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 7df-lab/devo --skill refinegit clone --depth 1 https://github.com/7df-lab/devoWrote 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/7df-lab/devo/refine)<a href="https://agentmods.dev/skills/7df-lab/devo/refine"><img src="https://agentmods.dev/badge/skills/7df-lab/devo/refine/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/7df-lab/devo/refine"><img src="https://agentmods.dev/badge/skills/7df-lab/devo/refine.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00038 | $0.00176 |
| Opus 5.5 | $0.00015 | $0.00070 |
| Sonnet 5.5 | $0.00008 | $0.00035 |
| Haiku 4.5 | $0.00004 | $0.00018 |
Grade A, and why
refine 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 10d 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:
- refine — 100% identical, 0 lines differ
What it actually says
Refine skill
Schedule continual harness refinement from the Python REPL. Refinement never runs mid-cell — the host applies pending edits when the turn ends.
await refine.status()
await refine.run(instructions="Prefer local memory for this session's blockers")
await refine.run(instructions="Promote durable preference", global_=True)
Harness CRUD for memories and related entries lives on rlm.harness (requires
RLM_SESSION_DIR / RLM_HARNESS_STATE_DIR from the host):
rlm.harness.create_memory(title="...", content="...")
rlm.harness.overview()
What ships with it
1 file 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.
- 10d ago First seen · 23 lines · 38 tokens per session scan A ebbe4f248410
refine is a skill published in the GitHub repository 7df-lab/devo (321 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 176 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 2026-10-06.
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