ./packages/skills-catalog/skills/(gtm)/gtm-metrics/SKILL.md
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 content is a well-structured, highly actionable GTM metrics reference with concrete formulas, benchmarks, and cadences, but it is monolithic and verbose with some duplicated reference material. Splitting dense benchmark/attribution tables into referenced files and de-duplicating the Quick Reference would improve both conciseness and progressive disclosure.

Suggestions

De-duplicate the Quick Reference against the section-1 tables, or consolidate the benchmarks into one location, to reduce token redundancy and lift conciseness.

Move large reference blocks (NRR/growth benchmark tables, attribution model comparison, tool selection) into separate files under references/ and link to them from SKILL.md to improve progressive disclosure.

Add an explicit validation checkpoint to the dashboard-build and weekly-review workflows (e.g., 'verify each metric against its benchmark target and flag red/yellow/green before publishing') to strengthen workflow clarity.

DimensionReasoningScore

Conciseness

The body is dense, reference-driven, and avoids explaining concepts Claude already knows, but it is voluminous (~415 lines) and the Quick Reference section duplicates figures already present in the section-1 tables (CAC payback, Magic Number, NRR), which could be tightened.

3 / 5

Actionability

Provides concrete formulas (Data Health Score, PQL Score), specific benchmark targets (CAC Payback <8 months, Magic Number >0.75, NRR >106%), a timed weekly agenda, and a scorecard template; minor gaps are the "$X" placeholders and the absence of SQL/code for computing the metrics.

4 / 5

Workflow Clarity

Clear sequencing from the "Before Starting" context-gathering list through numbered sections to the weekly/monthly/quarterly review cadences, with most checkpoints present; minor gap is the lack of an explicit validate-against-benchmarks feedback loop.

4 / 5

Progressive Disclosure

No bundle files exist and the entire skill is a monolithic ~415-line document; large reference blocks (benchmark tables, attribution model comparison, tool selection) that could live in separate referenced files are inlined, though section headers and the Quick Reference provide reasonable internal structure.

3 / 5

Total

14

/

20

Passed

Description

95%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.

The description is strong: it clearly states what the skill covers, provides an extensive list of natural trigger terms with synonyms, and includes an explicit negative boundary. The only minor weakness is that the named actions are domain-level rather than fine-grained operations.

DimensionReasoningScore

Specificity

Lists several concrete domain actions ("define GTM metrics, build a metrics dashboard, measure pipeline efficiency," "metric selection through dashboard design, including AI-specific cost metrics, attribution models, and weekly review cadences") with minor gaps in fine-grained granularity versus the anchor-5 example.

4 / 5

Completeness

Explicitly answers both what ("covers GTM measurement from metric selection through dashboard design...") and when ("Use when the user mentions..." with concrete trigger phrases), plus a negative boundary ("Do NOT use for technical implementation, code review, or software architecture").

5 / 5

Trigger Term Quality

Comprehensive coverage of natural user terms including synonyms ("TTFV"/"time-to-first-value," "GTM metrics," "revenue latency," "CAC," "LTV," "NRR," "magic number," "pipeline velocity," "funnel metrics"), matching the anchor-5 example.

5 / 5

Distinctiveness Conflict Risk

Clear niche (GTM measurement for AI products) with distinct triggers and an explicit negative boundary excluding technical implementation, code review, and architecture, minimizing conflict risk.

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
tech-leads-club/agent-skills
Reviewed

Table of Contents

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