Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add HKUST-KnowComp/DeepRefine-Skill --skill deeprefine_skillgit clone --depth 1 https://github.com/HKUST-KnowComp/DeepRefine-SkillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hkust-knowcomp/deeprefine-skill/deeprefine_skill)<a href="https://agentmods.dev/skills/hkust-knowcomp/deeprefine-skill/deeprefine_skill"><img src="https://agentmods.dev/badge/skills/hkust-knowcomp/deeprefine-skill/deeprefine_skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hkust-knowcomp/deeprefine-skill/deeprefine_skill"><img src="https://agentmods.dev/badge/skills/hkust-knowcomp/deeprefine-skill/deeprefine_skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.04737 |
| Opus 5.5 | $0.00017 | $0.01895 |
| Sonnet 5.5 | $0.00008 | $0.00947 |
| Haiku 4.5 | $0.00004 | $0.00474 |
Grade A, and why
deeprefine scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- deeprefine — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 477 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepRefine — Agent refinement loop (strict)
Default safety policy: dry-run only
A normal /deeprefine invocation MUST NEVER call deeprefine apply.
The default /deeprefine workflow must stop after:
deeprefine loop validatedeeprefine review- showing the proposed actions and HIGH/MEDIUM/LOW review report to the user
Then ask the user for explicit approval.
Only if the user's next message explicitly says to approve/apply/write the graph may you run:
deeprefine apply --refresh-wiki --trace-file ... --refinement-file ...
deeprefine loop finish --trace-file ... --refinement-file ...
Do not treat generation of <refinement> actions as approval. Do not treat a valid trace as approval. Do not apply in the same /deeprefine turn.
You MUST implement the same control flow as DeepRefine.refine() in DeepRefine (autorefiner/src/deeprefine.py).
| Component | Agent mode | CLI deeprefine refine |
|---|---|---|
| Retrieval | graphify query + k-hop from graph.json |
FAISS retriever |
| LLM | Your session model | External API / vLLM |
| Graph writes | Dry-run proposal + deeprefine review; deeprefine apply only after user approval |
Dry-run by default; --apply persists |
FORBIDDEN (hard stop)
Do NOT:
- Run
deeprefine refine(unless the user explicitly asks for CLI/FAISS mode). - Call
deeprefine applywithout a validloop_trace_<query_id>.json(CLI will reject). - Call
deeprefine applybefore runningdeeprefine reviewand receiving explicit user approval. - Ignore LOW-confidence review warnings unless the user explicitly requests
--allow-low-confidence. - Skip any hop’s
<judge>Yes</judge>/<judge>No</judge>judgement. - Skip error abduction when
len(interaction_history) > 1. - Write
<refinement>before abduction when refinement is required. - Hand-edit
graph.jsonwith Python or ad-hoc JSON patches. - Ignore pending history and refine only one latest query when unrefined queries already exist.
- Invent a shorter pipeline (“read file → write refinement → apply”).
What ships with it
40 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- __init__.py 105 B runs code
- action_review.py 14 KB runs code
- adapter_graphify.py 6.7 KB runs code
- agent_graph.py 7.2 KB runs code
- agent_loop.py 8.6 KB runs code
- agent_prompts.py 2.9 KB runs code
- claude_skill/references/deeprefine-workflow.md 5.4 KB
- claude_skill/references/llm-prompts.md 3.3 KB
- claude_skill/references/trace-and-commands.md 4.2 KB
- claude_skill/SKILL.md 5.5 KB
- cli.py 48 KB runs code
- codex_skill/agents/openai.yaml 275 B
- codex_skill/references/deeprefine-workflow.md 5.5 KB
- codex_skill/references/llm-prompts.md 3.3 KB
- codex_skill/references/trace-and-commands.md 5.1 KB
- codex_skill/SKILL.md 5.5 KB
- commands/opencode/deeprefine-apply.md 1.1 KB
- commands/opencode/deeprefine-review.md 892 B
- commands/opencode/deeprefine.md 757 B
- dsh_skill/references/deeprefine-workflow.md 5.5 KB
- dsh_skill/references/llm-prompts.md 3.3 KB
- dsh_skill/references/trace-and-commands.md 4.2 KB
- dsh_skill/SKILL.md 6.3 KB
- gemini_extension/commands/deeprefine.toml 1.7 KB
- gemini_extension/commands/deeprefine/apply.toml 1.2 KB
- gemini_extension/commands/deeprefine/review.toml 1.3 KB
- gemini_extension/gemini-extension.json 252 B
- gemini_extension/GEMINI.md 1.6 KB
- gemini_extension/skills/deeprefine/SKILL.md 19 KB
- history.py 6.2 KB runs code
- installers.py 23 KB runs code
- obsidian_refresh.py 614 B runs code
- paths.py 6.7 KB runs code
- refine_runner.py 9.2 KB runs code
- SKILL_COPILOT.md 22 KB
- SKILL_OPENCODE.md 33 KB
- wiki_importer.py 12 KB runs code
- wiki_refresh.py 12 KB runs code
- wiki_retrieval.py 6.4 KB runs code
- wiki_update.py 15 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 477 lines · 42 tokens per session scan A 2d3bf1019872
deeprefine is a skill published in the GitHub repository HKUST-KnowComp/DeepRefine-Skill (93 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 4,737 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
llm-wiki
Build and maintain a persistent, interlinked Obsidian-compatible markdown wiki using Karpathy's LLM Wiki pattern. Extension-backed with auto-generated metadata, guardrails, and 14 custom tools (+3 opt-in agent-trajectory tools).
binder-import
Import external data into a Binder workspace. Handles CSV, JSON, YAML, Markdown files, and directories of Markdown. Use when asked to "import data", "load records from a file", "ingest documents", "migrate data into binder", or bulk-create records from an external source.
ml_inference_optimization
This document describes the Leeroopedia MCP tools available during the with-KB benchmark run. It is a standalone reference and is not fed to the agents automatically.
customer_support_agent
You have access to Leeroopedia, a curated ML/AI knowledge base, via MCP tools. These are real MCP tools registered in your environment -- call them directly like any other tool. They contain framework-specific docs, code examples, API references, and best practices.
leeroopedia-mcp
Use Leeroopedia MCP to fetch grounded ML/AI best practices, build and review ML plans, debug failures, verify code/math correctness, and expand KB citations via getpage.
self_evolve_rag
This document describes how to use the Leeroopedia MCP tools for the Self-Evolving RAG task. It is kept as a reference and is NOT included in the agent prompt.