curiositech/windags-skills

최근 기록된 소스 활동: 2026년 10월 19일 04:57

SkillsMP 카탈로그 업데이트: 2026년 10월 29일 18:30

수집된 skills: 589

GitHub 스타: 16
GitHub 포크: 2

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Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/curiositech/windags-skills.

저장소 README

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<p align="center"> <img src="docs/screenshots/next-move-hero.png" alt="/next-move prediction in Claude Code" width="700" /> <br/><br/> <img src="docs/screenshots/next-move-hero-2.png" alt="/next-move execution results" width="700" /> </p> <h1 align="center">windags-skills</h1> <p align="center"> <strong>463+ agent skills</strong> for Claude Code, Codex, Gemini CLI, Cursor, and 40+ other coding agents.<br/> Built by <a href="https://windags.ai">WinDAGs</a> — DAG orchestration for multi-agent workflows. </p> <p align="center"> <a href="#next-move">next-move</a> · <a href="#skill-search">skill search</a> · <a href="#all-categories">all skills</a> · <a href="#install">install</a> · <a href="https://windags.ai">windags.ai</a> </p> --- ## /next-move The flagship skill. Tell it what you're working on (or don't — it reads your git state), and it produces a **predicted DAG** of the highest-impact agents to run next. ``` /next-move # auto-detect from git + conversation /next-move fix the flaky auth tests # tell it what you care about ``` <img src="docs/screenshots/next-move-prediction.png" alt="/next-move prediction output" width="700" /> **What you get:** - Problem classification — well-structured, ill-structured, or wicked - 3-8 subtasks decomposed into parallel waves - Each subtask matched to the best skill from 463+ via BM25 retrieval - Risk analysis with mitigations (PreMortem agent) - Time and cost estimates per node - Accept / Modify / Reject feedback loop **How it works:** A 5-agent meta-DAG runs inside your Claude Code session: ``` Wave 0: Sensemaker — classify the problem, halt gate if uncertain Wave 1: Decomposer — break into subtasks with dependencies Wave 2: Skill Selector — BM25 search → match skills to subtasks (parallel) Wave 2: PreMortem — scan for failure patterns (parallel) Wave 3: Synthesizer — assemble the predicted DAG + present to user ``` No extra API keys. No external services. Runs entirely in your Claude session. --- ## Skill Search BM25 ranked retrieval across the full catalog. Porter stemming, real stopword removal, document-length normalization. Zero API keys — runs locally. <img src="docs/screenshots/skill-search-bm25.png" alt="BM25 skill search" width="700" /> Queries like "drone computer vision" find `computer-vision-pipeline` and `drone-cv-expert`. "Wedding photo 3d gaussian" finds `wedding-immortalist` at 35.9. Stemming handles morphological variants — "deploy" matches "deployment" and "deployed". --- ## DAG Execution Skills don't just get recommended — they get **executed** in parallel waves. The WinDAGs engine spawns real agents (`claude -p`) with skill-injected prompts, running independent nodes concurrently. <img src="docs/screenshots/dag-execution.png" alt="DAG execution" width="700" /> --- <h2 id="install">Install</h2> **Claude Code** (plugin): ```bash claude plugin add /path/to/windags-skills ``` **Manual** (any agent): ```bash git clone https://github.com/curiositech/windags-skills.git # Use /next-move: cp -r windags-skills/skills/next-move ~/.claude/commands/ # Or symlink the whole catalog: ln -s $(pwd)/windags-skills/skills ~/.claude/skills ``` **Single skill:** ```bash cp -r windags-skills/skills/api-architect ~/.claude/commands/ ``` --- <h2 id="all-categories">All Skills (463)</h2> | Category | Count | Highlights | |----------|------:|-----------| | **Agent & Orchestration** | 78 | dag-orchestrator, next-move, skill-architect, task-decomposer | | **Research & Academic** | 61 | raft-consensus, bdi-agents, chain-of-thought, tree-of-thoughts | | **Design & Creative** | 46 | design-system-creator, pixel-art, typography, vibe-matcher | | **Backend & Infrastructure** | 35 | api-architect, microservices, caching, websocket | | **Cognitive Science** | 29 | naturalistic-decision-making, sensemaking, expertise-elicitation | | **AI & Machine Learning** | 27 | ai-engineer, RAG, computer-vision, embeddings | | **DevOps & Infrastructure** | 26 | github-actions, kubernetes, terraform, docker | | **Frontend & UI** | 24 | nextjs, react-performance, animation, framer-motion | | **Data & Analytics** | 17 | data-pipeline, dbt, data-viz, dimensional-modeling | | **Mobile Development** | 17 | ios, react-native, flutter, swiftui | | **Code Quality & Testing** | 16 | vitest, playwright, code-review, refactoring | | **Productivity & Meta** | 16 | prompt-engineer, documentation, skill-creator | | **Recovery & Wellness** | 16 | sobriety, crisis-intervention, speech-pathology | | **Lifestyle & Personal** | 14 | ADHD, grief, finance, interior-design | | **Content & Marketing** | 14 | SEO, copywriting, product-launches | | **Career & Interview** | 9 | interview-prep, resume, hiring-manager | | **Legal & Compliance** | 7 | expungement, HIPAA, legal-tech | | **Video & Audio** | 6 | video-production, TTS, sound-design | | **Security** | 5 | auth, vulnerability-scanning, zero-trust | --- ## Academic Formatting - [APA formatting](skills/apa-formatting/SKILL.md): APA 7 student and professional papers, author–date citations, and References. - [MLA formatting](skills/mla-formatting/SKILL.md): MLA 9 papers, author–page citations, and Works Cited. - [Product palette designer](skills/product-palette-designer/SKILL.md): bespoke OKLCH ramps, light/dark semantic tokens, Tailwind/shadcn/native exports, and coordinated brand assets. Both skills include source guidance, worked examples, and document scaffolds. They flag missing bibliographic details and distinguish text checks from rendered document checks. ## How Skills Work Each skill is a markdown file (`SKILL.md`) with YAML frontmatter: ```yaml --- name: api-architect description: "Expert API designer for REST, GraphQL, gRPC architectures" category: Backend & Infrastructure tags: [api, rest, graphql, grpc] --- ``` The body contains activation triggers, core capabilities, anti-patterns, and reference files. When an agent loads a skill, it gains bespoke expertise — not just instructions, but deep domain knowledge with working examples. Skills follow the [Agent Skills](https://agentskills.io) open standard and work with Claude Code, OpenAI Codex CLI, Gemini CLI, Cursor, VS Code Copilot, and 30+ other agents. --- ## Install ### Claude Code (one command) ```bash claude plugin marketplace add curiositech/windags-skills claude plugin install windags-skills ``` This installs all 463+ skills + the 5 `/next-move` subagents (sensemaker, decomposer, skill-selector, premortem, synthesizer). ### Cross-tool install (Codex, Gemini CLI, Cursor, Aider, …) Clone the plugin and run the installer. It detects which tools you have under `$HOME` and links skills + subagents into the right place. No hardcoded paths — uses `$HOME` and the plugin checkout dir. ```bash git clone https://github.com/curiositech/windags-skills.git ~/coding/windags-skills ~/coding/windags-skills/scripts/install.sh ``` What it does (idempotent — safe to re-run after `git pull`): | Tool | What gets installed | |---|---| | Claude Code | `~/.claude/skills/{next-move, windags-*}` + `~/.claude/agents/{5 subagents}.md` | | Codex | `~/.codex/skills/{next-move, windags-*}` + `~/.codex/AGENTS.md` | | Cursor / Cline / Aider | `~/AGENTS.md` (auto-discovered by AGENTS.md-aware tools) | | Gemini CLI | Marked block appended to `~/.gemini/GEMINI.md` | Override the install location with `WINDAGS_HOME=/path ./install.sh`. Preview without changes with `--dry-run`. The MCP server lives at `<plugin>/mcp-server/index.js` — wire it into any MCP-aware client. It exposes 10 tools and 1 prompt: | Tool | Purpose | |---|---| | `windags_skill_search` | Cascade-ranked skill search (BM25 + MiniLM + RRF + cross-encoder + per-user attribution k-NN) | | `windags_skill_graft` | Full SKILL.md bodies for top primaries + adjacencies + asset manifest | | `windags_skill_reference` | Load one reference file from a skill's `references/` | | `windags_history` | Recent `/next-move` predictions for a project | | `windags_skill_search_batch` | N searches in one round-trip (capped at 20) | | `windags_skill_graft_batch` | N grafts in one round-trip (capped at 20) | | `windags_node_requirements` | Per-skill `allowed-tools`, `pairs-with`, suggested `model_tier`, and **provider-native** model IDs | | `windags_validate_dag` | Schema-check a candidate DAG before saving | | `windags_estimate_cost` | Per-node + total cost estimate during planning | | `windags_run_pipeline` | Run the full 5-stage `/next-move` pipeline server-side. Provider-agnostic — auto-detects from env (Anthropic / OpenAI / Google / Groq / OpenRouter / Together / DeepSeek / Fireworks / Cerebras / xAI). Pin one with `WINDAGS_PROVIDER=<id>`. ~$0.02–$0.15 per run. Returns a halt response or a validated `PredictedDAG`. | | Prompt | Purpose | |---|---| | `next_move` | Templated `/next-move` runner. Drives the 5-stage pipeline (sensemaker → decomposer → skill-selector + premortem → synthesizer) for non-Claude clients that don't have Claude Code's slash commands. Args: `task` (focus hint), `fresh` (`"true"` to ignore conversation history). | --- ## License **BUSL-1.1** (Business Source License). Free for non-commercial and personal use. Converts to Apache 2.0 on **2030-03-03**. Commercial licensing: [licensing@curiositech.ai](mailto:licensing@curiositech.ai) --- <p align="center"> Built by <a href="https://curiositech.ai">Curiositech</a> · <a href="https://windags.ai">windags.ai</a> </p>

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소프트웨어 개발자 데이터 과학자 네트워크·컴퓨터 시스템 관리자 웹 개발자

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Framework for deliberate problem-solving with LLMs through exploration of branching reasoning paths

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2026-10-14

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