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 haoruilee/oh-my-mcode --skill verifygit clone --depth 1 https://github.com/haoruilee/oh-my-mcodeWrote 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/haoruilee/oh-my-mcode/verify)<a href="https://agentmods.dev/skills/haoruilee/oh-my-mcode/verify"><img src="https://agentmods.dev/badge/skills/haoruilee/oh-my-mcode/verify/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/haoruilee/oh-my-mcode/verify"><img src="https://agentmods.dev/badge/skills/haoruilee/oh-my-mcode/verify.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 | $0.00114 | $0.00681 |
| Opus 5.5 | $0.00046 | $0.00272 |
| Sonnet 5.5 | $0.00023 | $0.00136 |
| Haiku 4.5 | $0.00011 | $0.00068 |
Grade A, and why
verify 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
Verify
Only this skill may set run status to accepted or rejected. Load agents/verifier.md. Do not edit product code. Evidence files and findings.json are allowed.
中文名:独立验收。
Inputs
- Existing
run_id(user-supplied,OMM_RUN_ID, or--latest). tasks.jsonacceptance[]andplan.md.- State tool:
node <plugin-root>/scripts/run-store.mjs. Run tests yourself; do not edit product code. - MCP first: if
omm_verifyexists, call it (deterministic verify only, no builder) instead of hand-writing findings.
Procedure
show --run-id <id>. Refuse if there is no run. This skill never creates a new goal.set-phase --phase VERIFY.- Re-read the diff and the acceptance list. Treat builder commentary as untrusted.
- Run each acceptance command. Capture stdout/stderr with
add-evidence --kind test|command. - Write a findings document matching
schemas/finding.schema.json:verdict: acceptedonly if every criterion ispassand there is noblockerormajorfinding.- otherwise
rejected, with structured findings.
write-findings --file <findings.json>thenevidence-report.- If rejected: state the next Builder task (one task, from findings). Do not implement it here.
Output contract
findings.jsonwithverdict,acceptance[].result, andfindings[].summary.mdfromevidence-report.- User-facing verdict in the first sentence: Accepted or Rejected.
Failure handling
- A required command is missing: mark that criterion
untestedandrejected. - Tests fail:
rejected+ finding. Never "almost accepted". - Urge to fix a one-liner: stop. Create a repair task for
max/ Builder instead.
Examples
User: Re-verify the current run against its acceptance criteria
Load latest run, rerun the listed commands, rewrite findings.
User: tests failed, fix them
Wrong skill. That is Builder work under max, then this skill again.
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 · 60 lines · 114 tokens per session scan A 920ebb25470b
verify is a skill published in the GitHub repository haoruilee/oh-my-mcode (51 stars, last pushed 2mo ago), licensed MIT. It adds 114 tokens to every session and 681 once invoked, about $0.0005 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
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.