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 skills add vinvcn/addyosmani-agent-skills-zh --skill incremental-implementationgit clone --depth 1 https://github.com/vinvcn/addyosmani-agent-skills-zhWrote 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/skills/vinvcn/addyosmani-agent-skills-zh/incremental-implementation)<a href="https://agentmods.dev/skills/vinvcn/addyosmani-agent-skills-zh/incremental-implementation"><img src="https://agentmods.dev/badge/skills/vinvcn/addyosmani-agent-skills-zh/incremental-implementation/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/skills/vinvcn/addyosmani-agent-skills-zh/incremental-implementation"><img src="https://agentmods.dev/badge/skills/vinvcn/addyosmani-agent-skills-zh/incremental-implementation.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 | $0.00046 | $0.02160 |
| Opus 5 | $0.00023 | $0.01080 |
| Sonnet 5 | $0.00009 | $0.00432 |
| Haiku 4.5 | $0.00005 | $0.00216 |
Grade A, and why
incremental-implementation 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 11d 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incremental Implementation
概览
用薄的垂直切片构建:实现一小块,测试它,验证它,然后再扩展。避免一次性实现整个功能。每个增量都应该让系统保持可工作、可测试的状态。这是让大型功能可管理的执行纪律。
何时使用
- 实现任何多文件变更
- 从任务拆解中构建新功能
- 重构现有代码
- 任何你想在测试前写超过约 100 行代码的时候
何时不要使用: 范围已经最小化的单文件、单函数变更。
增量循环
┌──────────────────────────────────────┐
│ │
│ Implement ──→ Test ──→ Verify ──┐ │
│ ▲ │ │
│ └───── Commit ◄─────────────┘ │
│ │ │
│ ▼ │
│ Next slice │
│ │
└──────────────────────────────────────┘
对每个切片:
- Implement 最小的完整功能片段
- Test:运行测试套件(或在没有测试时编写测试)
- Verify:确认切片按预期工作(测试通过、构建成功、手动检查)
- Commit:用描述性消息保存进度(原子提交指导见
git-workflow-and-versioning) - Move to the next slice:继续推进,不要从头开始
切片策略
垂直切片(优先)
构建一条贯穿整个技术栈的完整路径:
Slice 1: Create a task (DB + API + basic UI)
→ Tests pass, user can create a task via the UI
Slice 2: List tasks (query + API + UI)
→ Tests pass, user can see their tasks
Slice 3: Edit a task (update + API + UI)
→ Tests pass, user can modify tasks
Slice 4: Delete a task (delete + API + UI + confirmation)
→ Tests pass, full CRUD complete
每个切片都交付可工作的端到端功能。
Contract-First Slicing
当后端和前端需要并行开发时:
Slice 0: Define the API contract (types, interfaces, OpenAPI spec)
Slice 1a: Implement backend against the contract + API tests
Slice 1b: Implement frontend against mock data matching the contract
Slice 2: Integrate and test end-to-end
Risk-First Slicing
先处理风险最高或最不确定的部分:
Slice 1: Prove the WebSocket connection works (highest risk)
Slice 2: Build real-time task updates on the proven connection
Slice 3: Add offline support and reconnection
如果 Slice 1 失败,你会在投入 Slice 2 和 3 前发现。
实现规则
规则 0:简单优先
写任何代码之前,先问:“最简单能工作的东西是什么?”
写完代码后,用这些检查审视它:
- 能用更少代码完成吗?
- 这些抽象值得它们带来的复杂度吗?
- staff engineer 看了会不会说“为什么不直接……”?
- 我是在为假想的未来需求构建,还是为当前任务构建?
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.
- 11d ago First seen · 246 lines · 46 tokens per session scan A b54aa6d7e12e
incremental-implementation is a skill published in the GitHub repository vinvcn/addyosmani-agent-skills-zh (30 stars, last pushed 4mo ago), licensed MIT. It adds 46 tokens to every session and 2,160 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 2026-08-30.
Other skills, from other repositories
test-driven-development
Drives development with tests via Red-Green-Refactor and the Prove-It pattern, with hard rules against weakening assertions or faking green suites. Use when implementing any logic, fixing any bug, or changing any behavior. Triggers on "add a feature", "fix this bug", "write tests", or any task where done must be…
ai-ops
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chaos-engineering
Guides systematic fault injection and resilience testing. Use when designing for high availability, verifying disaster recovery, testing failure modes, or building fault-tolerant systems. Use when you need to prove your system survives infrastructure failures, network partitions, dependency outages, or cascading…
ci-cd-and-automation
Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
data-engineering
Guides data pipeline design, ETL/ELT workflows, schema evolution, and data quality assurance. Use when building data pipelines, designing data warehouses, migrating schemas, or ensuring data integrity across systems. Use when you need reliable, testable, and observable data flows.