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.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

65%

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

A comprehensive, actionable GTM-measurement reference that scores well on actionability but is held back by a monolithic single-file structure, dated benchmark claims not isolated in a deprecated section, and reference-catalog organization lacking explicit workflow checkpoints.

Suggestions

Move the large benchmark tables (NRR by stage, growth-rate bands, attribution model comparison) into a one-level-deep reference file (e.g. BENCHMARKS.md) and keep SKILL.md as a concise overview with links, improving progressive disclosure.

Isolate time-sensitive figures (2025-2026 growth/CAC/NRR stats, '42% in 2025') into an explicitly dated 'benchmark sources' or 'old patterns' block so stale data is easy to spot and refresh.

Add a short sequenced workflow (gather context → select metrics → build dashboard → choose attribution → set review cadence) with explicit checkpoints, rather than presenting the material as parallel numbered reference sections.

Dimension	Reasoning	Score
Conciseness	Mostly efficient and packed with domain-specific benchmarks Claude does not reliably know, but it carries time-sensitive dated claims scattered throughout ('26% in 2025-2026', '42% of SaaS companies... in 2025', 'NRR median (2025)') that are not isolated in a deprecated/old-patterns section, and the Quick Reference repeats figures already present in the body tables.	2 / 3
Actionability	Concrete, executable guidance throughout — explicit formulas ('Data Health Score = (Completeness * 0.35) + (Accuracy * 0.30) + (Recency * 0.20) + (Consistency * 0.15)', 'PQL Score = (Usage Signals * 0.50) + ...'), specific targets, tiered dashboard structures, and tool-selection tables; absence of code is fine for this instruction skill.	3 / 3
Workflow Clarity	Sections are numbered (1–9) and the weekly review has a timed agenda, but the body reads as a reference catalog rather than a sequenced process, with no explicit validation checkpoints between phases.	2 / 3
Progressive Disclosure	Well-organized into clear sections, but it is a single monolithic ~420-line SKILL.md with no bundle files; large benchmark and attribution reference tables that could live in separate one-level-deep files are inlined rather than split out.	2 / 3
	Total	9 / 12 Passed

Description

100%

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, trigger-rich description that clearly states what the skill does, when to use it, and when not to. It is in third person about the skill and avoids vague language.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'define GTM metrics, build a metrics dashboard, measure pipeline efficiency, or track AI product performance' plus 'metric selection through dashboard design, including AI-specific cost metrics, attribution models, and weekly review cadences', matching the multiple-specific-actions anchor.	3 / 3
Completeness	Explicitly answers both what ('covers GTM measurement from metric selection through dashboard design') and when ('When the user wants to...' / 'Also use when the user mentions...'), with an explicit negative trigger ('Do NOT use for technical implementation, code review, or software architecture').	3 / 3
Trigger Term Quality	Broad coverage of natural user terms ('GTM metrics,' 'revenue latency,' 'TTFV,' 'time-to-first-value,' 'CAC,' 'LTV,' 'NRR,' 'magic number,' 'pipeline velocity,' 'funnel metrics'), the kind of vocabulary a user would actually say.	3 / 3
Distinctiveness Conflict Risk	Clear niche (GTM measurement for AI products) with distinct triggers and an explicit exclusion clause, making it unlikely to fire for adjacent skills like ai-pricing or gtm-engineering.	3 / 3
	Total	12 / 12 Passed

Validation

100%

Warnings & errors only

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: tech-leads-club/agent-skills
Commit: 9026164

Reviewed: 4 days ago

Table of Contents

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