DeepRefine-Skill

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

/deeprefine

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

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News

• [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.

Why Agent CLI first

• Uses your current Cursor session model (no separate API/vLLM setup required)
• Follows the same control flow as Reafiner.refine()
• Integrates with graphify query memory automatically
• Handles pending queries in batch, one by one

One-time setup

pip install deeprefine-cli graphifyy

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

After upgrading the package, run deeprefine cursor install 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)

When you run /deeprefine, it should follow this order:

1. deeprefine history sync-memory
   • import queries from graphify-out/memory/query_*.md
   • write to graphify-out/.deeprefine/history.jsonl
2. load pending queries from history.jsonl (refined != true)
3. refine all pending queries sequentially
4. mark each finished query as refined via deeprefine loop finish

Agent artifacts

graphify-out/
├── graph.json                              # graphify main graph (refined in-place)
├── 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
    ├── loop_trace_<query_id>.json          # per-query loop audit trace
    ├── refinement_results_<YYYYMMDD>.jsonl # per-day run log
    └── refinement_actions_*.txt            # optional; only when refinement path is taken

Agent-related commands

Run from your KB project root.

Command	Description
deeprefine cursor install	Install /deeprefine skill into current project
deeprefine cursor install --user	Install skill for all projects (~/.cursor/skills/)
deeprefine history sync-memory	Import graphify-out/memory/query_*.md into DeepRefine history
deeprefine history list --pending	Show unrefined queue
deeprefine loop init --query "..."	Create loop_trace_<id>.json template
deeprefine loop validate --trace-file T	Validate trace against Reafiner control flow
deeprefine apply --trace-file T --refinement-file F	Apply <refinement> actions to graph.json
deeprefine loop finish --trace-file T [--refinement-file F]	Persist results and mark history refined

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Terminal CLI (FAISS + API/vLLM)

Use this section when you want a pure terminal workflow without Cursor /deeprefine.

Extra requirements

• DeepRefine repository installed in atlastune
• Inference backend configured (API or vLLM)

conda activate atlastune
cd /path/to/DeepRefine && pip install -e .
pip install deeprefine-cli

# Optional, if DeepRefine repo is elsewhere
export DEEPREFINE_REPO=/path/to/DeepRefine

Inference environment (CLI mode)

Variable	Default
DEEPREFINE_LLM_URL	(empty; SDK default)
DEEPREFINE_EMBED_URL	(empty; SDK default)
DEEPREFINE_API_KEY	fallback to OPENAI_API_KEY
DEEPREFINE_LLM_API_KEY	fallback to DEEPREFINE_API_KEY
DEEPREFINE_EMBED_API_KEY	fallback to DEEPREFINE_API_KEY
DEEPREFINE_MODEL	gpt-4.1-mini
DEEPREFINE_EMBED_MODEL	text-embedding-3-small

Terminal workflow

cd /path/to/your-kb-project

# Option A: import from graphify memory first (recommended)
deeprefine history sync-memory
deeprefine history list --pending
deeprefine refine

# Option B: add one explicit query
deeprefine history add --query "your question"
deeprefine refine

Terminal commands

Command	Description
deeprefine history add --query "..."	Append one query to history
deeprefine history list	List all history rows
deeprefine history sync-memory	Import graphify memory queries into history
deeprefine history list --pending	List only unrefined queries
deeprefine refine	Refine all pending queries
deeprefine refine --query "..."	Refine a single query (also records it)
deeprefine refine --rebuild-index	Rebuild FAISS before refine
deeprefine index --rebuild	Rebuild FAISS cache only

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Installation

Method	Command
PyPI	pip install deeprefine-cli==0.1.7
Source	pip install -e /path/to/DeepRefine-Skill

deeprefine --help
# Expect: cursor, history, index, refine, apply, loop

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License

MIT — see LICENSE.