smart-docs

Documentation Community
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
Documentation
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
85 / 100 · community maintained
Author / version / license
@sopaco · no license declared
Token usage
Lean
Setup complexity
Guided setup
External API key
Not required
Operating systems
macOS · Linux · Windows
Runtime requirements
Node.js · Python
Permissions
  • Read-only
  • Write / modify
Network behavior
External requests
Install commands
26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools,默认拥有全部工具权限。; 上游仓库已 206 天未更新,可能与最新 agent 行为不一致。

Output preview smart-docs.preview
---
name: smart-docs
description: AI-powered comprehensive codebase documentation generator. Analyzes project structure, identifie…
category: documentation
runtime: Node.js / Python
---

# smart-docs output preview

## PART A: Task fit
- Use case: AI-powered comprehensive codebase documentation generator. Analyzes project structure, identifies architecture patterns, creates C4 model diagrams, and generates professional technical documentation. Use when users need to document codebases, understand software architecture, create technical specs, or generate developer guides. Supports all programming languages. Alternative to Litho/deepwiki-rs that uses Claude Code subscription without external API costs..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Core Principles / Workflow / Phase 1: Project Discovery (5-10 minutes)” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “AI-powered comprehensive codebase documentation generator. Analyzes project structure, identifies architecture patterns, creates C4 model diagrams, and generates professional technical documentation. Use when users need to document codebases, understand software architecture, create technical specs, or generate developer guides. Supports all programming languages. Alternative to Litho/deepwiki-rs that uses Claude Code subscription without external API costs.”.
- **02** When the source has headings, the agent prioritizes “Core Principles / Workflow / Phase 1: Project Discovery (5-10 minutes)” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; may access external network resources; usually needs no extra API key.

## Running Rules
- read files, write/modify files; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: AI-powered comprehensive codebase documentation generator. Analyzes project structure, identifies architecture patterns, creates…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Core Principles”, “Workflow”, “Phase 1: Project Discovery (5-10 minutes)”, “Phase 2: Architecture Analysis (10-20 minutes)”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name smart-docs directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Core Principles / Workflow / Phase 1: Project Discovery (5-10 minutes)” before expanding.

Boundaries And Review

  • Dependencies: It usually needs no extra API key, so start with a small validation task.
  • Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Discussion

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