champion-tracker

Track product champions for job changes and qualify their new companies against ICP. Takes a CSV of known champions (with LinkedIn URLs), creates a baseline snapshot via Apify enrichment, then detects when champions move to new companies. Scores new companies on a 0-4 ICP fit scale. Outputs a downloadable CSV of movers with qualification verdicts.

Quality

83%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Low

Low-risk findings worth noting

Quality

Content

100%

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

The body is well-structured and highly actionable, with executable commands, explicit dry-run validation for the costly batch step, and a clean one-level-deep reference to the script. It assumes Claude's competence and avoids concept padding.

Dimension	Reasoning	Score
Conciseness	Lean body with no explanations of concepts Claude already knows; tables, commands, and the file-structure map each earn their place. Not 2 because there is no unnecessary padding to tighten.	3 / 3
Actionability	Provides copy-paste executable commands (e.g. 'python3 …/champion_tracker.py init -i champions.csv --dry-run'), explicit prerequisite env vars, required CSV columns, and concrete cost figures. Not 2 because guidance is complete and executable, not pseudocode.	3 / 3
Workflow Clarity	Clear two-phase sequence (Phase A numbered 1–4; Phase B init→check→status) with an explicit dry-run validation checkpoint before costly batch LinkedIn enrichment. Not 2 because the validation checkpoint is explicit, not implicit, so the batch-operation cap does not apply.	3 / 3
Progressive Disclosure	SKILL.md serves as a clear overview with a single one-level-deep, well-signaled script reference (champion_tracker.py, confirmed present in scripts/); implementation detail lives in the script rather than being inlined. Not 2 because content is appropriately split and easily navigable.	3 / 3
	Total	12 / 12 Passed

Description

67%

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 specific and distinctive with a clear niche, but lacks an explicit 'Use when' trigger clause and relies on domain jargon over natural user phrasing. Adding trigger guidance and common term variations would raise the completeness and trigger-term scores.

Suggestions

Add an explicit 'Use when…' clause (e.g. 'Use when tracking known product champions for job changes or qualifying their new employers against ICP') to satisfy the completeness dimension.

Broaden natural trigger terms with common variations a user might actually say (e.g. 'champion tracking', 'customers who left', 'job change alerts', 'ICP scoring') instead of relying solely on rev-ops jargon.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — track champions for job changes, qualify new companies against ICP, create a baseline snapshot via Apify, detect moves, score on a 0-4 scale, and output a CSV. Not 2 because the action set is comprehensive rather than partial.	3 / 3
Completeness	Clearly answers what the skill does, but provides no 'Use when…' clause or equivalent explicit trigger guidance; per the rubric this caps completeness at 2.	2 / 3
Trigger Term Quality	Terms like 'product champions', 'job changes', 'ICP', and 'movers' are natural for the niche, but the phrasing is jargon-heavy with no common variations and no explicit 'Use when' trigger. Not 1 (real relevant keywords exist), not 3 (coverage of natural variations is thin).	2 / 3
Distinctiveness Conflict Risk	Targets a clear niche — champion job-change detection plus ICP qualification — making it unlikely to trigger for the wrong skill.	3 / 3
	Total	10 / 12 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: gooseworks-ai/goose-skills
Path: skills/sales/capabilities/champion-tracker/SKILL.md
Commit: 8866b2a

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.