how
Explain a subsystem's behavior from source, callers, data structures, and runtime evidence.
Skill by MachineLearning-Nerd
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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/machinelearning-nerd-how
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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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
- None asked for
- 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
- Explain a subsystem's behavior from source, callers, data structures, and runtime evidence. Use for how questions or understanding code before a change.
- License
- MIT
- Instructions
- 32 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# How Read ../pstack-harness/SKILL.md. Remain read-only when answering an investigation. Find the relevant entry point, callers, authoritative types, and the smallest set of files that establish behavior. Do not dump the whole repository into context. Answer narrow questions about one function or file directly with evidence. For a substantial single-module explanation, delegate a fresh read-only explainer using references/explainer-prompt.md without its explorer-findings section. Replace its synthesis premise with an instruction to explore the source and explain it in one pass; remove statements that prior explorers did the research. For work spanning subsystems, delegate two to four read-only explorers using references/explorer-prompt.md, with distinct questions and file pointers. Resolve `how explorer` for discovery and `how explainer` for synthesis. Every delegate inherits the current chat's model. Assess each brief under [the effort contract](../pstack-harness/SKILL.md#task-difficulty-and-effort). Give a fresh explainer the explorer findings and original question. The explainer owns the final explanation; the lead makes only light factual or clarity edits. Brief researchers with questions, not presumed answers. If delegation is unavailable, explain the missing independent research or synthesis and answer from the evidence available. Never fabricate delegation or runtime checks. Explain Overview, Key Concepts, How It Works, Where Things Live, and Gotchas at the depth the question needs. Cite actual files and symbols. Distinguish observed runtime results from source inference, and identify missing evidence. For intent or history, route to why only when that evidence can answer the question. Apply unslop to the reply. In an investigation, do not create a PR, edit the code, or trigger implementation merely because a design question arose.
From MachineLearning-Nerd/pstack-dual, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 15, 2026 (15 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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SkillAFreshness: AClaude Code +9 - 0.561score
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how skill. Agent Mart. https://agentmart.dev/skills/machinelearning-nerd-how (last updated 2026-10-24)