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 agentmods add commands/vakovalskii/ontoship/shipgit clone --depth 1 https://github.com/vakovalskii/ontoshipWrote 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/commands/vakovalskii/ontoship/ship)<a href="https://agentmods.dev/commands/vakovalskii/ontoship/ship"><img src="https://agentmods.dev/badge/commands/vakovalskii/ontoship/ship.svg" alt="Measured on agentmods" 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 | $0.00047 | $0.00344 |
| Opus 5 | $0.00023 | $0.00172 |
| Sonnet 5 | $0.00009 | $0.00069 |
| Haiku 4.5 | $0.00005 | $0.00034 |
Grade A, and why
ship 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 5d 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
Drive the change described in $ARGUMENTS through the OntoShip dev-flow.
Follow the dev-flow skill end to end:
- Research — understand from facts (logs, traces, code); reproduce before fixing.
- Tasks — decompose into tracked tasks.
- Goal — crystallize one clear goal + "done" criterion.
- Spec — write it as markdown in the KB via
kb-curate(node_type, frontmatter, typed linksdocuments:[src/…]). Search the KB first (kb-search) — don't duplicate. - Isolate — work in a dedicated
git worktree. - Implement — code to the spec.
- Tests — write/adjust unit + E2E.
- Independent review — run an independent model (e.g. Codex CLI, read-only) over the diff.
- Dev-tests — MR + commits into
dev; run the full suite. Red → fix, don't merge. - Prod-tests — E2E/smoke against the real prod contour.
- Ship — merge
dev → mainand deploy (build-before-stop + healthcheck-poll).
Keep the gates (tests + independent review). Don't skip the spec — it's the carrier of knowledge, not a throwaway ticket.
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.
- 5d ago First seen · 24 lines · 47 tokens per session scan A df4cbf9fa4ef
ship is a command published in the GitHub repository vakovalskii/ontoship (76 stars, last pushed 10d ago), licensed MIT. It adds 47 tokens to every session and 344 once invoked, about $0.0002 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.