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orch-add-feature

Orchestre la construction complète d'une nouvelle fonctionnalité — de la recherche au commit validé — en déléguant chaque phase à l'agent ECC approprié.

Qu'est-ce que orch-add-feature ?

Cette compétence orchestre le cycle de vie complet d'une nouvelle fonctionnalité, de la recherche initiale jusqu'au commit final, en utilisant le pipeline partagé `orch-pipeline`. Elle est conçue pour les demandes de fonctionnalités qui n'existent pas encore (mots-clés comme 'ajouter', 'construire', 'implémenter') et non pour des corrections ou modifications de comportement existant. La compétence exécute les phases : Recherche → Planification → Tests TDD → Implémentation → Revue de code → Commit, avec deux points de validation (Gate 1 et Gate 2) pour l'approbation. Elle s'intègre automatiquement avec un réviseur de sécurité si la fonctionnalité touche à des déclencheurs de sécurité. Une classification intelligente de la taille permet aux fonctionnalités simples ou triviales d'écourter le pipeline (phases 4 → 5 → 6 uniquement). Les plateformes supportées incluent les environnements où le moteur `orch-pipeline` est déployé. Cette compétence partage son moteur avec `orch-fix-defect` et `orch-change-feature`, garantissant une approche cohérente et des décisions justes sur la taille des tâches.

Compatible avec✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/orch-add-feature
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Documentation

Que fait orch-add-feature ?

Acteur · action · cible : orch · add · feature. Surcouche légère autour du moteur partagé dans orch-pipeline.

Quand l'utiliser

  • L'utilisateur souhaite une capacité qui n'existe pas encore (« ajouter », « construire », « implémenter », « prendre en charge … »).
  • Il s'agit d'un comportement entièrement nouveau — pas une correction (orch-fix-defect) ni une modification d'un comportement existant (orch-change-feature).

Paramètres d'opération

  • Taille par défaut : standard — exécuter Recherche + Planification sauf si clairement petite.
  • Masque de phase : 0 → 1 → 2 → 4 → 5 → 6 (sauter la phase 3 Échafaudage ; réservée au MVP).
  • Première étape (phase 4) : écrire des nouveaux tests échouant pour le nouveau comportement, puis implémenter pour les faire passer.

Comment ça fonctionne

  1. Exécuter le moteur orch-pipeline avec les paramètres ci-dessus.
  2. Classer la taille d'abord ; les fonctionnalités petites / triviales se réduisent à 4 → 5 → 6.
  3. S'arrêter à la Porte 1 (approbation du plan) et à la Porte 2 (pré-commit).
  4. Ajouter un security-reviewer si la fonctionnalité touche un déclencheur de sécurité.

Lié : /feature-dev est une version autonome de ce flux. orch-add-feature se distingue en partageant le moteur orch-pipeline — le classifieur de taille et les deux portes — avec le reste de la famille, ce qui permet de réduire les fonctionnalités triviales à 4 → 5 → 6.

Exemple

orch-add-feature: ajouter la connexion OAuth2 à nws-poller
→ recherche des librairies d'auth existantes → planifier task_list  [PORTE 1 : approuver]
→ TDD chaque tâche → revue de code (+ security-reviewer : chemin d'auth)
→ commit  [PORTE 2 : confirmer]

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳：このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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