OpenClaw Knowledge Coach

An OpenClaw-native skill for local knowledge retention. Import knowledge, generate practice, evaluate answers, and produce insight cards — all powered by the host agent's model, with zero extra API key configuration.

OpenClaw Users (Recommended)

When running inside an OpenClaw agent, the host provides model configuration. No

praxis llm setup

or API key setup is required.

Install the library:

pip install openpraxis

The skill uses the host agent's LLM capability automatically. Set the environment variable to enable OpenClaw mode:

export OPENPRAXIS_MODE=openclaw

Standalone CLI (Fallback)

For use outside of an OpenClaw agent, configure your own provider:

pip install openpraxis
praxis llm setup
praxis llm show

Environment variables override config file values:

export OPENAI_API_KEY="your_key_here"
# or ARK_API_KEY / MOONSHOT_API_KEY / DEEPSEEK_API_KEY based on provider

Core Workflow

1. Confirm scope and source

• Confirm knowledge domains, source folders, and accepted file types.
• Confirm whether to preserve existing metadata (tags, dates, project names).

2. 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

  .

3. 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.

4. 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.

5. Build review queue

• Group exercises by topic and difficulty.
• Schedule spaced review windows (for example: day 1, day 3, day 7, day 14).

6. 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:

1. Add a local file

praxis add "/absolute/path/to/note.md" --type report

2. List recent inputs and capture target

   input_id

praxis list --limit 20

3. Force-generate a new practice scene for an existing input

praxis practice <input_id>

4. Submit answer by file (preferred for deterministic runs)

praxis answer <scene_id> --file "/absolute/path/to/answer.md"

5. Inspect pipeline results and insight cards

praxis show <input_id>
praxis insight <input_id>

6. 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

.