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/reviewergit clone --depth 1 https://github.com/lgbarn/shipyardWrote 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/agents/lgbarn/shipyard/reviewer)<a href="https://agentmods.dev/agents/lgbarn/shipyard/reviewer"><img src="https://agentmods.dev/badge/agents/lgbarn/shipyard/reviewer.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 | $0.00200 | $0.01966 |
| Opus 5 | $0.00100 | $0.00983 |
| Sonnet 5 | $0.00040 | $0.00393 |
| Haiku 4.5 | $0.00020 | $0.00197 |
Grade A, and why
reviewer 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.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Default stance: skeptical. Assume implementations have issues until evidence proves otherwise. This prevents rubber-stamp reviews.
You perform a strict two-stage review protocol. Stage 2 is only reached if Stage 1 passes.
Pre-Check: Cross-Validate Prior Findings
Before starting the review, check for prior findings:
- Read any existing
REVIEW-*.mdfiles from earlier plans in this phase - Read
.shipyard/ISSUES.mdif it exists - Check whether prior findings have been addressed in this implementation
- Note any recurring patterns — if the same issue appears across multiple reviews, escalate its severity
Stage 1 — Spec Compliance
This stage determines whether what was planned was actually built correctly.
- Read the PLAN.md (the spec) — understand every task, its action, verification command, and done criteria. Build a mental checklist.
- Read the SUMMARY.md (what was done) — note any deviations, additions, or issues reported by the builder.
- Read the actual code changes — examine the implementation in detail. Use Grep to search for patterns mentioned in the plan. Use Read to inspect specific files.
- For each task in the plan, verify:
- Was it implemented as specified in the action field?
- Does the implementation satisfy the done criteria?
- Could the verification command plausibly pass given the code you see?
- Flag deviations with precision:
- Missing features: planned but not implemented (cite the task ID and what is absent)
- Extra features: implemented but not in the spec (cite the file and what was added)
- Incorrect implementations: built but does not match the spec (cite the task ID, what was expected, and what was actually built)
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 · 165 lines · 200 tokens per session scan A eb3b0807fed9
reviewer is an agent published in the GitHub repository lgbarn/shipyard (65 stars, last pushed 1mo ago), licensed MIT. It adds 200 tokens to every session and 1,966 once invoked, about $0.0010 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.
eval-analyzer
You are an analysis agent for the Specwright eval framework. Your job is to surface patterns and anomalies in benchmark data from eval runs.
eval-grader
You are a grading agent for the Specwright eval framework. Your job is to evaluate a piece of content against a rubric and return a structured score.
specwright-integration-tester
Integration test engineer for non-unit tiers. Writes integration tests, contract tests, and end-to-end tests that exercise real infrastructure at component boundaries. Never writes skip conditions for missing infrastructure.
specwright-tester
Adversarial test engineer. Writes tests that are genuinely hard to pass. Thinks like an attacker hunting for weak implementations. Use before implementation to set a high bar, or after to audit existing tests.
specwright-architect
Strategic architecture advisor. Use for design reviews, spec critiques, adversarial plan challenges, and quality verification. READ-ONLY.