Task brief for Claude Code: frame a feature end to end
Brief template to delegate a complete feature to Claude Code: goal, starting point, constraints, definition of done, working rules and recap format.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
You are Claude Code and you will implement a feature end to end in this repository. Here is the brief; read it fully, then start by presenting a 5-to-10-step plan and wait for my approval before writing code.
Goal (one sentence, from the user's point of view):
[GOAL]
Why now:
[BUSINESS CONTEXT IN 2-3 LINES]
Starting point:
- Files or modules involved: [PATHS]
- Current behaviour: [WHAT HAPPENS TODAY]
- Concrete example of input → expected output: [EXAMPLE]
Constraints:
- Technical: [e.g. no new dependency, strict TypeScript, compatible with migration X]
- Product: [e.g. FR + EN, keyboard accessible, no public API change]
- Do not touch: [FORBIDDEN FILES / FOLDERS]
Definition of done (all must be true):
- [VERIFIABLE CRITERION, e.g. test T passes]
- [CRITERION, e.g. build passes with no new warning]
- [CRITERION, e.g. a test covers edge case Y]
- [CRITERION, e.g. doc D is updated]
Working rules:
- Write or adapt tests before the implementation when possible.
- Small commits, one per plan step.
- If you find the brief incomplete or contradictory, stop and ask a one-line question; do not guess.
- If you see another problem in the code, note it at the end, do not fix it.
At the end, return a recap: files changed (exhaustive list), what changed per file (one line), what you deliberately left out, and what remains to do.
Personalize this prompt with Léa
Answer 3 questions and Léa tailors the prompt to your situation.
Why this prompt works
<p>The quality of a task delivered by Claude Code depends less on the model than on the <strong>brief</strong>. An autonomous agent told "add CSV export" will guess the format, the localisation and the scope — and get at least one of the three wrong. This template forces the four pieces of information that remove back-and-forth: a concrete input → output example, the areas not to touch, a verifiable definition of done, and the instruction to stop when in doubt rather than improvise.</p> <p>Requiring a validated plan before code is deliberate: it is the cheapest moment to correct a misreading. The mandatory final recap (files, changes, left out, remaining) makes review fast and doubles as the pull-request description.</p> <p>This brief works as-is with Codex or any code agent that accepts a long instruction; only the first line changes.</p>
Use Cases
Expected Output
First a 5-to-10-step plan submitted for approval; then the implementation in small commits; finally a four-part recap (files, changes, left out, remaining).
Frequently Asked Questions
Is an input → output example really necessary?
It is the most profitable line of the brief. One example lifts more ambiguity than three paragraphs of description, and it naturally becomes the first test.
What if the task is too big for one brief?
Split by verifiable deliverable (a migration, then the API, then the UI) and chain three briefs. An agent ships three two-hour tasks better than one six-hour task.
Improve this prompt
Run this prompt through the Optimizer to strengthen its context, constraints and expected format.
Improve this prompt with the OptimizerComments
Be the first to comment on this prompt.
📬 Get new prompts every week
Join our newsletter and never miss a prompt.
Go further
Similar Prompts
Master Git Reset, Rebase, and Cherry-Pick
Master advanced Git commands (reset, rebase, cherry-pick, stash) with clear diagrams and practical scenarios.
Implement Kubernetes network policies
Secure Kubernetes networking
Perplexity Prompt for Generating JavaScript Code
Perplexity stands out from other AI assistants by its unique ability to combine real-time web search and code generation. When you ask it to produce JavaScript code, it doesn't just rely on static knowledge: it consults official documentation, recent GitHub repositories, and current technical discussions to provide you with up-to-date and functional code. This approach is especially valuable in JavaScript, an ecosystem where best practices, browser APIs, and conventions evolve rapidly. Where a classic LLM might suggest outdated syntax or a deprecated method, Perplexity checks its sources and cites the references used. Whether you need a React component, a Node.js utility function, or a vanilla DOM manipulation, crafting a structured prompt with the right technical context ensures a result that is immediately usable. This guide shows you how to get the most out of Perplexity for your JavaScript code generation needs, from simple scripts to complete modular architectures.
Copy Variations for Different Brand Tones
Brand voice testing, A/B tests