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/spec-driven-developmentnpx skills add addyosmani/agent-skills --skill spec-driven-developmentgit 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/spec-driven-development)<a href="https://agentmods.dev/skills/addyosmani/agent-skills/spec-driven-development"><img src="https://agentmods.dev/badge/skills/addyosmani/agent-skills/spec-driven-development.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 | $0.00067 | $0.02586 |
| Opus 5 | $0.00034 | $0.01293 |
| Sonnet 5 | $0.00013 | $0.00517 |
| Haiku 4.5 | $0.00007 | $0.00259 |
Grade A, and why
spec-driven-development 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
1 near-identical copy found in the catalogue:
- spec-driven-development — 97% identical, 1 lines differ
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.
Spec-Driven Development
Overview
Write a structured specification before writing any code. The spec is the shared source of truth between you and the human engineer — it defines what we're building, why, and how we'll know it's done. Code without a spec is guessing.
When to Use
- Starting a new project or feature
- Requirements are ambiguous or incomplete
- The change touches multiple files or modules
- You're about to make an architectural decision
- The task would take more than 30 minutes to implement
When NOT to use: Single-line fixes, typo corrections, or changes where requirements are unambiguous and self-contained.
The Gated Workflow
Spec-driven development has four phases, preceded by a scope check (Phase 0) that activates only when one request bundles several independently testable capabilities. Do not advance to the next phase until the current one is validated.
SPECIFY ──→ PLAN ──→ TASKS ──→ IMPLEMENT
│ │ │ │
▼ ▼ ▼ ▼
Human Human Human Human
reviews reviews reviews reviews
Phase 0: Scope Check
Most requests describe one capability. If this one does, skip this phase and go straight to Specify — Phase 0 exists for the exception, not the rule, and it puts no hierarchy on single-capability features.
Detection. Decompose before specifying when a single requirement bundles several independently testable capabilities:
- The requirement names distinct capabilities with their own consumers or data (e.g. identity, billing, notifications, reporting)
- Acceptance criteria cluster into groups that could ship and be verified separately
- One capability could be cut or replaced without rewriting the others' requirements
Propose a capability map before writing any spec. Small and reviewable — a module table plus a build order, not a project plan:
# Capability Map: [Initiative Name]
| Module id | Responsibility | Depends on |
|---|---|---|
| identity | Accounts, sessions, SSO | — |
| billing | Plans, invoices, payments | identity |
| notifications | Email and webhook fan-out | identity |
| reporting | Usage dashboards | billing, notifications |
Build order: identity → billing, notifications → reporting
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 · 246 lines · 67 tokens per session scan A 615ff006266a
spec-driven-development is a skill published in the GitHub repository addyosmani/agent-skills (92,284 stars, last pushed yesterday), licensed MIT. It adds 67 tokens to every session and 2,586 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.