CtrlK
BlogDocsLog inGet started
Tessl Logo

github-repo-signals

Extract and score leads from GitHub repositories by analyzing stars, forks, issues, PRs, comments, and contributions. Produces unified multi-repo CSV with deduplicated user profiles. No paid API credits required.

56

Quality

66%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./skills/lead-generation/packs/lead-gen-devtools/github-repo-signals/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%

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

Highly actionable body with a real executable command and precise data schemas, but it is somewhat verbose and inlines a long analysis/recommendation playbook that would benefit from being split into reference files, and it lacks validation feedback loops for its batch operation.

Suggestions

Add a post-run verification checkpoint (e.g., confirm the CSV row count matches the requested limit and surface rate-limit/error recovery steps) to lift workflow clarity.

Move the Phase 3 analysis briefing and 7-case recommendation framework into a references/ANALYSIS_PLAYBOOK.md, keeping SKILL.md a lean overview with a clearly signaled one-level-deep link.

Trim restated rationale in the recommendation framework (e.g., "This is the highest-signal segment — call it out explicitly") to improve token efficiency.

DimensionReasoningScore

Conciseness

Mostly efficient with concrete commands, scoring weights, and CSV schemas, but the 7-case recommendation playbook and analysis briefing include restated rationale and hand-holding ("call it out explicitly", elaborate cost-estimate breakdowns) that could be tightened.

2 / 3

Actionability

Provides a copy-paste-ready invocation of the real ${CLAUDE_SKILL_DIR}/scripts/gh_repo_signals.py with concrete flags, explicit per-interaction scoring weights, and full CSV column definitions.

3 / 3

Workflow Clarity

Steps 1–8 are clearly sequenced with an environment check, but the long batch profile-fetch operation (~5,000 profiles/hr) lacks an output-verification checkpoint and a rate-limit/error feedback loop, capping workflow clarity at 2.

2 / 3

Progressive Disclosure

Content is well-sectioned and the referenced scripts/gh_repo_signals.py is a real bundle file, but the analysis and recommendation playbook is inlined monolithically with no one-level-deep reference documents (no references/ or assets/ directory).

2 / 3

Total

9

/

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.

A specific, distinct description that clearly states what the skill does, but it omits an explicit "Use when..." trigger clause and relies on limited variation of the core lead-finding phrasing.

Suggestions

Add a "Use when..." clause with explicit user triggers, e.g. "Use when finding leads or prospects from GitHub repositories, or when the user wants to identify active developers in a repo's community."

Broaden trigger terms with natural variations a user might say: "find leads", "GitHub users", "lead generation", "scrape GitHub repo".

Keep the concrete action list as-is — it is the strongest part of the description.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — "analyzing stars, forks, issues, PRs, comments, and contributions" and "Produces unified multi-repo CSV with deduplicated user profiles" — matching the multi-action anchor.

3 / 3

Completeness

The "what" is clearly stated, but there is no "Use when..." clause or equivalent explicit trigger guidance, so completeness is capped at 2 per the judging guidelines.

2 / 3

Trigger Term Quality

Relevant natural terms appear ("GitHub repositories", "leads", "stars", "forks", "issues", "PRs") but the core task lacks common variations a user might say ("find leads", "GitHub users", "lead generation", "scrape GitHub"), so coverage is partial.

2 / 3

Distinctiveness Conflict Risk

The GitHub lead-extraction niche is distinct and tied to specific interaction signals, making it unlikely to trigger for the wrong skill.

3 / 3

Total

10

/

12

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.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository
gooseworks-ai/goose-skills
Reviewed

Table of Contents

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.