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Manages feature implementation task state via SAM MCP tools. Use when querying task status, listing ready tasks, dispatching tasks for execution, updating task timestamps, or coordinating multi-task feature rollout. Activated by the /dh:execution orchestrator to track progress — also activates directly when managing tasks.
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Run a high-thoroughness end-to-end implementation loop using clean worker worktrees, per-slice planning, implementation, explicit review/fix iteration, orchestrator integration, and final commit/PR drafting. Use when the user explicitly wants a Ralph/Wiggum-style loop, autonomous multi-agent execution, a delegated feature/bugfix carried from confirmed scope through review-ready completion, or RFC-driven implementation that should be reviewed, bumped to In Progress, decomposed into phases, and executed through nested sub-loops. Supports Codex subagents by default and external CLI workers such as OpenCode when explicitly requested and available.
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This skill should be used when the user wants to "create a skill", "add a skill to plugin", "write a new skill", "improve skill description", "organize skill content", or needs guidance on skill structure, progressive disclosure, or skill development best practices for Claude Code plugins.
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Pattern for progressively refining context retrieval to solve the subagent context problem
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Track and measure agentic coding KPIs for ZTE progression. Use when measuring workflow effectiveness, tracking Size/Attempts/Streak/Presence metrics, or assessing readiness for autonomous operation.
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Assess agentic layer maturity using the 12-grade classification system (Class 1-3). Use when evaluating codebase readiness, identifying next upgrade steps, or tracking progress toward the Codebase Singularity.
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Create and manage AI agent skills following best practices. Use when creating new skills, optimizing context, designing multi-agent systems, or implementing progressive disclosure patterns.
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Orchestrates multi-agent workflows by delegating ALL tasks to spawned subagents via /spawn command. Parallelizes independent work, supervises execution, tracks progress in UUID-based output directories, and generates summary reports. Never executes tasks directly. Triggers on keywords: orchestrate, manage agents, spawn agents, parallel tasks, coordinate agents, multi-agent, orc, delegate tasks
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Generate markdown reports from issues. Filter by type, priority, epic, date range. Supports summary, detailed, progress, completion, velocity, and backlog analysis views.
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Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.
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Git Context Controller (GCC) v2 — Lean agent memory backed by real git. Stores hash + intent + optional decision notes instead of verbose markdown. Auto-bridges to aiyoucli vector memory when available. Dual mode: git-backed (lean index.yaml) or standalone (markdown fallback). Triggers on /gcc commands or natural language like 'commit this progress', 'branch to try an alternative', 'merge results', 'recover context'.
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Run the pilot-facing Week Close protocol with progress review and carry-over.
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Post a sitrep (situation report) for a live incident, in its incident channel: a short fixed-layout update for people who haven't read the whole channel — the current picture and what moved since the previous update. Use on "sitrep", "status update", "where are we", "what's the latest", "catch me up", "summarize the incident so far", when asked to keep posting on a schedule ("post a sitrep every hour until resolved"), or when the oncall memory sets an update cadence for this severity and the incident owner asks Claude to keep it. Reads the channel, alert thread and findings posted, re-reads the one key signal, and writes: status line, TL;DR, impact, what changed since the last sitrep, what's in progress and who has it, what's needed, when the next update comes, one chart. Facts come only from what people said or Claude read first-hand; cause and recovery time are never guessed. Fits one phone screen. Not investigation (`incident-investigate`) or the write-up afterwards (`incident-postmortem`).
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Read and manage Jira Cloud issues, projects, boards, sprints, comments, and transitions. Use this whenever the user wants to search issues with JQL, create or update a ticket, transition an issue (move to In Progress / Done), add a comment, check a sprint or board, look up a project, or ask "what's in my Jira queue" — even if they don't say "API". Also use it for any *.atlassian.net URL, an issue key like "PROJ-123", or a JQL string. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.
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Execute Anarlog work immediately while recording issues, decisions, progress, and lessons in Linear. Use for Anarlog repository or Anarlog desktop, web, mobile, and API work, including related worktrees and ANLG issues. Explicit brainstorming stays discussion-first. Do not use for unrelated repositories or meeting-data queries.
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Plan and execute your second and third contributions to become a regular contributor. Covers relationship building, progressive complexity escalation, and community presence. Use when your first PR was merged and you want to keep contributing, when transitioning from one-off contributor to regular, or when planning a sustained OSS involvement. Not for your first contribution — use the oss-find-issue → oss-contribute chain for that.
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Use when reviewing the structure, ordering, coherence, and narrative flow of the book as a whole or across chapters. Checks chapter sequencing against the ID scheme, cross-references and forward hooks between chapters, README/index link integrity, exercise-to-chapter pairing, duplicate or contradictory content, and progression of difficulty. Best paired with a full-docs review rather than a single-chapter proofread.
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Run the full content pipeline in one command — brief, write, gate, log. Progress updates between steps. 5-minute timeout with partial state recovery.
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Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.
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Create a GitHub issue, assign to current user, and set status to In Progress
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Use when the user asks about evolution progress, current scores, best version, how many iterations ran, or whether the loop is stagnating.
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Session wrap-up: consolidates every descriptive markdown file (README.md, AGENTS.md, HANDOFF.md, docs/*) into ONE canonical CLAUDE.md, then reconciles it against the current code state so a fresh session or new teammate can pick up immediately. CLAUDE.md keeps a mandatory PROGRESS block at the top answering two questions: where are we now and what's the next concrete action. Trigger when the user says: "wrap", "wrap up", "/wrap", "tidy docs", "sync docs", "update docs", "archive", "this phase is done", or the Chinese equivalents "收尾", "同步文档", "整理文档", "整理一下", "更新文档", "存档", "梳理一下", "这阶段做完了". A bare "tidy" / "整理" with prior dev context counts. Do not under-trigger.
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Compare browser-agent reliability on a repeatable task and anti-bot suite before choosing a stack or claiming progress.
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Monitors ArgoCD application sync status via the ArgoCD REST API and argocd CLI. Detects OutOfSync conditions, tracks sync wave progress, and alerts on failed sync operations with detailed resource diff analysis using argocd app diff.
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