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/researchgit 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 | $0.00012 | $0.00551 |
| Opus 5 | $0.00006 | $0.00275 |
| Sonnet 5 | $0.00002 | $0.00110 |
| Haiku 4.5 | $0.00001 | $0.00055 |
Grade A, and why
research 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 3d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/shipyard:research - On-Demand Domain Research
You are executing on-demand domain research. Follow these steps precisely.
Step 1: Parse Arguments
Extract from the command:
topic(REQUIRED): The research topic. Examples:"React vs Vue for dashboard"— technology comparison"best Postgres connection pooling library"— ecosystem investigation"authentication approaches for REST API"— architectural research"gRPC vs REST for microservices"— protocol comparison
If no topic is provided, ask the user what they want to research.
Step 2: Detect Context
- Check if
.shipyard/exists (optional — this command works anywhere). - If
.shipyard/config.jsonexists, readmodel_routing.planningfor model selection. - Otherwise, use default model: sonnet.
- 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 research topic from Step 1
.shipyard/PROJECT.md(if exists) — for project stack context- Codebase docs per Codebase Docs Protocol (if
.shipyard/exists) — especially STACK.md, ARCHITECTURE.md - Working directory and current branch
- Any existing technology choices to consider compatibility with
Step 4: Dispatch Researcher
Dispatch a researcher agent (subagent_type: "shipyard:researcher") with:
- Follow Model Routing Protocol — resolve model from
model_routing.planning(default: sonnet) - max_turns: 15
- All context from Step 3
- Instruction: Investigate the topic thoroughly — identify candidates, deep-dive each via web search and documentation, analyze codebase compatibility, build comparison matrix, formulate recommendation with risks and mitigations
Step 5: Present Results
Display the research document to the user.
Offer follow-up:
"Would you like me to:
- Save this research to
.shipyard/for future reference- Create an implementation plan based on the recommendation
- Research a related topic"
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.
- 3d ago First seen · 67 lines · 12 tokens per session scan A 649fcf0dee5d
research is a command published in the GitHub repository lgbarn/shipyard (65 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 551 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.
Other commands, from other repositories
git
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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.