CtrlK
BlogDocsLog inGet started
Tessl Logo

repo-health-investigate

Investigate one dotnet/machinelearning repository-health finding locally, gather evidence for an issue, pull request, or pipeline problem, determine root cause confidence, and draft a dashboard report. Use when following up a repo-health finding or investigating a specific ML.NET maintenance risk.

73

Quality

90%

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

SKILL.md
Quality
Evals
Security

Quality

Content

92%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.

A concise, well-structured instruction-only skill with an explicit approval gate before any write and clean one-level reference navigation. Inlining a minimal report-format sketch would lift actionability further.

Suggestions

Inline a short example of the dashboard report format (or its key fields) so the core output is actionable without opening the playbook.

Add a one-line note on what 'minimum evidence' means per category so the evidence-gathering step is executable without referencing the playbook.

DimensionReasoningScore

Conciseness

Lean, efficient body — a compact inputs table, a tight numbered workflow, and terse guardrail sentences — that assumes Claude's competence with no padded explanations.

5 / 5

Actionability

Gives a concrete 8-step procedure with specific categories and a confidence-classification scheme, but the exact report format is delegated to the playbook reference rather than inlined.

4 / 5

Workflow Clarity

Steps are clearly sequenced with explicit validation checkpoints — 'Show the exact report before any write' and 'Post one comment only after explicit approval' — gating the outward-facing write.

5 / 5

Progressive Disclosure

A clear overview body pointing to a single well-signaled one-level-deep reference (references/playbook.md, which exists), with content appropriately split between overview and detail.

5 / 5

Total

19

/

20

Passed

Description

87%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, specific description that answers both 'what' and 'when' with concrete actions and a clear use-when trigger. Only minor room to add synonyms or enumerate evidence types more finely.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions — 'gather evidence for an issue, pull request, or pipeline problem, determine root cause confidence, and draft a dashboard report' — with only minor gaps in enumerating evidence specifics.

4 / 5

Completeness

Explicitly states what the skill does (investigate, gather evidence, determine confidence, draft report) and when to use it via a concrete 'Use when ...' trigger clause.

5 / 5

Trigger Term Quality

Includes natural user-facing phrases like 'following up a repo-health finding' and 'investigating a specific ML.NET maintenance risk', with good coverage but a few synonyms missing.

4 / 5

Distinctiveness Conflict Risk

A clearly carved niche (dotnet/machinelearning repository-health findings) with distinct, specific triggers and minimal overlap risk with other skills.

5 / 5

Total

18

/

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
dotnet/machinelearning
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.