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 evist0/okf-matt-skills --skill implementgit clone --depth 1 https://github.com/evist0/okf-matt-skillsWrote 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/evist0/okf-matt-skills/implement)<a href="https://agentmods.dev/skills/evist0/okf-matt-skills/implement"><img src="https://agentmods.dev/badge/skills/evist0/okf-matt-skills/implement/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/evist0/okf-matt-skills/implement"><img src="https://agentmods.dev/badge/skills/evist0/okf-matt-skills/implement.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 | $0.00016 | $0.00140 |
| Opus 5.5 | $0.00006 | $0.00056 |
| Sonnet 5.5 | $0.00003 | $0.00028 |
| Haiku 4.5 | $0.00002 | $0.00014 |
Grade A, and why
implement 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.
What it actually says
Implement the work described by the user in the spec or tickets.
Use /tdd where possible, at pre-agreed seams.
Run typechecking regularly, single test files regularly, and the full test suite once at the end.
Once done, use /vet to review the work.
After the review's findings are resolved, run /reconcile to bring the OKF knowledge bundle back into agreement with what you built — over the same diff the review used.
Commit your work to the current branch, so code and knowledge land in one commit.
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 First seen · 18 lines · 16 tokens per session scan A ab1d271bca2b
implement is a skill published in the GitHub repository evist0/okf-matt-skills (17 stars, last pushed 3mo ago), licensed MIT. It adds 16 tokens to every session and 140 once invoked, about $0.0001 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
product-roadmap
Use when doing weekly progress checks or milestone validation — tracks decision, execution, evidence, and gate states across four layers of truth.
workspace
Use when coordinating a multi-repo workspace — init the .adlc/ structure, discover and link child repos as submodules, or audit workspace health (branch, dirty, unpushed, SHA drift).
product-clarify
Use when PDRs need refinement, conflict detection, or approval to Accepted status before /product-implement.
team-levelup
Use when a session ends to extract CDRs, score confidence, batch review, and publish accepted CDRs to team-ai-directives. Auto-triggers on sessionend event. Also invoked from team-boot's Class Boots catalog for CDR descriptor matches.
product-boot
Use when product or feature work starts, or a PDR-worthy decision (scope, persona, monetization) emerges — injects the PDR index (docs/adlc/memory/pdr/, legacy .adlc/memory/pdr/ fallback) as session context and pairs the decision with capture via /product-specify; invoked from team-boot's Class Boots catalog.
factory-mission
Use when executing a spec-gated mission from the queue — inner loop specify → plan → implement ↔ converge with tracker integration, worktree isolation, and a circuit breaker.