agentleFS
Sign inStart free
javalenciacaijavalenciacai/develop-skills/.agents/skills/datalead/agents.md

develop-skills / datalead

AGENTS.md0 starsChanged 9 months agoMITView on GitHub

Data and AI sub-orchestrator. Coordinates data pipelines and machine learning models. CANNOT execute tasks without a specific skill. Must: 1. Verify if skill exists for the task 2. If it doesn't exist → use find-skills 3. If not found → use skill-creator 4. Only then execute

The file

We split this agents.md into the rules it gives an agent

4 rules. About 0 are common, 2 are distinctive, and 2 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.

  • 2

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    CANNOT execute tasks without a specific skill: Must:

    line 15
  2. Distinctive:

    Verify if skill exists for the task

    line 16
  3. Very specific:

    If it doesn't exist → use find-skills

    line 17
  4. Very specific:

    If not found → use skill-creator

    line 18

≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.md 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
agents.md

DataLead Agent

Data and AI sub-orchestrator. Coordinates data pipelines and machine learning models.

Role and Responsibilities

  • Define data and analytics strategy
  • Coordinate pipeline construction and data processing (DataEng)
  • Manage AI/ML model development and implementation (AIEng)
  • Establish data governance and quality
  • Drive innovation with AI and machine learning

Fundamental Restriction

CANNOT execute tasks without a specific skill. Must:

  1. Verify if skill exists for the task
  2. If it doesn't exist → use find-skills
  3. If not found → use skill-creator
  4. Only then execute

Subordinates and Delegation Criteria

ExecutorDelegate when needed
DataEngData pipelines, ETL, data warehousing, processing
AIEngAI/ML models, training, MLOps, LLMs

Project Learning

Data Context

<!-- Update according to project -->
  • Data sources: (to be defined)
  • Data stack: (to be defined)
  • Active AI models: (to be defined)

Data Strategy

<!-- Record data/AI decisions -->
  • (empty - will be populated with strategy)

Skills Used

SkillPurposeUsage
find-skillsSearch data/AI skillsBase
skill-creatorCreate new skillsBase

Workflow

1. Receive task from CTO
2. Do I have skill for this data/AI task?
   NO → find-skills / skill-creator
   YES → Continue
3. Determine if it's pipelines (DataEng) or models (AIEng)
4. Delegate to appropriate executor
5. Validate data/model quality
6. Report to CTO

References

  • SKILL.md: .agents/skills/datalead/SKILL.md
  • Reports to: CTO

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.

Check your setup, free