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Found 279 Skills
Load PROACTIVELY when task involves building a complete feature across multiple layers. Use when user says "build a feature", "add user profiles", "create a dashboard", or any request spanning database, API, UI, and tests. Orchestrates multi-agent work sequentially: schema and migrations, API endpoints, UI components, tests, and review. Handles dependency ordering and cross-layer type sharing.
Web research, content extraction, and deep analysis. Multi-source parallel search with extended thinking. Supports Fabric pattern selection (242+ prompts). USE WHEN: "research X", "extract wisdom from", "analyze this content", "find info about".
Review the current session for errors, issues, snags, and hard-won knowledge, then update the rules/ files (or AGENTS.md if no suitable rule file exists) with actionable learnings.
Agent testing methodology - run agents with test inputs, observe outputs, iterate until outputs are accurate and well-structured.
Orchestrator workflow for running ZeroContext Lab (ZCL) attempts/suites with deterministic artifacts, trace-backed evidence, and fast post-mortems (shim support for "agent only types tool name").
Execute complete FPF cycle from hypothesis generation to decision
FORGE + Agent Teams — Exploits Agent Teams for true parallel execution of FORGE agents. 3 patterns: pipeline (full pipeline with parallel stories), party (multi-agent debate), build (parallel story development). Requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1. Usage: /forge-team pipeline "objective" | /forge-team party "topic" | /forge-team build [STORY-IDs]
Execute a single Ralph iteration - implement one user story autonomously. Use for manual mode where you want maximum control and fresh context per story. Triggers on: ralph iterate, execute one story, run single iteration, manual ralph.
Proposal-first development workflow with commit hygiene and decision authority rules. Enforces: propose before modifying, atomic commits, no force flags, warnings-as-errors. Use for any project where AI agents are primary developers and need guardrails.
Unified requirement clarification to prevent downstream implementation churn by resolving ambiguity early. Default: research-first with autonomous decision-making and persistent questioning. --light: direct iterative Q&A. Triggers: "cwf:clarify", "clarify this", "refine requirements"
Apply plugin knowledge base updates to an existing generated system. Consults the Ars Contexta research graph for methodology improvements, proposes skill upgrades with research justification. Never auto-implements. Triggers on "/upgrade", "upgrade skills", "check for improvements", "update methodology".
Generate structured prd.json files for autonomous agent loops (Ralph Wiggum pattern). Use when planning bulk/batch tasks, migrations, refactoring campaigns, or any work that can be decomposed into independent items with verification steps.