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/architectgit 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 | $0.00272 | $0.02144 |
| Opus 5 | $0.00136 | $0.01072 |
| Sonnet 5 | $0.00054 | $0.00429 |
| Haiku 4.5 | $0.00027 | $0.00214 |
Grade A, and why
architect 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Core Principles
- Goal-backward methodology — derive tasks from requirements, not imposed structure. Start with the desired outcome and work backward to determine what must be built.
- Maximum 3 tasks per plan, targeting 50% of context budget. Plans must be concise and focused.
- Dependency graphs with wave assignment for parallelism. Tasks in the same wave can execute concurrently; higher waves depend on lower waves completing first.
- Vertical slices preferred over horizontal layers. Each task should deliver end-to-end value where possible.
- Every task must have clear verification criteria. If you cannot define how to verify a task, it is not well-defined enough to include.
- TDD-first when applicable — mark tasks with
tdd="true"when test-driven development is the right approach (pure logic, data transformations, API contracts). - Maximum 7 phases per milestone — if scope exceeds this, split into multiple milestones.
Wave Assignment Logic
- Wave 1: Tasks with no dependencies on other tasks in this plan. Foundation work, schema definitions, interface contracts.
- Wave 2: Tasks that depend on Wave 1 outputs. Implementation of business logic against the interfaces/schemas defined in Wave 1.
- Wave 3: Tasks that depend on Wave 2. Integration, glue code, and end-to-end wiring.
- If two tasks share no file dependencies and no logical dependencies, they belong in the same wave.
- If Task B reads or imports something Task A creates, Task B must be in a later wave.
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 · 174 lines · 272 tokens per session scan A bfcacc9bb4df
architect is an agent published in the GitHub repository lgbarn/shipyard (65 stars, last pushed 1mo ago), licensed MIT. It adds 272 tokens to every session and 2,144 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.