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 agentmods add skills/hkust-knowcomp/deeprefine-skill/dsh_skillnpx skills add HKUST-KnowComp/DeepRefine-Skill --skill dsh_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/dsh_skill)<a href="https://agentmods.dev/skills/hkust-knowcomp/deeprefine-skill/dsh_skill"><img src="https://agentmods.dev/badge/skills/hkust-knowcomp/deeprefine-skill/dsh_skill.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00073 | $0.01526 |
| Opus 5 | $0.00036 | $0.00763 |
| Sonnet 5 | $0.00015 | $0.00305 |
| Haiku 4.5 | $0.00007 | $0.00153 |
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 5d 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.
This is a copy
89% identical to deeprefine — 47 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepRefine - DeepSeek Harness (dsh) Adapter
This file is the dsh-specific entrypoint. It keeps the platform rules small and loads longer DeepRefine procedure details only when needed:
- Full workflow, queue selection, refinement branch logic, and review rules: references/deeprefine-workflow.md
- Verbatim judgement, abduction, and refinement prompts: references/llm-prompts.md
- Checklist, command sequence, trace schema, paths, and CLI mode: references/trace-and-commands.md
Do not reimplement or shorten the algorithm from memory. Load the relevant reference file before executing that part of the workflow.
dsh Invocation
Trigger this skill when the user:
- explicitly invokes
/deeprefine; - asks to refine, improve, diagnose, repair, inspect, or review a Graphify knowledge graph;
- asks to apply a previously reviewed DeepRefine refinement.
Run from the knowledge-base project root, where graphify-out/graph.json
exists. If the user is planning or asking how DeepRefine works, explain the
workflow and do not mutate files.
If deeprefine is unavailable, tell the user to install it:
pip install deeprefine-cli
For source development:
pip install -e /path/to/DeepRefine-Skill
Hard Safety Policy
A normal /deeprefine invocation is dry-run only and MUST NEVER call
deeprefine apply.
The default workflow must stop after:
deeprefine loop validatedeeprefine review- showing the proposed actions and HIGH/MEDIUM/LOW review report to the user
Then ask for explicit approval.
Only if the user's next message explicitly says to approve/apply/write the graph may you run:
deeprefine apply --trace-file ... --refinement-file ...
deeprefine loop finish --trace-file ... --refinement-file ...
Do not treat any of these as approval:
- generation of a
<refinement>block; - a valid
loop_trace_<query_id>.json; - a prior user message;
- a successful
deeprefine review.
What ships with it
3 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.
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.
- 5d ago First seen · 188 lines · 73 tokens per session scan A 905738d00195
deeprefine is a skill published in the GitHub repository HKUST-KnowComp/DeepRefine-Skill (93 stars, last pushed 12d ago), licensed MIT. It adds 73 tokens to every session and 1,526 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to deeprefine, differing in 47 lines, and is treated as a copy.
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