agentleFS
Sign inStart free
hidenobunagaihidenobunagai/astack/skills/how/SKILL.md

how

Skill1 starsChanged 11 days agoMITView on GitHub

Use for \"how does X work\", walkthroughs of a data pipeline, notebook, or analysis module before changing something, and placement / ownership / layering questions (\"where should this transform live\", \"which module owns this metric\", \"is this the right layer\"). Explains the data flow, subsystem architecture, and runtime flow for onboarding. Use why for motivation.

90 other projects use a version of this, 48 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use setup-pstack, ketchup-video and 3 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • bnemaSkill

    setup-pstack

    Configure which Pi models pstack uses per role. Detects available provider/model specs and writes pstack's user configuration. Use for /setup-pstack, "configure pstack models", or changing pstack model choices.

    Also in 25 projects that use this

  • tristanphvnSkill

    ketchup-video

    Catch the user up like /keel:ketchup, with a short silent video of slides for what happened and the waiting calls as text under it. Use for /keel:ketchup-video.

    Also in 13 projects that use this

  • ianclearySkill

    poteto-help

    Guides users through pstack setup, /poteto-mode, and picking the skill, playbook, or principle for a task. Type /poteto-help with a question.

    Also in 14 projects that use this

  • eryzerzSkill

    make-bot-ui

    Use when building a custom UI (page, dashboard, buttons) that should wake a Grok Bot over a webhook, when the user must provide a webhook sender key, or when exposing that UI on Tailscale.

    Also in 19 projects that use this

  • instructureSkill

    ketchup

    Rebuild context after /clear by analyzing uncommitted changes across Canvas LMS and all plugin repositories

    Also in 22 projects that use this

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 89 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    When in doubt, take the simple path.

    line 24
  2. Very specific:

    When all explorers have returned, spawn one explainer with references/explainer-prompt.md and every explorer's findings filled in.

    line 32

“Common” is measured: kept by at least 6 in 10 of the 89 edited versions of this file we could read. “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

How

Explore the code to answer "how does X work?" questions. Produce an architecture explanation at the level of a senior analyst or engineer onboarding onto a data project. The reader should build a working mental model, not get annotated source.

The subject can be a Python package (uv, Polars), a notebook (Jupyter or marimo), SQL or dbt-style models, a pipeline, or a dashboard.

Spawn settings

Agents in this skill use the Agent tool. Agents run in the background by default and report back on completion. Wait for every agent before the next step.

  • Explorers: subagent_type Explore, model haiku, effort low. The brief says "do not edit files" and "do not run git write commands".
  • Explainer: subagent_type general-purpose, model sonnet, effort medium.

1. Assess complexity

  • Simple (one module, one transform, one notebook section, or a narrow question such as "how does this function compute weekly retention"): no explorers. Spawn one explainer that explores and explains in one pass. Build its prompt from references/explainer-prompt.md without the explorer-findings section. Go to step 4.
  • Complex (a pipeline across several stages or files, a metric defined in several places, a full project overview): go to step 2.

When in doubt, take the simple path.

2. Explore (complex only)

Split the question into 2 to 4 angles, each a distinct slice. For a pipeline, the usual angles are: raw inputs and ingestion, transforms and joins, metric or feature definitions, and consumers (notebooks, reports, dashboards). Spawn all explorers in one message, each with references/explorer-prompt.md and its angle filled in.

3. Synthesize (complex only)

When all explorers have returned, spawn one explainer with references/explainer-prompt.md and every explorer's findings filled in.

4. Present

Present the explainer's output. Light edits for clarity or for context from the conversation are fine. Do not rewrite it substantially.

Output format

The explanation uses the sections in references/explainer-prompt.md: Overview, Key Concepts, How It Works, Where Things Live, Gotchas. Drop any section that does not apply.

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