* fix(providers): remove ~anthropic, skip ~ prefixes in sync script
OpenRouter uses ~ prefixes for internal auto-routing aliases (e.g. ~anthropic).
These are not real providers — they already route through openrouter.toml.
The generated ~anthropic.toml was confusing (looked like a stale backup)
and redundant with the existing openrouter provider.
- Delete providers/~anthropic.toml
- Skip provider IDs starting with ~ in sync-pricing.py --create-missing
* fix(providers): remove morph, aider, kwaipilot
- morph: specialized code-editing/patching tool, not a general LLM provider
- aider: CLI meta-tool wrapper (base_url empty), redundant with claude-code/codex-cli/gemini-cli/qwen-code
- kwaipilot: Kwai internal coding assistant routed via OpenRouter, niche
* fix(sync): add morph/aider/kwaipilot to SKIP_PROVIDERS to prevent re-creation
* feat(sync): merge OpenRouter-only providers into openrouter.toml
Instead of generating standalone .toml files that just wrap the OpenRouter
endpoint, merge their models directly into openrouter.toml with the
standard 'openrouter/{provider}/{model}' ID convention.
- Add _build_model_fields() and _model_lines() helpers to deduplicate
model rendering between standalone and merged paths
- Add merge_into_openrouter() that appends new models idempotently
- generate_provider_toml() now only runs for providers in PROVIDER_API
- --create-missing routes OpenRouter-only providers to merge_into_openrouter
* fix(providers): remove 14 OpenRouter-only standalone files
These providers have no direct public API and all route through
openrouter.ai/api/v1. Per the new sync-pricing.py policy, their models
will be merged into openrouter.toml on the next CI run instead of
living in separate files that just wrap the OpenRouter endpoint.
Removed: allenai, deepcogito, essentialai, inclusionai, inflection,
liquid, meituan, nex-agi, nousresearch, prime-intellect, relace,
switchpoint, tngtech, writer
* fix(providers): remove 7 niche providers with no driver support
No dedicated LLM driver code exists for these providers — they rely
purely on OpenAI-compatible passthrough with no special handling.
Removing them reduces registry noise; users can still reach them via
openrouter.toml if needed.
Removed: microsoft, ibm-granite, xiaomi, upstage, inception, aion-labs, arcee-ai
* fix(providers): remove ai21, chutes, venice
All three use ApiFormat::OpenAI with no special handling — pure passthrough.
No registry entry needed; users can reach them via openrouter.toml or by
adding a custom provider.
* docs(providers): rewrite README with full provider catalog and inclusion criteria
- List all 46 providers grouped by category with descriptions
- Document why each provider exists (direct API, unique endpoint, dedicated driver, local, CLI)
- Add inclusion criteria section explaining when to create standalone files vs merging into openrouter.toml
- Document sync script routing logic
- Update model counts: 49→46 providers, 339→232 models
* docs: add comprehensive READMEs for all registry sections + deepinfra provider
- agents/README.md: 32 agents across 7 categories with capability field reference
- channels/README.md: 44 channels across 5 categories with protocol reference table
- hands/README.md: 18 hands across 5 categories with HAND.toml format guide
- mcp/README.md: 33 MCP servers across 5 categories with transport/auth format
- plugins/README.md: 12 plugins with hook protocol documentation
- skills/README.md: 60 skills across 9 categories with SKILL.md format guide
- providers/deepinfra.toml: add DeepInfra serverless inference (5 models)
198 lines
9.0 KiB
Markdown
198 lines
9.0 KiB
Markdown
# Hands Registry
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Hands are pre-packaged capability bundles that compose agents, tools, skills, MCP servers, and plugins into a working application. Installing a hand gives you a complete, ready-to-use workflow — not just a single agent.
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> "You have many hands helping you."
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A hand can contain one agent (single-agent) or multiple coordinated agents (multi-agent). Each agent in a multi-agent hand can have its own role-specific skills, model config, and capability restrictions.
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## File Format
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Each hand lives in its own subdirectory:
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```
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hands/
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├── researcher/
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│ ├── HAND.toml # required: hand definition
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│ └── SKILL.md # optional: shared reference knowledge for all agents
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├── devteam/
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│ ├── HAND.toml
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│ ├── SKILL-pm.md # optional: role-specific knowledge for PM agent
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│ ├── SKILL-engineer.md # optional: role-specific knowledge for Engineer agent
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│ └── SKILL-qa.md # optional: role-specific knowledge for QA agent
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```
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### HAND.toml format
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```toml
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id = "researcher"
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version = "1.1.1"
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name = "Researcher Hand"
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description = "Autonomous deep researcher — exhaustive investigation, cross-referencing, fact-checking, and structured reports"
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category = "productivity" # productivity | development | data | content | communication
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icon = "lucide:flask-conical"
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# Tools available to all agents in this hand
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tools = [
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"shell_exec", "file_read", "file_write", "web_fetch", "web_search",
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"memory_store", "memory_recall", "knowledge_query", "event_publish",
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]
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# MCP servers all agents can use
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mcp_servers = ["github"]
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# Skills allowlist (empty = all available)
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skills = []
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# Plugin allowlist
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allowed_plugins = ["todo-tracker", "auto-summarizer"]
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# ─── Routing ──────────────────────────────────────────────────────────────────
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[routing]
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aliases = ["deep research", "investigate", "fact check"] # exact activation phrases
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weak_aliases = ["research", "look into"] # keyword hints
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# ─── Configurable settings ────────────────────────────────────────────────────
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[[settings]]
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key = "research_depth"
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label = "Research Depth"
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description = "How exhaustive each investigation should be"
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setting_type = "select" # select | toggle | text
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default = "thorough"
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[[settings.options]]
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value = "quick"
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label = "Quick (5-10 sources, 1 pass)"
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[[settings.options]]
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value = "thorough"
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label = "Thorough (20-30 sources, cross-referenced)"
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# ─── Single-agent definition ──────────────────────────────────────────────────
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[agent]
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name = "researcher"
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base = "researcher" # inherits from agents/researcher/agent.toml
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[agent.model]
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system_prompt = """Custom prompt override..."""
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# ─── Multi-agent definition (alternative to [agent]) ─────────────────────────
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[agents.pm]
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coordinator = true
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base = "planner" # inherits from agents/planner/agent.toml
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invoke_hint = "Task coordination and issue triage"
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[agents.engineer]
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base = "coder"
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invoke_hint = "Implementation"
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[agents.qa]
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base = "test-engineer"
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invoke_hint = "Quality assurance and validation"
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# ─── Dashboard metrics ────────────────────────────────────────────────────────
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[dashboard]
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[[dashboard.metrics]]
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label = "Reports Written"
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memory_key = "metric_reports_written"
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format = "number"
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# ─── i18n ─────────────────────────────────────────────────────────────────────
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[i18n.zh]
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name = "研究员"
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description = "自主深度研究员 — 详尽调查、交叉核实、事实核查与结构化报告"
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```
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## Installing and Using Hands
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```bash
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# List all available hands
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librefang catalog hands
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# Install a hand
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librefang hand install researcher
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# Install with a specific agent name
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librefang hand install researcher --name my-researcher
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# List installed hands
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librefang hand list
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# Remove a hand
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librefang hand remove my-researcher
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```
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## All Hands (18 total)
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### Productivity
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| ID | Name | Category | Description |
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|----|------|----------|-------------|
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| researcher | Researcher Hand | productivity | Autonomous deep researcher — exhaustive investigation, cross-referencing, fact-checking, and structured reports |
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| strategist | Strategist Hand | productivity | Autonomous strategy analyst — market research, competitive analysis, business planning, and strategic recommendations |
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| wiki | Wiki Hand | productivity | LLM-maintained personal knowledge base — builds an Obsidian-compatible wiki from raw sources with provenance tracking |
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| browser | Browser Hand | productivity | Autonomous web browser — navigates sites, fills forms, clicks buttons, and completes multi-step web tasks |
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### Development
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| ID | Name | Category | Description |
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|----|------|----------|-------------|
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| devteam | Dev Team | development | Autonomous software development team — PM triages issues, Engineer implements, QA validates |
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| devops | DevOps Hand | development | Autonomous DevOps engineer — CI/CD management, infrastructure monitoring, deployment automation, and incident response |
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| apitester | API Tester Hand | development | Autonomous API testing agent — endpoint discovery, request validation, load testing, and regression detection |
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### Data
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| ID | Name | Category | Description |
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|----|------|----------|-------------|
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| analytics | Analytics Hand | data | Autonomous data analytics agent — data collection, analysis, visualization, dashboards, and automated reporting |
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| collector | Collector Hand | data | Autonomous intelligence collector — monitors any target continuously with change detection and knowledge graphs |
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| lead | Lead Hand | data | Autonomous lead generation — discovers, enriches, and delivers qualified leads on a schedule |
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| predictor | Predictor Hand | data | Autonomous future predictor — collects signals, builds reasoning chains, makes calibrated predictions, and tracks accuracy |
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| trader | Trading Hand | data | Autonomous market intelligence and trading engine — multi-signal analysis, adversarial bull/bear reasoning, and strict risk management |
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### Content
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| ID | Name | Category | Description |
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|----|------|----------|-------------|
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| clip | Clip Hand | content | Turns long-form video into viral short clips with captions and thumbnails |
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| creator | Creator Hand | content | AI media studio — generates images, videos, music, and speech from text prompts |
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### Communication
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| ID | Name | Category | Description |
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|----|------|----------|-------------|
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| linkedin | LinkedIn Hand | communication | Autonomous LinkedIn manager — profile optimization, content creation, networking, and professional engagement |
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| reddit | Reddit Hand | communication | Autonomous Reddit manager — monitors subreddits, posts content, replies to threads, and tracks engagement |
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| twitter | Twitter Hand | communication | Autonomous Twitter/X manager — content creation, scheduled posting, engagement, and performance tracking |
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### Data (additional)
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| ID | Name | Category | Description |
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|----|------|----------|-------------|
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| clip | Clip Hand | content | Turns long-form video into viral short clips with captions and thumbnails |
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## Resource Composition Summary
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| Resource | How to compose | Notes |
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|----------|----------------|-------|
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| Agent templates | `base = "coder"` on `[agents.*]` | Inherits prompt, model config, fallbacks from `agents/coder/agent.toml` |
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| Tools | `tools = [...]` at hand level | All agents in the hand share these built-in tools |
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| Skills | `skills = [...]` at hand level | Empty list means all available skills are allowed |
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| MCP servers | `mcp_servers = [...]` at hand level | Agent interacts via MCP tools, not hardcoded API calls |
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| Plugins | `allowed_plugins = [...]` at hand level | Empty list means all installed plugins are allowed |
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| Per-agent knowledge | `SKILL-{role}.md` files | Different reference prompts per agent role |
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| Per-agent capabilities | `[agents.*.capabilities]` | Fine-grained shell / network / memory per agent |
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## Adding a New Hand
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1. Create `hands/<name>/HAND.toml` with at least `id`, `name`, `description`, and `category`.
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2. Add `SKILL.md` (shared) or `SKILL-{role}.md` (per-agent) files for reference knowledge.
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3. Use `base = "agent-name"` in each `[agents.*]` block to inherit from existing agent templates.
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4. Specify `mcp_servers`, `skills`, and `allowed_plugins` for resource composition.
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5. Ensure `id` matches the directory name.
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6. Run `python scripts/validate.py`.
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7. Submit a PR.
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See [CONTRIBUTING.md](../CONTRIBUTING.md) for the full guide.
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