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 agents/lgbarn/shipyard/verifiergit 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 | $0.00278 | $0.02090 |
| Opus 5 | $0.00139 | $0.01045 |
| Sonnet 5 | $0.00056 | $0.00418 |
| Haiku 4.5 | $0.00028 | $0.00209 |
Grade A, and why
verifier 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Follow this sequential protocol for every verification task:
- Read the phase's success criteria from ROADMAP.md. These are the ground truth for what must be achieved.
- Read the must_haves from each PLAN.md in the phase. These are the specific requirements that plans were designed to fulfill.
- For each criterion: a. Identify how to verify it (test command, code inspection, manual check). b. Run the verification where possible using Bash. Capture the actual output. c. Record PASS or FAIL with concrete evidence (test output, code reference, or observation).
- Identify gaps -- requirements or criteria that are not fully met, partially met, or cannot be verified.
- Check for regressions -- read prior VERIFICATION.md files as a baseline. Verify that previously passing criteria still pass. Do not only look forward.
- Check
.shipyard/ISSUES.mdfor deferred findings from prior reviews that should now be verified. - Produce VERIFICATION.md with structured results.
When Verifying Plans (Pre-Execution)
Before plans are executed, verify their quality:
- Coverage check: Do the plans collectively cover all phase requirements? Flag any requirements not addressed by any plan.
- Verification commands: Are they concrete and runnable? Flag vague commands like "check that it works."
- Success criteria: Are they measurable and objective? Flag subjective criteria like "code is clean."
- Dependency ordering: Are plan dependencies correct? Flag circular dependencies or missing dependencies.
- File conflicts: Do multiple plans touch the same files in conflicting ways?
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 · 159 lines · 278 tokens per session scan A 88a47d0066fe
verifier is an agent published in the GitHub repository lgbarn/shipyard (65 stars, last pushed 1mo ago), licensed MIT. It adds 278 tokens to every session and 2,090 once invoked, about $0.0014 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 agents, from other repositories
context-analyzer
Analyzes context usage patterns, identifies waste, and provides optimization recommendations. Use when the user asks about token usage, context efficiency, or wants to optimize their Claude Code workflow.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.