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 wikigit 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/wiki)<a href="https://agentmods.dev/skills/mifunedev/openharness/wiki"><img src="https://agentmods.dev/badge/skills/mifunedev/openharness/wiki/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/wiki"><img src="https://agentmods.dev/badge/skills/mifunedev/openharness/wiki.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 | $0.00187 | $0.01762 |
| Opus 5.5 | $0.00075 | $0.00705 |
| Sonnet 5.5 | $0.00037 | $0.00352 |
| Haiku 4.5 | $0.00019 | $0.00176 |
Grade A, and why
wiki 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wiki
One parameterized skill over the harness knowledge base. The first token of
$ARGUMENTS selects the operation; the remainder is that subcommand's argument
string. This dispatcher holds the routing logic and the rules shared by all four
operations; the full per-subcommand procedure lives in references/.
Two surfaces, one owner each. .oh/knowledge/ owns the data; this skill
owns the procedure. No knowledge page lives under .oh/skills/wiki/, and no
schema rule lives anywhere but references/schema.md.
Subcommands
| Subcommand | Argument form | Purpose | Reference |
|---|---|---|---|
ingest |
<url|path> [--slug <override>] · --from-draft <slug> [--allow-stale] |
Capture a source or promote a draft into an entity page (the only authorized write path) | references/ingest.md |
query |
<topic> [--patterns] |
Frontmatter OR-search over tracked knowledge; read the top matches into context (≤3 entity, ≤5 pattern) | references/query.md |
lint |
[--dry-run] |
Six correctness checks + atomic .oh/knowledge/README.md index regeneration |
references/lint.md |
compile |
[--from <path>] [--task <slug>] [--dry-run] |
Consolidate a /retro report into kind: pattern entries (create or patch) |
references/compile.md |
Dispatch
Parse $ARGUMENTS: the first whitespace-delimited token is the subcommand; the
rest is the subcommand's argument string.
ARGUMENTS="${ARGUMENTS:-}"
SUB="${ARGUMENTS%% *}" # first token
REST="${ARGUMENTS#"$SUB"}" # everything after it
REST="${REST# }" # trim one leading space
Route on $SUB, then follow the matching reference document end-to-end (its
instructions are authoritative — this dispatcher does not restate them):
$SUB |
Action |
|---|---|
ingest |
Read references/ingest.md; execute it with $REST as its argument string. |
query |
Read references/query.md; execute it with $REST as the <topic>. |
lint |
Read references/lint.md; execute it with $REST (only --dry-run is recognized). |
compile |
Read references/compile.md; execute it with $REST as its argument string. |
| anything else (incl. empty) | Print the usage line from argument-hint and exit 0. Do not guess a subcommand. |
What ships with it
10 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.
- references/compile.md 10 KB
- references/concurrent-ingest-worktrees.md 1.2 KB
- references/github-repo-research-wiki.md 2.7 KB
- references/ingest.md 39 KB
- references/lint.md 17 KB
- references/official-docs-research-wiki.md 2.1 KB
- references/query.md 12 KB
- references/schema.md 24 KB
- references/social-image-wiki-ingest.md 1.9 KB
- scripts/knowledge-impact.sh 5.4 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 Changed · -3 lines · -70 tokens per session 92958f061a9f
- 1mo ago Changed · +27 lines · +38 tokens per session 4329baeff16c
- 1mo ago First seen · 99 lines · 219 tokens per session scan A 290ead629df9
wiki is a skill published in the GitHub repository mifunedev/openharness (37 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 187 tokens to every session and 1,762 once invoked, about $0.0007 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.