flow-maintenance — analyse de code flow-maintenance, flowai, community, analyse de code, ide skills, maintenance de projet, outils de développement, règles de curseur, développement de logiciels, amélioration de code

v1.0.0

À propos de ce Skill

Parfait pour les Agents d'Analyse de Code nécessitant une maintenance et des audits de santé rigoureux dans des contextes de développement de logiciels. Flow-maintenance est un ensemble de règles de curseur qui standardisent le travail dans différents contextes de développement de logiciels

Fonctionnalités

Analyse de déviations structurelles
Détection de documentation inconsistante
Identification de code mort
Analyse de complexité
Détection de dette technique
Génération de documentation de code

# Core Topics

korchasa korchasa
[3]
[0]
Updated: 3/16/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 10/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
10/11
Quality Score
60
Canonical Locale
en
Detected Body Locale
en

Parfait pour les Agents d'Analyse de Code nécessitant une maintenance et des audits de santé rigoureux dans des contextes de développement de logiciels. Flow-maintenance est un ensemble de règles de curseur qui standardisent le travail dans différents contextes de développement de logiciels

Pourquoi utiliser cette compétence

Habilite les agents à exécuter un balayage de maintenance de 7 points, en identifiant les déviations structurelles, les incohérences de documentation, le code mort, les points chauds de complexité, la dette technique, la documentation de code manquante et la dérive de terminologie, en utilisant des règles de curseur pour standardiser le travail et assurer des bases de code maintenables et documentées.

Meilleur pour

Parfait pour les Agents d'Analyse de Code nécessitant une maintenance et des audits de santé rigoureux dans des contextes de développement de logiciels.

Cas d'utilisation exploitables for flow-maintenance

Automatiser les audits de santé de la base de code pour détecter la dette technique et les points chauds de complexité
Générer des résultats actionnables pour la documentation de la planche blanche et la maintenance du projet
Déboguer les incohérences de code et la dérive de terminologie dans des contextes de développement de logiciels

! Sécurité et Limitations

  • Nécessite un accès à la base de code et à la documentation du projet
  • Limité à des contextes de développement de logiciels
  • Nécessite une intégration avec des règles de curseur pour la standardisation

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

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FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is flow-maintenance?

Parfait pour les Agents d'Analyse de Code nécessitant une maintenance et des audits de santé rigoureux dans des contextes de développement de logiciels. Flow-maintenance est un ensemble de règles de curseur qui standardisent le travail dans différents contextes de développement de logiciels

How do I install flow-maintenance?

Run the command: npx killer-skills add korchasa/flowai/flow-maintenance. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for flow-maintenance?

Key use cases include: Automatiser les audits de santé de la base de code pour détecter la dette technique et les points chauds de complexité, Générer des résultats actionnables pour la documentation de la planche blanche et la maintenance du projet, Déboguer les incohérences de code et la dérive de terminologie dans des contextes de développement de logiciels.

Which IDEs are compatible with flow-maintenance?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for flow-maintenance?

Nécessite un accès à la base de code et à la documentation du projet. Limité à des contextes de développement de logiciels. Nécessite une intégration avec des règles de curseur pour la standardisation.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add korchasa/flowai/flow-maintenance. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use flow-maintenance immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

flow-maintenance

Install flow-maintenance, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Task: Project Maintenance & Health Audit

Overview

Execute a rigorous 7-point maintenance sweep to identify structural deviations, documentation inconsistencies, dead code, complexity hotspots, technical debt, missing code documentation, and terminology drift. All findings must be actionable and saved to whiteboard.

Context

<context> This command is the "Garbage Collector" and "Building Inspector" for the project. It ensures the codebase remains maintainable, documented, and aligned with architectural standards. It addresses: 1. **Structure**: Files in wrong places. 2. **Consistency**: Docs vs. Code truth. 3. **Hygiene**: Dead code, unused imports, weak tests. 4. **Complexity**: "God objects" and massive functions. 5. **Debt**: Accumulated TODOs. 6. **Language**: Inconsistent terminology. 7. **Doc Coverage**: Missing explanations in code. </context>

Rules & Constraints

<rules> 1. **Output Target**: All findings MUST be written to whiteboard. Start with a timestamped header. 2. **Precision**: Use specific thresholds (e.g., File > 500 lines). 3. **Constructive**: Every "Issue" must have a "Proposed Fix". 4. **Holistic**: Scan `documents/`, `.cursor/`, and source code directories. 5. **Mandatory**: Use a task management tool (e.g., `todo_write`, `todowrite`) to track progress through the 7 phases. 6. **Language Agnostic**: Adapt checks (imports, syntax, test patterns) to the primary language of the project (TS, JS, Py, Go, etc.). </rules>

Instructions

<step_by_step>

  1. Initialize & Plan

    • Use a task management tool (e.g., todo_write, todowrite) to create a plan covering the 7 phases below.
    • Read project whiteboard to preserve existing long-term notes (if any), but clear old automated reports.
    • Identify project's primary language and source directories.
  2. Phase 1: Structural Integrity

    • File placement: Check that all source files reside in expected directories per project conventions (e.g., src/, lib/, scripts/). Flag files at wrong levels.
    • Dead directories: Identify empty or orphaned directories with no purpose.
    • Naming conventions: Verify file and directory names follow project conventions (case, separators).
    • Config files: Ensure project config files (deno.json, package.json, etc.) are at expected locations.
  3. Phase 2: Code Hygiene & Dependencies

    • Dead Code: Identify exported/public symbols in source directories that are never imported/called elsewhere.
    • Unused Imports: Scan source files for imports/includes that are not used in the file body.
    • Test Quality: Read test files (e.g., *.test.*, *_test.*, test_*.py). Flag tests that:
      • Have no assertions.
      • Use trivial assertions (e.g., expect(true).toBe(true), assert True).
      • Are commented out.
  4. Phase 3: Complexity & Hotspots

    • Files: Flag any source file exceeding 500 lines.
    • Functions: Scan for functions/methods exceeding 50 lines.
    • God Objects: Identify classes/modules with mixed concerns (e.g., logic + UI + database in one file).
  5. Phase 4: Technical Debt Aggregation

    • Scan: Search for TODO, FIXME, HACK, XXX tags in the codebase.
    • Group: Organize by file/module.
    • Analysis: Flag any that look critical or like "temporary" fixes that became permanent.
  6. Phase 5: Consistency (Docs vs. Code)

    • Terminology: Extract key terms from README.md and documents/. Check if code uses different synonyms (e.g., "User" in docs vs "Customer" in code).
    • Drift: Pick 3 major claims from documents/*.md (e.g., "The system handles X asynchronously"). Verify if the code actually does that.
  7. Phase 6: Code Documentation Coverage

    • Rule: Every file, class, method, and exported function MUST have documentation (JSDoc, Docstring, Rustdoc, etc.).
    • Check:
      • Responsibility: Does the comment explain what it does?
      • Nuances: For complex logic (cyclomatic complexity > 5 or > 20 lines), are there examples or edge case warnings?
    • Scan: primary source directories.
    • Report: List undocumented symbols.
  8. Phase 7: Reporting

    • Compile all findings into whiteboard with the following format:
      markdown
      1# Maintenance Report (YYYY-MM-DD) 2 3## 1. Structural Issues 4 5- [ ] File X is in root but should be in Y. (Fix: Move file) 6 7## 2. Hygiene & Quality 8 9- [ ] Unused export `myFunc` in `utils.*`. (Fix: Delete) 10- [ ] `main.*` is 550 lines. (Fix: Extract `processLogic` to new file) 11 12## 3. Technical Debt 13 14- [ ] 5 TODOs in `api.*` regarding error handling. 15 16## 4. Consistency 17 18- [ ] Docs say "User", code says "Client". (Fix: Standardize on User) 19 20## 5. Documentation Coverage 21 22- [ ] `utils.*` - function `parseData` missing docs. (Fix: Add docs) 23- [ ] `ComplexClass` missing usage example. (Fix: Add example)

</step_by_step>

Verification

<verification> [ ] Checked structural integrity (file placement, naming, configs). [ ] Scanned for dead code and unused imports. [ ] Checked file/function length limits (500/50 lines). [ ] Aggregated all TODO/FIXME tags. [ ] Verified documentation terminology vs code usage. [ ] Checked for missing code documentation (File/Class/Method). [ ] Saved structured report to whiteboard. </verification>

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