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.
git 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/commands/lgbarn/shipyard/debug)<a href="https://agentmods.dev/commands/lgbarn/shipyard/debug"><img src="https://agentmods.dev/badge/commands/lgbarn/shipyard/debug/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/commands/lgbarn/shipyard/debug"><img src="https://agentmods.dev/badge/commands/lgbarn/shipyard/debug.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00011 | $0.00640 |
| Opus 5.5 | $0.00004 | $0.00256 |
| Sonnet 5.5 | $0.00002 | $0.00128 |
| Haiku 4.5 | $0.00001 | $0.00064 |
Grade A, and why
debug 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.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/shipyard:debug - Root Cause Analysis
You are executing the Shipyard debugging workflow. This dispatches a dedicated debugger agent for systematic root-cause investigation.
Step 1: Parse Arguments
- If an error description is provided, use it as the problem statement.
- If no argument is provided, ask the user:
"What issue are you investigating? Provide the error message, failing test, or describe the unexpected behavior."
Step 2: Gather Context
- Read
.shipyard/STATE.jsonif it exists (for current phase context) - Read
.shipyard/config.jsonfor model routing - Follow Worktree Protocol (detect if running in a git worktree; if so, use worktree root for paths and record the branch name; see
docs/PROTOCOLS.md) - Follow Model Routing Protocol (select the correct model for the debugger role using
model_routing.debuggingfrom config; seedocs/PROTOCOLS.md)
Step 3: Dispatch Debugger
Dispatch a debugger agent (subagent_type: "shipyard:debugger") following the Model Routing Protocol with:
- The problem statement (error description, test name, or unexpected behavior)
- Working directory, current branch, and worktree status
- Codebase docs per Codebase Docs Protocol (resolve configured codebase docs path and load CONVENTIONS.md, STACK.md, ARCHITECTURE.md, etc.; see
docs/PROTOCOLS.md) .shipyard/PROJECT.mdif it exists- Recent git log (
git log --oneline -20) - Any error output or stack traces the user provided
The debugger agent performs:
- Root cause investigation (reproduce, trace, gather evidence)
- Pattern analysis (compare working vs broken code)
- Hypothesis testing (minimal changes, one variable at a time)
- Remediation plan (documented steps for the builder)
Step 4: Present Results
Read the debugger's ROOT-CAUSE.md output and present to the user:
- Root cause summary
- Evidence chain
- Remediation plan
Step 5: Route Forward
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 · 69 lines · 11 tokens per session scan A 322b630b1394
debug is a command published in the GitHub repository lgbarn/shipyard (65 stars, last pushed 8d ago), licensed MIT. It adds 11 tokens to every session and 640 once invoked, about $0.0000 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 commands, from other repositories
crystallize
After a session or an incident, turn what happened into reusable knowledge — learn captures this session's lessons, review/approve promotes incident patterns into agent improvements, skill turns a repeating procedure into a skill.
debug
Systematic debugging: root cause before any fix, one hypothesis at a time, and a three-strikes rule that questions the architecture instead of stacking patches.
analyze-misfires
Identify skills injected where not needed, propose regex and description tightening.
bugfix
TDD-driven bugfix workflow: tester writes failing test (RED) → developer fixes (GREEN) → developer refactors (REFACTOR) → reviewer validates. Accepts issue number, description, or both. Asks before pushing and creating a PR; --no-pr commits locally without asking.
dead-code-scan
Scan for dead code, unused imports, duplicates, and zombie code across the project.
fec-tdd
Use front-end TDD workflow to implement functions, fix bugs, or refactor logic: first write failing tests, then implement minimal code, and then refactor.