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 skills/wrannaman/agentic-engineering/shipnpx skills add wrannaman/agentic-engineering --skill shipgit clone --depth 1 https://github.com/wrannaman/agentic-engineeringWhat 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 | $0.00038 | $0.01128 |
| Opus 5 | $0.00019 | $0.00564 |
| Sonnet 5 | $0.00008 | $0.00226 |
| Haiku 4.5 | $0.00004 | $0.00113 |
Grade A, and why
ship 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ship
Plan it. Build it. Review it. Fix it. Push it. One command.
Purpose
Runs the full cycle — plan, implement, review, fix, push — without stopping for human feedback between steps. For well-understood incremental tasks where you trust the system and just want it done.
This is /plan + /work + /review + /pr-push composed into a single uninterrupted flow. The agent makes all intermediate decisions. You review the final PR on GitHub.
Usage
/ship "add a /health endpoint that returns 200 with {status: ok}"
/ship "fix the date formatting bug in the orders page — dates show UTC instead of local time"
/ship "add pagination to the /api/users endpoint using cursor-based pagination"
When to Use
- Well-scoped incremental tasks — new endpoint, bug fix, small feature
- Tasks that follow existing patterns — the KB has examples of similar work
- When you trust the compound loop — the KB is well-seeded, the one-shot rate is high
- When you want to review the PR, not babysit the process
When NOT to Use
- Novel architecture — use
/brainstormthen/planseparately - Ambiguous requirements — if you're not sure what you want,
/brainstormfirst - High-risk changes — auth, payments, data migrations — use the manual cycle with human checkpoints
- Your first week — until the KB is seeded and you trust the system, use the manual cycle
Process
Step 1: Plan (Silent)
Run the /plan skill's process internally:
- Load KB context and learnings
- Research codebase with parallel sub-agents (existing types, similar implementations, test patterns)
- Design implementation steps with verification strategy
- Define PR stack boundaries (single PR for
/shipunless the task clearly needs multiple)
Do NOT ask the user questions. Make your best judgment call on any decision points. If something is genuinely ambiguous (two equally valid approaches with different trade-offs), pick the simpler one.
Save the plan to .plans/ as usual — it's useful for the compound loop later even if the user never reads it.
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 · 130 lines · 38 tokens per session scan A 42c9db69e4be
ship is a skill published in the GitHub repository wrannaman/agentic-engineering (2 stars, last pushed 4mo ago), licensed MIT. It adds 38 tokens to every session and 1,128 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…