agentleFS
BioconductorBioconductor/ai-agent-skills/skills/check-bioconductor-skills/SKILL.md

check-bioconductor-skills

Skill10 starsChanged 3 months agoMITView on GitHub

Verify that bioconductor/ai-agent-skills are available and help users find the right skill

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Report Status: Start with "✅ Skills detected" if found, or "❌ Skills not detected" if missing.

    line 30
  2. Distinctive:

    Skills not detected: Verify skill paths in editor settings, ensure the repository is cloned, and see platform-specific instructions.

    line 52

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

check-bioconductor-skills

Confirm bioconductor skills are installed and visible, then provide a concise catalog of available skills by querying SKILLS.md.

Usage

Invoke this skill to check your setup or discover available skills:

  • "Do I have the bioconductor skills?"
  • "Is there a good bioconductor skill for [task]?"

Prerequisites

  • The bioconductor/ai-agent-skills repository is present locally.

Process

  1. Read SKILLS.md from the repository root — this is the primary discovery mechanism.
  2. Read USAGE.md (if present) for the "Finding Skills by Use Case" section, which provides curated task-to-skill mappings for richer recommendations.
  3. Parse skill names, descriptions, and categories from SKILLS.md.
  4. List/Recommend: Present skills organized by category, or recommend the 1-2 best-fit skills for a specific task. Use the use-case mappings from USAGE.md when the user asks task-oriented questions (e.g. "Is there a skill for X?").
  5. Report Status: Start with "✅ Skills detected" if found, or "❌ Skills not detected" if missing.

Examples

Example: Discovery and Recommendation

User: "Do I have the bioconductor skills? Is there one for testing?"

Agent: (Reads SKILLS.md and USAGE.md)

✅ Skills detected

Available bioconductor skills:
[Lists top-level categories and skills]

For testing, I recommend:
**Primary**: improve-code-coverage
[Rationale]

Troubleshooting

  • Skills not detected: Verify skill paths in editor settings, ensure the repository is cloned, and see platform-specific instructions.
  • Duplicate listings: Multiple configuration locations pointing to the same skills (normal).

Related: See SKILLS.md for the complete index and USAGE.md for use-case guidance.

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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.