Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
openclaw-knowledge-coach
Build and operate an OpenClaw-based local knowledge assistant that imports personal/local documents into a knowledge base and creates practice exercises duri...
skill openclawclawhub Free
Build and operate an OpenClaw-based local knowledge assistant that imports personal/local documents into a knowledge base and creates practice exercises duri...
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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
- SKILL.md, references/exercise-patterns.md
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. 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. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- Sibo-Zhao
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
OpenClaw Knowledge Coach
Create a local knowledge workflow in OpenClaw where importing knowledge also produces practice material for retention. OpenPraxis is on PyPI: use pip install openpraxis to get the praxis CLI.
CLI First
Use OpenPraxis CLI as the default execution path. Install from PyPI (recommended): pip install openpraxis praxis --help Or install from source for development: git clone https://github.com/Sibo-Zhao/OpenPraxis.git cd OpenPraxis pip install -e ".[dev]" praxis --help Configure provider/model/API key before ingestion/practice: praxis llm setup praxis llm show Use environment variables when needed (higher priority than config file): export OPENAI_API_KEY="your_key_here" # or ARK_API_KEY / MOONSHOT_API_KEY / DEEPSEEK_API_KEY based on provider
Core Workflow
Confirm scope and source Confirm knowledge domains, source folders, and accepted file types. Confirm whether to preserve existing metadata (tags, dates, project names). Define import contract Normalize each source into a record with doc_id, title, source_path, tags, created_at, and content. Split long content into chunks with stable IDs such as doc_id#chunk-001. Import into OpenClaw Ingest normalized records into the local OpenClaw knowledge base. Keep a deterministic mapping between source file and imported IDs for later updates. Generate exercises at import time For each chunk, create at least one retrieval exercise. Prefer three exercise types: free-recall: ask the user to explain from memory. qa: ask direct question-answer pairs. application: ask scenario-based transfer questions. Save answer keys and concise grading rubrics. Build review queue Group exercises by topic and difficulty. Schedule spaced review windows (for example: day 1, day 3, day 7, day 14). Validate quality Reject exercises that can be answered without the imported knowledge. Reject ambiguous or duplicate questions. Ensure every exercise points back to doc_id and chunk_id.
CLI Command Playbook
Run this sequence when the user asks to import local knowledge and create practice: Add a local file praxis add "/absolute/path/to/note.md" --type report List recent inputs and capture target input_id praxis list --limit 20 Force-generate a new practice scene for an existing input praxis practice <input_id> Submit answer by file (preferred for deterministic runs) praxis answer <scene_id> --file "/absolute/path/to/answer.md" Inspect pipeline results and insight cards praxis show <input_id> praxis insight <input_id> Export insights to Markdown/JSON praxis export --format md --output "/absolute/path/to/insights.md" praxis export --format json --output "/absolute/path/to/insights.json"
Agent Execution Rules
Prefer praxis add for import and initial exercise generation. Parse IDs from CLI output, then chain praxis practice and praxis answer. Use praxis answer --file instead of interactive stdin in automation flows. If duplicate content is skipped, rerun with praxis add ... --force when user wants reprocessing. Use one-shot runtime model override only when requested: praxis --provider openai --model gpt-4.1-mini add "/absolute/path/to/note.md" For image notes, pass image file path directly to praxis add; OCR extraction is built in. Always finish with praxis show plus praxis insight or praxis export so user gets concrete output artifacts.
Output Contract
When executing tasks with this skill, always provide these outputs: Import summary: files processed, chunks created, failures. Exercise summary: counts by type/topic/difficulty. Review plan: next due batches and estimated workload. Traceability map: source -> doc_id -> chunk_id -> exercise_id.
Exercise Format
Use this compact JSON-like structure per exercise: { "exercise_id": "ex-...", "doc_id": "...", "chunk_id": "...", "type": "free-recall | qa | application", "question": "...", "answer_key": "...", "rubric": ["point 1", "point 2"], "difficulty": "easy | medium | hard", "next_review": "YYYY-MM-DD" } For more generation patterns, read references/exercise-patterns.md.
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
2 Docs
- SKILL.md
- references/exercise-patterns.md