hkust-knowcomp/deeprefine-skill

DeepRefine-Skill plugs into agent workflows and use a single command /deeprefine in your agent (Cursor, Copilot CLI, Gemini CLI, Codex, OpenCode, Claude Code) to refine and evolve your LLM-Wiki (e.g., graphify) knowledge base.

It refines your graphify knowledge graph for better future retrieval and Q&A quality.

Supported agent frameworks:

                 

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News

• [2026/7/10] v0.2.0 - Claude Code and OpenCode adapters: deeprefine claude install / deeprefine opencode install, bundled skill + command templates.
• [2026/7/3] v0.1.9 - Release with Codex, Copilot CLI, and Gemini CLI skills bundled; dry-run-first refinement, evidence-aware action review (HIGH/MEDIUM/LOW), ambiguous-node warnings, and LOW-confidence apply guard.
• [2026/6/24] v0.1.9 - Codex skill supported.
• [2026/6/18] v0.1.9 - Gemini CLI and Copilot CLI supported.
• [2026/6/17] v0.1.9 - Added dry-run-first refinement, evidence-aware action review, ambiguous-node warnings, and LOW-confidence apply guard.
• [2026/6/15] v0.1.8 - Aligned interaction memory with LLM-Wiki (graphify) and fixed the single query refinement issue.
• [2026/6/2] v0.1.7 — Cursor skill + deeprefine refine with configurable API. And strict DeepRefine agent loop.

Agent CLI (Recommended)

This is the default mode and the main workflow for this project.

One-time setup

pip install deeprefine-cli graphifyy

cd /path/to/your-kb-project
graphify cursor install

# for Cursor
deeprefine cursor install
# for Copilot CLI
deeprefine copilot install
# for Gemini CLI
deeprefine gemini install # or deeprefine gemini link
# for Codex
deeprefine codex install
# for Claude Code
deeprefine claude install
# for OpenCode
deeprefine opencode install

After upgrading the package, run the command again to refresh local skill files.

Typical session (Agent CLI)

/graphify .
/graphify ./ --wiki
/graphify query "your question 1"
/graphify query "your question 2"
# ..
/deeprefine

What /deeprefine does now (default queue behavior)

Agent artifacts

graphify-out/
├── graph.json                              # graphify main graph; unchanged until apply approval
├── memory/
│   └── query_*.md                          # graphify query logs (sync source)
└── .deeprefine/
    ├── history.jsonl                       # DeepRefine-maintained history queue
    ├── graph.json.bak                      # backup before first apply in this run
    ├── checkpoints/graph.checkpoint..json  # post-state graph after each apply
    ├── checkpoints.json                    # checkpoint timeline registry
    ├── loop_trace_.json          # per-query loop audit trace
    ├── refinement_results_.jsonl # per-day run log
    ├── refinement_actions_*.txt            # optional; only when refinement path is taken
    ├── proposed_refinement_actions_*.txt    # CLI dry-run proposed actions
    ├── proposed_refinement_review_*.md      # evidence-aware review report
    └── proposed_refinement_review_*.json    # optional structured review report

Agent-related commands

Run from your KB project root.

Evidence-aware review and safe apply

/deeprefine should default to dry-run-first behavior. Proposed actions are reviewed before they can modify graphify-out/graph.json. Each action is labeled:

Bare function names such as main(), run(), train(), test(), and setup() are treated as ambiguous. Prefer file-qualified names:

BAD:  insert_edge("main()", "calls", "Trainer")
GOOD: insert_edge("pretraining/pretraining_CLIP_fine-grained.py::main()", "calls", "Trainer")

deeprefine apply refuses LOW-confidence actions by default. Use --allow-low-confidence only when the user explicitly accepts the risk.

When the knowledge base was created with Graphify Wiki output, use --refresh-wiki. DeepRefine stages the refined graph, regenerates the Wiki with graphify export wiki, validates wiki/index.md, and only then replaces the production graph and Wiki. If export fails, the existing graph and Wiki remain unchanged.

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Codex Integration

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Copilot CLI Integration

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Gemini CLI Integration

OpenCode Integration

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Claude Code Integration

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Terminal CLI (FAISS + API/vLLM)

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License

MIT — see LICENSE.