define-project

define-project is a skill for Claude Code, Codex from ryan-scheinberg/harness. It costs 67 tokens per session (573 once invoked), scanned A, original, MIT.

A planning skill that turns a short project idea into a detailed PROJECTBRIEF.md file. The brief describes the goal, scope, features, technical choices, and known risks so another developer or agent can build it.

In plain words
What is it for?
Use it to define a new product, plan a feature, scope a bug fix, or prepare a project brief for implementation.
Why use it?
It removes the guesswork and missing decisions that often make project requests unclear. It also uses the existing codebase to match its tools and conventions.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/ryan-scheinberg/harness/define-project
Any agent
npx skills add ryan-scheinberg/harness --skill define-project
Clone the repo
git clone --depth 1 https://github.com/ryan-scheinberg/harness

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for define-project

README.md
[![agentmods](https://agentmods.dev/badge/skills/ryan-scheinberg/harness/define-project.svg)](https://agentmods.dev/skills/ryan-scheinberg/harness/define-project)
Your own site
<a href="https://agentmods.dev/skills/ryan-scheinberg/harness/define-project"><img src="https://agentmods.dev/badge/skills/ryan-scheinberg/harness/define-project.svg" alt="Measured on agentmods" height="20"></a>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 573 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00067 $0.00573
Opus 5 $0.00034 $0.00287
Sonnet 5 $0.00013 $0.00115
Haiku 4.5 $0.00007 $0.00057

Measured 3d ago against content hash de8b5a33066b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

define-project 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 3d 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.

skills/project-skillset/define-project/SKILL.md · 55 lines

How it starts

The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Produce a PROJECT_BRIEF.md proficient agents can pick up and build from without clarifying questions. Terse, opinionated, decisions made not deferred. The user's persona and preferences already live in CLAUDE.md — don't restate them

Process

  1. Read the user's input. Infer what you can as an architect. iterate-plan closes the rest after the first draft
  2. Explore the codebase if one exists starting with AGENTS.md files. Tech stack, test setup, CI, module boundaries, naming conventions. Don't ask what code can answer
  3. Decide. Pick the stack and scope the MVP — the thinnest end-to-end vertical path that proves the approach. Genuine uncertainty goes in Risks & Open Questions. Default to deciding. Hedged briefs ship ambiguity forward into dev turns. Don't name downstream slices here — that's plan-to-slices' job, and pre-slicing the brief locks in a shape before the MVP has taught you anything
  4. Write the brief using the template. Adapt freely — drop sections that don't apply, add sections when the project needs them. A bug fix doesn't need a business model; a SaaS needs all of it

Template

[Project Name]

Context

What this is, why it exists, the problem it solves. No throat-clearing

Audience

Who uses or benefits. For internal or infra work this may be "the deployment pipeline" or "future contributors to this repo"

Scope

The MVP Slice

One sentence: "A user/system can [do X] and [see/get Y]"

In Scope

  • ...

Out of Scope

  • What's excluded and why

Technical Approach

Stack, architecture, data, auth, infra with rationale. For existing codebases: what changes, what doesn't

Testing & Observability

What gets tested and instrumented. Structured logging, key metrics, health checks. What tells you this is working or broken in prod

Deployment & Rollout

Environments, rollout strategy, flags, rollback. Skip for non-deployable work

Risks & Open Questions

What's uncertain. Each item notes what would close it

Read the full file on GitHub · 55 lines

Changes

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.

  1. 3d ago First seen · 55 lines · 67 tokens per session scan A de8b5a33066b

Subscribe to this mod's changes

define-project is a skill published in the GitHub repository ryan-scheinberg/harness (2 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 573 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 2026-08-31.

Related

Other skills, from other repositories

fablize

A harness that makes Opus (or any Claude model) behave like Fable — it enforces seeing a task through to the end, with evidence and verification, as procedure. Use when starting a multi-step task (2+ sequential stories), long autonomous work, debugging or root-cause investigation, building render/executable artifacts…

fivetaku/fablize · 104 tokens

api-gateway

Call third-party APIs through the Maton gateway, which injects the credential for an app the user has already connected. Use this skill when the user names a connected app and a concrete action in it - read a mailbox, query a CRM, file an issue, update a spreadsheet, run a query through a connected search or scraping…

maton-ai/api-gateway-skill · 173 tokens

owner-facing-reporting

Use for every progress update, status report, problem explanation, ETA, review, acceptance, or completion report to the operator/owner, especially when they ask “到哪了”, “什么问题”, “做了什么”, “结果呢”, “还要多久”, or “说人话”. Translate technical evidence into visible or operational outcomes before details.

ConnorRX56/presentation-delivery-skills · 76 tokens

human-intent-audit

Enforce traceability from natural-language intent to machine predicates, final observables, evidence, and owner-readable acceptance. Use when conducting any audit, review, QA pass, parity or compliance check, security assessment, code or architecture review, visual or behavioral comparison, data validation, system…

ConnorRX56/presentation-delivery-skills · 77 tokens

visual-deck-studio

视觉演示工坊——用HTML做高保真原型、幻灯片、动画、可视化与专家评审,需求模糊时给设计方向。触发词:做原型、PPT、幻灯片、动画、设计风格、评审、做个HTML页面、UI mockup、导出MP4/GIF、做个好看的。生产级Web App/需后端的系统不适用。.

ConnorRX56/presentation-delivery-skills · 103 tokens

conducting-scientific-research

Conduct rigorous, reproducible multi-step scientific work with literature, databases, local files, Python, R, shell, artifacts, reviewers, and approved compute. Use for evidence synthesis, data or statistical analysis, machine learning, simulation, study design, scientific figures or manuscripts, reproduction audits…

Shoko-official/Claude-Science-System-Prompts · 99 tokens