Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท AI
LLM Supervisor
Graceful rate limit handling with Ollama fallback. Notifies on rate limits, offers local model switch with confirmation for code tasks.
skill openclawclawhub Free
Graceful rate limit handling with Ollama fallback. Notifies on rate limits, offers local model switch with confirmation for code tasks.
โฌ 0 downloads โ
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, SKILL.md, commands/llm.ts, dist/commands/llm.js, dist/hooks/beforeTaskExecute.js, dist/hooks/onAgentStart.js
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
- 0.3.0
Provenance
- Publisher
- dhardie
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
LLM Supervisor ๐ฎ
Handles rate limits and model fallbacks gracefully.
On Rate Limit / Overload Errors
When I encounter rate limits or overload errors from cloud providers (Anthropic, OpenAI): Tell the user immediately โ Don't silently fail or retry endlessly Offer local fallback โ Ask if they want to switch to Ollama Wait for confirmation โ Never auto-switch for code generation tasks
Confirmation Required
Before using local models for code generation, ask: "Cloud is rate-limited. Switch to local Ollama (qwen2.5:7b)? Reply 'yes' to confirm." For simple queries (chat, summaries), can switch without confirmation if user previously approved.
/llm status
Report current state: Which provider is active (cloud/local) Ollama availability and models Recent rate limit events
/llm switch local
Manually switch to Ollama for the session.
/llm switch cloud
Switch back to cloud provider.
Using Ollama
# Check available models ollama list # Run a query ollama run qwen2.5:7b "your prompt here" # For longer prompts, use stdin echo "your prompt" | ollama run qwen2.5:7b
Installed Models
Check with ollama list. Configured default: qwen2.5:7b
State Tracking
Track in memory during session: currentProvider: "cloud" | "local" lastRateLimitAt: timestamp or null localConfirmedForCode: boolean Reset to cloud at session start.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Scripts2 Docs
- SKILL.md
- README.md
- commands/llm.ts
- dist/commands/llm.js
- dist/hooks/beforeTaskExecute.js
- dist/hooks/onAgentStart.js