how
Use for "how does X work", walkthroughs of a data pipeline, notebook, or analysis module before changing something, and placement / ownership / layering…
Skill by hidenobunagai
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/hidenobunagai-how-fa7109
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About
Use cases: data
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
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- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/how
What's inside
- Skill name
how- Description
- 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.
- License
- MIT
- Instructions
- 37 lines in SKILL.md · view on GitHub
Files (3)
- SKILL.md
- explainer-prompt.md · references/
- explorer-prompt.md · references/
SKILL.md (first lines)
# 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.
From hidenobunagai/astack, MIT.
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Sketch data shapes (grain, keys, schemas), function signatures, and the module or pipeline structure before code, then stay in the loop while implementation…
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analyst-mode
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Data analysis style for an analysis repo.
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blast-radius
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Find what a change to a table, column, transform, metric, or notebook could break downstream (dashboards, saved queries, notebooks, consumers) before it…
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benchmark-checklist
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Vet a runtime measurement for a query, dataframe transform, pipeline, or notebook before you report or act on it: limiter, tuning, data volume, warm vs cold…
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catch-up
hidenobunagai
Catch the user up on what changed in this chat since their last message: pipeline runs, numbers, PRs, and agents, in a few plain sentences and small visuals.
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no-comments
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Spawn the comment-critic agent on a data repo's code, notebooks, or SQL, act on its accepted findings, and offer encodings for claimed constraints.
SkillAFreshness: A★ 1Claude Code +9 - 0.560score
arena
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Run N parallel candidates at the same analysis, pipeline, notebook, or chart task, pick a base, and graft the strongest parts of the rest into it.
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principle-define-the-question
hidenobunagai
Apply at the start of an analysis, before writing the first query.
SkillAFreshness: A★ 1Claude Code +9 - 0.556score
principle-encode-lessons-in-structure
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Apply when you write the same instruction a second time, or notice a recurring correction, such as a join that drops rows, a timezone mix-up, or a chart axis…
SkillAFreshness: A★ 1Claude Code +9 - 0.556score
plain-words
hidenobunagai
Restate the last message, such as an analysis result, a metric explanation, or a pipeline status, in plain human language with no jargon.
SkillAFreshness: A★ 1Claude Code +9 - 0.556score
principle-boundary-discipline
hidenobunagai
Apply when wiring data ingestion, parsing raw files, validating input schemas, or connecting to databases, APIs, and notebooks.
SkillAFreshness: A★ 1Claude Code +9
Signals
- Stars
- 1
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 23, 2026 (9 days ago)
- Last release
- Oct 23, 2026 (8 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/hidenobunagai/astack
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from hidenobunagai
- 0.568score
architect
hidenobunagai
Sketch data shapes (grain, keys, schemas), function signatures, and the module or pipeline structure before code, then stay in the loop while implementation…
SkillAFreshness: A★ 1Claude Code +9 - 0.568score
astack-help
hidenobunagai
Help a user pick the right astack skill, analyst-mode playbook, or principle for a data analysis task, such as EDA, a metric move, a notebook, a pipeline, or…
SkillAFreshness: A★ 1Claude Code +9 - 0.563score
benchmark-checklist
hidenobunagai
Vet a runtime measurement for a query, dataframe transform, pipeline, or notebook before you report or act on it: limiter, tuning, data volume, warm vs cold…
SkillAFreshness: A★ 1Claude Code +9 - 0.563score
analyst-mode
hidenobunagai
Data analysis style for an analysis repo.
SkillAFreshness: A★ 1Claude Code +9 - 0.563score
blast-radius
hidenobunagai
Find what a change to a table, column, transform, metric, or notebook could break downstream (dashboards, saved queries, notebooks, consumers) before it…
SkillAFreshness: A★ 1Claude Code +9 - 0.560score
no-comments
hidenobunagai
Spawn the comment-critic agent on a data repo's code, notebooks, or SQL, act on its accepted findings, and offer encodings for claimed constraints.
SkillAFreshness: A★ 1Claude Code +9
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how skill. Agent Mart. https://agentmart.dev/skills/hidenobunagai-how-fa7109 (last updated 2026-10-24)