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 0xDarkMatter/claude-mods --skill okf-opsgit clone --depth 1 https://github.com/0xDarkMatter/claude-modsWrote 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/0xdarkmatter/claude-mods/okf-ops)<a href="https://agentmods.dev/skills/0xdarkmatter/claude-mods/okf-ops"><img src="https://agentmods.dev/badge/skills/0xdarkmatter/claude-mods/okf-ops/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/0xdarkmatter/claude-mods/okf-ops"><img src="https://agentmods.dev/badge/skills/0xdarkmatter/claude-mods/okf-ops.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 | $0.00070 | $0.01235 |
| Opus 5 | $0.00035 | $0.00617 |
| Sonnet 5 | $0.00014 | $0.00247 |
| Haiku 4.5 | $0.00007 | $0.00123 |
Grade A, and why
okf-ops 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 9d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OKF Ops
The Open Knowledge Format (OKF) is a minimal, open convention for representing knowledge as a directory tree of markdown files with YAML frontmatter — the metadata and curated context that surrounds data and systems. This skill helps you assess whether a repo is a good fit, validate a bundle for conformance, and adopt OKF where it earns its keep.
Full format rules: references/okf-spec.md. Copy-ready concept doc: assets/concept-template.md.
Honest scope (read this before adopting)
OKF is a v0.1 draft (Google-published, platform-agnostic). It's deliberately
minimal: one required frontmatter field (type), reserved index.md/log.md, and a
permissive-consumption contract. Two consequences worth knowing up front:
- Adoption cost is shaped by the repo, not the size. A repo that already uses
frontmatter consistently is often one mechanical
type-derivation pass from conformant. A repo of bare prose markdown needs frontmatter authored on every file — often not worth it, and arguably the wrong files to make "concepts." - Conformance is a weak guarantee. "OKF-conformant" means the structural floor is met, not that the content is good. Use the assessment to decide adoption per-repo; don't make it a blanket mandate.
The tools here are useful regardless of OKF's trajectory — assess-okf.py is a
general "how frontmatter-consistent is this doc tree?" scanner.
Workflow: assess → decide → validate
1. Assess (read-only) — is this repo a good candidate?
python scripts/assess-okf.py docs/ # human summary
python scripts/assess-okf.py --json docs/ | jq '.data.readiness_pct'
Reports total .md, how many already carry frontmatter, how many have a non-empty
type, a histogram of existing frontmatter keys (shows what vocabulary you already
have to derive type from), type-value distribution, reserved files present, files
that would need a type, and an overall readiness %. Never writes.
What ships with it
5 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.
- 9d ago First seen · 96 lines · 70 tokens per session scan A ccfe27c9e90d
okf-ops is a skill published in the GitHub repository 0xDarkMatter/claude-mods (33 stars, last pushed 16d ago), licensed MIT. It adds 70 tokens to every session and 1,235 once invoked, about $0.0003 per session on Opus 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.
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