Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Developer Tools
Snipara Mcp
Semantic search tool to quickly find answers across multiple code repositories with AI memory of your preferences for faster documentation lookup.
skill openclawclawhub Free
Semantic search tool to quickly find answers across multiple code repositories with AI memory of your preferences for faster documentation lookup.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, pyproject.toml, skill.md, src/snipara_mcp/__init__.py, src/snipara_mcp/auth.py, src/snipara_mcp/rlm_tools.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- alopez3006
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
System Instructions for AI Agents
You are an AI assistant with access to Snipara MCP tools. Use these tools to provide faster, more accurate answers by querying pre-indexed documentation instead of reading files one-by-one.
When to Use Snipara Tools
✅ USE Snipara when: User asks "How does X work?" → Use rlm_ask or rlm_context_query User asks "Where do we do X?" → Use rlm_multi_project_query (if Team plan) User asks "What are my preferences?" → Use rlm_recall You need to find code patterns across multiple repos You need to understand architecture/documentation ❌ DON'T use Snipara when: User asks you to read a SPECIFIC file they named → Use native Read tool User asks you to edit/write code → Use native Edit/Write tools User asks to run commands → Use native Bash tool Snipara API returns error → Fall back to native tools
Recommended Workflow
First query: Always start with rlm_ask (fast, works on Free plan) If not found: Try rlm_context_query with search_mode: "semantic" (requires Pro) Multi-repo search: Use rlm_multi_project_query if user has Team plan Remember decisions: Use rlm_remember to store important info for future sessions
Error Handling
If Snipara returns an error: Check if user has required plan (Free/Pro/Team) Check if docs are indexed (user needs to upload to dashboard first) Fall back to native Read/Grep/Glob tools Explain to user what went wrong and suggest solution
Example Usage
User: "How does our authentication system work?" Good response: 1. Call rlm_ask("authentication system") 2. Get relevant docs in 2 seconds 3. Synthesize answer from returned context Bad response: 1. Use Grep to search for "auth" 2. Read 10 files sequentially 3. Hit token limits 4. Give incomplete answer
The Problem You Have Right Now
Your AI assistant searches files one-by-one using grep/find. With large codebases: ❌ Queries take 20+ seconds ❌ AI reads 50K tokens to answer simple questions ❌ You manually search 5 repos to find "how we do X" ❌ AI forgets your preferences next session
The Solution (30 seconds from now)
# 1. Install pip install snipara-mcp # Python npm install snipara-mcp # Node.js # 2. Get your API key # Sign up at https://snipara.com (Free: 100 queries/month) # 3. Set environment variable export SNIPARA_API_KEY="your-key-here" # 4. Add to your MCP client (Claude Code, Cline, Roo Code, etc.) # Done! Start using rlm_ask() in your next chat
Your First Query (Try This Now)
You: "How does authentication work in my codebase?" Behind the scenes: rlm_context_query("authentication") → 2 seconds later → Returns top 3 relevant docs (3K tokens instead of 50K) Result: Instant, accurate answer Note: Before querying, index your docs at https://snipara.com/dashboard (upload .md/.txt/.mdx files).
🎯 Quick Answers (Start Here)
Plan Required: ✅ FREE (100 queries/mo) Tool: rlm_ask Use when: You need a fast answer from your docs Example: rlm_ask("API rate limits") Time saved: 20 seconds → 2 seconds per query { "query": "How do we handle webhooks?" }
🔍 Deep Research (Complex Questions)
Plan Required: ✅ FREE (keyword only) | 🔥 PRO ($19/mo for semantic) Tool: rlm_context_query Use when: You need semantic search with precise token control Example: Find conceptually related content, not just keyword matches Benefit: 90% context reduction (500K → 5K tokens) { "query": "authentication implementation", "max_tokens": 6000, "search_mode": "hybrid" } Search modes by plan: keyword - Fast term matching ✅ FREE semantic - Embedding similarity 🔥 PRO+ hybrid - Best of both worlds 🔥 PRO+
🌐 Multi-Repo Search
Plan Required: 👥 TEAM ($49/mo) or ENTERPRISE Tool: rlm_multi_project_query Use when: You have 5+ repos and don't know which has the answer Example: One query searches ALL your team's projects Time saved: 5 minutes of manual searching → 3 seconds { "query": "Where do we send email notifications?", "project_ids": [], "max_tokens": 8000 } ⚠️ Not available on Free/Pro plans - Requires Team plan for multi-project access.
🧠 AI Memory (Remember Preferences)
Plan Required: 🔥 PRO ($39/mo Agents) or 👥 TEAM ($79/mo Agents) Tools: rlm_remember + rlm_recall Use when: You want AI to remember your coding style/decisions Benefit: Consistent code across sessions Store a memory: { "content": "User prefers TypeScript strict mode with functional components", "type": "preference", "scope": "project" } Recall later: { "query": "What are my coding preferences?", "limit": 5 } Memory types: fact, decision, learning, preference, todo, context ⚠️ Requires separate Agents plan - Memory is part of Agents features, not Context plans.
👥 Team Standards (Auto-Enforce Rules)
Plan Required: 👥 TEAM ($49/mo) or ENTERPRISE Tool: rlm_shared_context Use when: Your team needs consistent coding practices Setup once: Upload coding standards to Shared Collection Every dev gets: Auto-injected team rules in every query { "categories": ["MANDATORY", "BEST_PRACTICES"], "max_tokens": 4000 } Categories by priority: MANDATORY - Non-negotiable rules (security, architecture) BEST_PRACTICES - Recommended patterns (40% token budget) GUIDELINES - Helpful suggestions REFERENCE - Background info ⚠️ Not available on Free/Pro plans - Team-wide features require Team plan.
🔧 Power User Tools
Multi-Query (Parallel Searches): { "queries": [ { "query": "auth flow", "max_tokens": 3000 }, { "query": "session management", "max_tokens": 3000 } ] } Decompose (Break Down Complex Questions): { "query": "Explain the complete payment system architecture" } Plan (Preview Execution): { "query": "Find all API endpoints", "strategy": "relevance_first" } Search (Regex Pattern Matching): { "pattern": "async def|async function", "max_results": 20 } Session Context (Inject Standards): { "context": "Use Python 3.11+, prefer dataclasses over Pydantic" }
📄 Document Management
Upload Single Doc: { "path": "docs/api.md", "content": "# API Documentation..." } Bulk Sync (CI/CD Integration): { "documents": [ { "path": "docs/auth.md", "content": "..." }, { "path": "docs/api.md", "content": "..." } ], "delete_missing": false } Check Stats: {}
Scenario 1: Solo Developer (Large Codebase)
Current pain: Grep/find searches take 20+ seconds, read 50K tokens per query MetricBefore SniparaWith SniparaSavingsQuery speed20 seconds2 seconds18 secondsDaily queries5050-Time per day16 minutes1.6 minutes14.4 min/dayTime per month7.2 hours0.72 hours6.5 hours/monthCost$0$0-19/moROI: 6.5 hours saved Plan recommendation: Start with FREE (100 queries), upgrade to PRO ($19/mo) if you need semantic search.
Scenario 2: Team (5+ Repositories)
Current pain: Switch between 5 projects manually, 5 minutes per search MetricBefore SniparaWith SniparaSavingsMulti-repo search5 min3 seconds4.97 minSearches per day1010-Time per day50 minutes30 seconds49.5 min/dayTime per month24.75 hours0.25 hours24.5 hours/monthCost$0$49/mo TeamROI: 24.5 hours saved Plan recommendation: TEAM ($49/mo) for rlm_multi_project_query + shared standards.
Scenario 3: Enterprise (Consistent Standards)
Current pain: 10 devs ask "how do we do X?" daily, inconsistent code BeforeWith Snipara Shared Context❌ Each dev googles/asks Slack✅ Standards auto-injected in every query❌ Inconsistent patterns✅ Enforced team conventions❌ Onboarding takes 2 weeks✅ New devs get standards instantly❌ Code review conflicts✅ Code follows standards from day 1 Cost: $49/mo Team or $499/mo Enterprise ROI: Consistency + faster onboarding = easily 20+ hours/month saved
Use Case 1: "I have huge docs and grep is slow"
Plan: ✅ FREE (100 queries/mo) # 1. Index your docs once Visit https://snipara.com/dashboard → Create project → Upload .md/.txt files # 2. Query instantly rlm_ask("How does authentication work?")
Use Case 2: "I work on 10 microservices"
Plan: 👥 TEAM ($49/mo) # 1. Create 10 projects on Snipara dashboard # 2. Enable Team plan # 3. Query all repos at once rlm_multi_project_query("How do we handle rate limiting?") ⚠️ Requires Team plan - Multi-project search not available on Free/Pro.
Use Case 3: "AI keeps forgetting my preferences"
Plan: 🔥 PRO Agents ($39/mo) or 👥 TEAM Agents ($79/mo) # 1. Enable Agents plan (separate from Context plan) # 2. Store your preferences once rlm_remember(type="preference", content="Use functional React components") # 3. AI recalls them forever rlm_recall("my coding preferences") ⚠️ Requires separate Agents subscription - Memory features not included in Context plans.
Context Plans (Documentation Search)
PlanPriceQueries/moSearch ModeMulti-ProjectFREE$0100Keyword only❌PRO$19/mo5,000Semantic + Hybrid❌TEAM$49/mo20,000Semantic + Hybrid✅ENTERPRISE$499/moUnlimitedSemantic + Hybrid✅
Agents Plans (Memory & Swarms)
PlanPricePrerequisiteFeaturesSTARTER$15/moNoneBasic memory (100 memories)PRO$39/moNoneUnlimited memories, swarmsTEAM$79/moContext TEAM+Team-wide memory sharingENTERPRISE$199/moContext ENTERPRISEAdvanced coordination ⚠️ Two separate subscriptions: Context plans for search, Agents plans for memory/swarms. Try free first: 100 queries is ~5 days of usage to test value.
Example 1: Quick Answer (FREE plan)
User: "What are our API rate limits?" You call: rlm_ask("API rate limits") Result: Returns relevant docs in 2 seconds
Example 2: Semantic Search (PRO plan)
User: "How do we validate user input?" You call: rlm_context_query("user input validation", search_mode="semantic") Result: Finds docs about "sanitization", "XSS prevention", "schema validation" even if they don't contain exact keywords
Example 3: Multi-Repo Search (TEAM plan)
User: "Show me all webhook implementations across our projects" You call: rlm_multi_project_query("webhook implementation") Result: Returns implementations from all 10 microservices in 3 seconds
Example 4: Persistent Memory (PRO Agents plan)
Session 1 (Monday): User: "I prefer TypeScript strict mode and functional components" You call: rlm_remember(type="preference", content="Prefers TS strict + functional") Session 2 (Friday - NEW SESSION): User: "Create a new React component" You call: rlm_recall("coding preferences") Result: AI remembers to use functional components from Monday!
Example 5: Team Standards (TEAM plan)
Setup (Admin does once): - Upload coding standards to Shared Context Collection - Link collection to all team projects Every developer: User: "Write a new API endpoint" You call: rlm_shared_context(categories=["MANDATORY"]) Result: Auto-injects team's API design rules, security requirements, etc.
Support & Resources
Website: https://snipara.com Documentation: https://docs.snipara.com GitHub: https://github.com/snipara/snipara-mcp Issues: https://github.com/snipara/snipara-mcp/issues Email: support@snipara.com
Quick Tips
Start small: Use rlm_ask for quick answers on FREE plan Upgrade smart: Get PRO when keyword search isn't finding what you need Team value: Multi-project search pays for itself with 5+ repos Memory requires separate plan: Context + Agents are two subscriptions Index first: Upload docs to dashboard before querying When in doubt, start with FREE and upgrade based on value received. 🚀
Query Tools (All Plans)
rlm_ask - Quick keyword search { "query": "API rate limits" } rlm_context_query - Full-featured semantic search { "query": "authentication", "max_tokens": 6000, "search_mode": "hybrid", "include_metadata": true } rlm_search - Regex pattern search { "pattern": "async def|async function", "max_results": 20 } rlm_inject - Set session context { "context": "Use Python 3.11+, prefer dataclasses", "append": false } rlm_context - Show current session context {} rlm_clear_context - Clear session context {}
Advanced Query Tools (Pro+)
rlm_multi_query - Parallel queries { "queries": [ { "query": "auth flow", "max_tokens": 3000 }, { "query": "session management", "max_tokens": 3000 } ], "max_tokens": 8000 } rlm_decompose - Break down complex questions { "query": "Explain payment system architecture", "max_depth": 2 } rlm_plan - Generate execution plan { "query": "Find all API endpoints", "strategy": "relevance_first", "max_tokens": 16000 }
Team Tools (Team+ Plan)
rlm_multi_project_query - Search across all repos { "query": "webhook implementation", "project_ids": [], "exclude_project_ids": [], "max_tokens": 8000, "per_project_limit": 3 } rlm_shared_context - Get team standards { "categories": ["MANDATORY", "BEST_PRACTICES"], "max_tokens": 4000, "include_content": true } rlm_list_templates - Browse prompt templates { "category": "code-review" } rlm_get_template - Use template with variables { "slug": "security-review", "variables": { "author": "John", "pr_number": "123" } } rlm_list_collections - List shared collections { "include_public": true } rlm_upload_shared_document - Upload to shared collection { "collection_id": "col_abc123", "title": "TypeScript Standards", "content": "# Standards...", "category": "BEST_PRACTICES", "priority": 90 }
Memory Tools (Agents Plan)
rlm_remember - Store memory { "content": "User prefers functional components", "type": "preference", "scope": "project", "category": "coding-style", "ttl_days": null } rlm_recall - Query memories { "query": "What are my preferences?", "type": "preference", "limit": 5, "min_relevance": 0.5 } rlm_memories - List all memories { "type": "preference", "category": "coding-style", "limit": 20, "offset": 0 } rlm_forget - Delete memories { "memory_id": "mem_abc123" }
Document Management Tools
rlm_upload_document - Upload single doc { "path": "docs/api.md", "content": "# API Documentation..." } rlm_sync_documents - Bulk upload { "documents": [ { "path": "docs/auth.md", "content": "..." }, { "path": "docs/api.md", "content": "..." } ], "delete_missing": false } rlm_store_summary - Store document summary { "document_path": "docs/api.md", "summary": "RESTful API with OAuth2 auth...", "summary_type": "concise", "generated_by": "claude-3.5-sonnet" } rlm_get_summaries - Get stored summaries { "document_path": "docs/api.md", "summary_type": "concise" } rlm_stats - Get documentation stats {} rlm_sections - List indexed sections { "filter": "auth", "limit": 50, "offset": 0 } rlm_read - Read specific lines { "start_line": 1, "end_line": 100 }
Advanced Features (Enterprise)
rlm_swarm_create - Create agent swarm { "name": "code-review-swarm", "description": "Parallel code review", "max_agents": 10 } rlm_swarm_join - Join swarm { "swarm_id": "swarm_abc123", "agent_id": "agent_1", "role": "worker", "capabilities": ["review", "test"] } rlm_claim - Claim resource for exclusive access { "swarm_id": "swarm_abc123", "agent_id": "agent_1", "resource_type": "file", "resource_id": "src/auth.ts", "timeout_seconds": 300 } rlm_release - Release claimed resource { "swarm_id": "swarm_abc123", "agent_id": "agent_1", "claim_id": "claim_abc123" } rlm_state_get - Read swarm state { "swarm_id": "swarm_abc123", "key": "progress" } rlm_state_set - Write swarm state { "swarm_id": "swarm_abc123", "agent_id": "agent_1", "key": "progress", "value": { "completed": 5, "total": 10 }, "expected_version": 1 } rlm_broadcast - Broadcast event to swarm { "swarm_id": "swarm_abc123", "agent_id": "agent_1", "event_type": "task_completed", "payload": { "task_id": "task_1" } } rlm_task_create - Create swarm task { "swarm_id": "swarm_abc123", "agent_id": "agent_1", "title": "Review auth module", "description": "Security review", "priority": 90 } rlm_task_claim - Claim task from queue { "swarm_id": "swarm_abc123", "agent_id": "agent_1", "task_id": "task_abc123" } rlm_task_complete - Mark task complete { "swarm_id": "swarm_abc123", "agent_id": "agent_1", "task_id": "task_abc123", "success": true, "result": { "issues_found": 0 } }
Settings & Configuration
rlm_settings - Get project settings { "refresh": false } Returns current project configuration including: Max tokens per query Default search mode Rate limits Enabled features For complete API documentation, visit: https://docs.snipara.com
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Scripts2 Docs1 Files
- README.md
- skill.md
- src/snipara_mcp/__init__.py
- src/snipara_mcp/auth.py
- src/snipara_mcp/rlm_tools.py
- pyproject.toml