addyosmani/agent-skills is a collection of reusable workflows, quality checks, commands, and other instructions that guide AI coding agents through software development. It is for developers who want agents to follow consistent engineering practices, and the catalogue entries are its packaged skills, commands, agents, plugins, instructions, and hooks.
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/addyosmani/agent-skills/incremental-implementationnpx skills add addyosmani/agent-skills --skill incremental-implementationgit clone --depth 1 https://github.com/addyosmani/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/skills/addyosmani/agent-skills/incremental-implementation)<a href="https://agentmods.dev/skills/addyosmani/agent-skills/incremental-implementation"><img src="https://agentmods.dev/badge/skills/addyosmani/agent-skills/incremental-implementation.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 | $0.00052 | $0.02041 |
| Opus 5 | $0.00026 | $0.01020 |
| Sonnet 5 | $0.00010 | $0.00408 |
| Haiku 4.5 | $0.00005 | $0.00204 |
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 5d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- incremental-implementation — 100% identical, 1 lines differ
- incremental-implementation — 94% identical, 16 lines differ
- incremental-implementation — 92% identical, 10 lines differ
- incremental-implementation — 89% identical, 18 lines differ
- incremental-implementation — 89% identical, 18 lines differ
- incremental-implementation — 89% identical, 19 lines differ
- incremental-implementation — 89% identical, 18 lines differ
- incremental-implementation — 89% identical, 18 lines differ
How it starts
The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incremental Implementation
Overview
Build in thin vertical slices — implement one piece, test it, verify it, then expand. Avoid implementing an entire feature in one pass. Each increment should leave the system in a working, testable state. This is the execution discipline that makes large features manageable.
When to Use
- Implementing any multi-file change
- Building a new feature from a task breakdown
- Refactoring existing code
- Any time you're tempted to write more than ~100 lines before testing
When NOT to use: Single-file, single-function changes where the scope is already minimal.
The Increment Cycle
┌──────────────────────────────────────┐
│ │
│ Implement ──→ Test ──→ Verify ──┐ │
│ ▲ │ │
│ └───── Commit ◄─────────────┘ │
│ │ │
│ ▼ │
│ Next slice │
│ │
└──────────────────────────────────────┘
For each slice:
- Implement the smallest complete piece of functionality
- Test — run the test suite (or write a test if none exists)
- Verify — confirm the slice works as expected (tests pass, build succeeds, manual check)
- Commit -- save your progress with a descriptive message (see
git-workflow-and-versioningfor atomic commit guidance) - Move to the next slice — carry forward, don't restart
Slicing Strategies
Vertical Slices (Preferred)
Build one complete path through the stack:
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
Each slice delivers working end-to-end functionality.
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.
- 5d ago First seen · 250 lines · 52 tokens per session scan A 3a3581e7084a
incremental-implementation is a skill published in the GitHub repository addyosmani/agent-skills (92,284 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 2,041 once invoked, about $0.0003 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-output-validation
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and fallback handling.
ai-ops
Guides operational excellence for AI/ML systems in production. Use when deploying models, managing inference infrastructure, monitoring model drift, or maintaining AI-powered features. Use when you need reliable, observable, and governable machine learning systems.
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.