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/233i/agent-skills/buildgit clone --depth 1 https://github.com/233i/agent-skillsWrote 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/233i/agent-skills/build)<a href="https://agentmods.dev/commands/233i/agent-skills/build"><img src="https://agentmods.dev/badge/commands/233i/agent-skills/build.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 | $0.00018 | $0.00197 |
| Opus 5 | $0.00009 | $0.00098 |
| Sonnet 5 | $0.00004 | $0.00039 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
build 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 4d 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.
What it actually says
调用 agent-skills:incremental-implementation，并同时启用 agent-skills:test-driven-development。
从计划中选择下一个待完成任务。对每个任务：
- 阅读任务的 acceptance criteria
- 加载相关上下文，例如现有代码、已有模式、类型定义
- 先为预期行为写一个失败测试（RED）
- 只实现让测试通过所需的最小代码（GREEN）
- 运行完整测试套件，检查是否有回归
- 运行构建，确认可以编译通过
- 使用清晰的提交信息完成提交
- 将该任务标记为完成，然后进入下一个任务
如果任一步失败，就切换到 agent-skills:debugging-and-error-recovery。
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.
- 4d ago First seen · 19 lines · 18 tokens per session scan A 14826c61ddd7
build is a command published in the GitHub repository 233i/agent-skills (6 stars, last pushed 5mo ago), licensed MIT. It adds 18 tokens to every session and 197 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 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.