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/lgbarn/shipyard/verifygit clone --depth 1 https://github.com/lgbarn/shipyardWhat 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 | $0.00014 | $0.00557 |
| Opus 5 | $0.00007 | $0.00279 |
| Sonnet 5 | $0.00003 | $0.00111 |
| Haiku 4.5 | $0.00001 | $0.00056 |
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 2d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/shipyard:verify - On-Demand Verification
You are executing on-demand verification. Follow these steps precisely.
Step 1: Parse Arguments
Extract from the command:
criteria(optional): What to verify. Accepts:- No argument: Run project test suite and report results
- File path (e.g.,
acceptance-criteria.md): Verify each criterion in the file - Phase number (e.g.,
3): Verify phase success criteria from ROADMAP.md - Inline criteria (e.g.,
"all API endpoints return JSON"): Verify specific criteria
Step 2: Detect Context
- Check if
.shipyard/exists (optional — this command works anywhere). - If
.shipyard/config.jsonexists, readmodel_routing.validationfor model selection. - Otherwise, use default model: haiku.
- Follow Worktree Protocol (see
docs/PROTOCOLS.md) — detect worktree context.
Step 3: Build Agent Context
Assemble context per Agent Context Protocol (see docs/PROTOCOLS.md):
- The criteria to verify from Step 1
.shipyard/PROJECT.md(if exists).shipyard/ROADMAP.md(if phase number provided)- Test framework configuration (package.json scripts, Makefile targets, etc.)
- Working directory, current branch, and worktree status
Step 4: Dispatch Verifier
Dispatch a verifier agent (subagent_type: "shipyard:verifier") with:
- Follow Model Routing Protocol — resolve model from
model_routing.validation(default: haiku) - max_turns: 15
- All context from Step 3
- Instruction: For each criterion, identify how to verify it (test command, code inspection, manual check), execute verification, and record PASS or FAIL with concrete evidence
Step 5: Present Results
Display the verification report to the user.
If failures exist, offer follow-up:
"Would you like me to:
- Fix the failing criteria
- Run a more detailed review (
/shipyard:review)- Check security on the verified code (
/shipyard:audit)"
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.
- 2d ago First seen · 65 lines · 14 tokens per session scan A 61ee98ac6599
verify is a command published in the GitHub repository lgbarn/shipyard (65 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 557 once invoked, about $0.0001 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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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.