Knowledge Extraction

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/bdistill-knowledge-extraction

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Knowledge Extraction Extract structured, quality-scored domain knowledge from any AI model — in-session from closed models (no API key) or locally from open-source models via Ollama.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: How It Works, Output Format, Tabular ML Data Generation, Local Model Extraction (Ollama), Security & Safety Notes, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when you need structured reference data on any domain (medical, legal, finance, cybersecurity)
• Use when building lookup tables, Q&A datasets, or research corpora
• Use when generating training data for traditional ML models (regression, classification — NOT competing LLMs)
• Use when you want cross-model comparison on domain knowledge
• Use when the request clearly matches the imported source intent: Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Overview

bdistill turns your AI subscription sessions into a compounding knowledge base. The agent answers targeted domain questions, bdistill structures and quality-scores the responses, and the output accumulates into a searchable, exportable reference dataset.

Adversarial mode challenges the agent's claims — forcing evidence, corrections, and acknowledged limitations — producing validated knowledge entries.

Imported: How It Works

Step 1: Install

pip install bdistill
claude mcp add bdistill -- bdistill-mcp   # Claude Code

Step 2: Extract knowledge in-session

/distill medical cardiology                    # Preset domain
/distill --custom kubernetes docker helm       # Custom terms
/distill --adversarial medical                 # With adversarial validation

Step 3: Search, export, compound

bdistill kb list                               # Show all domains
bdistill kb search "atrial fibrillation"       # Keyword search
bdistill kb export -d medical -f csv           # Export as spreadsheet
bdistill kb export -d medical -f markdown      # Readable knowledge document

Examples

Example 1: Ask for the upstream workflow directly

Use @bdistill-knowledge-extraction to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @bdistill-knowledge-extraction against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @bdistill-knowledge-extraction for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @bdistill-knowledge-extraction using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/bdistill-knowledge-extraction

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @azure-mgmt-apicenter-py

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @azure-mgmt-apimanagement-dotnet

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @azure-mgmt-apimanagement-py

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @azure-mgmt-applicationinsights-dotnet

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Output Format

Structured reference JSONL — not training data:

{
  "question": "What causes myocardial infarction?",
  "answer": "Myocardial infarction results from acute coronary artery occlusion...",
  "domain": "medical",
  "category": "cardiology",
  "tags": ["mechanistic", "evidence-based"],
  "quality_score": 0.73,
  "confidence": 1.08,
  "validated": true,
  "source_model": "Claude Sonnet 4"
}

Imported: Tabular ML Data Generation

Generate structured training data for traditional ML models:

/schema sepsis | hr:float, bp:float, temp:float, wbc:float | risk:category[low,moderate,high,critical]

Exports as CSV ready for pandas/sklearn. Each row tracks source_model for cross-model analysis.

Imported: Local Model Extraction (Ollama)

For open-source models running locally:

# Install Ollama from https://ollama.com
ollama serve
ollama pull qwen3:4b

bdistill extract --domain medical --model qwen3:4b

Imported: Security & Safety Notes

• In-session extraction uses your existing subscription — no additional API keys
• Local extraction runs entirely on your machine via Ollama
• No data is sent to external services
• Output is reference data, not LLM training format

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.