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 mifunedev/openharness --skill rlmgit clone --depth 1 https://github.com/mifunedev/openharnessWrote 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/mifunedev/openharness/rlm)<a href="https://agentmods.dev/skills/mifunedev/openharness/rlm"><img src="https://agentmods.dev/badge/skills/mifunedev/openharness/rlm/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/mifunedev/openharness/rlm"><img src="https://agentmods.dev/badge/skills/mifunedev/openharness/rlm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 42 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 122 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 171 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 172 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00235 | $0.02584 |
| Opus 5.5 | $0.00094 | $0.01034 |
| Sonnet 5.5 | $0.00047 | $0.00517 |
| Haiku 4.5 | $0.00023 | $0.00258 |
Grade A, and why
rlm 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.
How it starts
The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
rlm — context-as-environment decomposition
The /rlm skill is Layer B of the harness RLM integration (the plan:
.claude/plans/there-s-a-whole-snappy-crayon.md § Layer B). It answers a query over
an artifact too large to ingest by treating that artifact as an environment the
root agent addresses — grep/slice/chunk-map — rather than a blob it reads into its
context window. It then recurses sub-agent calls over the narrowed chunks under a
bounded budget, and aggregates their structured returns, routing competing
candidate answers through /weigh.
Single responsibility. /rlm owns decomposition; /weigh owns selection.
They compose: /rlm fans sub-calls out over chunks, /weigh scores/selects among the
candidate answers a chunk yields. This skill never re-implements selection — it calls
/weigh.
Reuse, don't reinvent (load-bearing). The recursion substrate already exists:
| Substrate | Owner | How /rlm uses it |
|---|---|---|
| Recursion loop | /spec execute |
each story re-reads disk = the REPL step — owned by the active implementation owner, never split into another session |
| Isolated recursion branches | .worktrees/ (the /worktrees skill) |
depth-2 sub-trees fork here — reused by reference, never edited |
| Recursion budget | /delegate (.oh/skills/delegate/SKILL.md) |
the Max depth N / Max children per level M / Step budget S triple — references/recursion-budget.md points at it and adds a per-run token ceiling |
| Chunk-map primitive | scripts/query-context.mjs (US-004, this skill) |
partitions the artifact without ingesting it |
| Candidate selection | /weigh |
scores competing per-chunk answers |
Do not edit
/spec execute's task cycle or anything under.worktrees/./rlmis a consumer of both. The only genuinely new substrate this skill adds isquery-context.mjsand this procedure.
When to use
/rlm <artifact> "<query>"to answer a question over an artifact too large to read whole (a big log undercrons/.cron.log, a large corpus file, a whole directory).- As a sub-step of
/weighwhen the cohort being sampled spans a large artifact that must itself be decomposed before sampling.
What ships with it
4 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.
- 1mo ago Changed dbd5e9ab4359
- 1mo ago First seen · 174 lines · 235 tokens per session scan A 19cd4979dc51
rlm is a skill published in the GitHub repository mifunedev/openharness (37 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 235 tokens to every session and 2,584 once invoked, about $0.0009 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
remember
Review the current conversation and capture valuable knowledge — best practices, coding conventions, architecture decisions, workflows, and user feedback — into persistent memory (AGENTS.md) or reusable skills. Use when the user says: (1) remember this, (2) save what we learned, (3) update memory, (4) capture…
coding-prefs
Read the user's coding preferences from /memory/coding-prefs.md before making non-trivial style decisions, and append new preferences when the user gives durable feedback.
data-visualization
Use for creating publication-quality charts and multi-panel analysis summaries. Triggers when tasks involve visualizing data, plotting results, creating charts, or producing visual reports from analysis output.
cuml-machine-learning
Use for GPU-accelerated machine learning on tabular data using NVIDIA cuML. Triggers when tasks involve classification, regression, clustering, dimensionality reduction, or model training on datasets.
social-media
Drafts engaging social media posts, writes hooks, suggests hashtags, creates thread structures, and generates companion images. Use when the user asks to write a LinkedIn post, tweet, Twitter/X thread, social media caption, social post, or repurpose content for social platforms.
blog-post
Writes and structures long-form blog posts, creates tutorial outlines, and optimizes content for SEO with cover image generation. Use when the user asks to write a blog post, article, how-to guide, tutorial, technical writeup, thought leadership piece, or long-form content.