Coding Agent Task Spec Prompt
Writes the task brief that keeps a coding agent inside scope.
/ quick answer
Use to convert a loose request into a spec an agent can execute and you can verify. Writes the task brief that keeps a coding agent inside scope.
Write a task specification for a coding agent working in an existing repository. Include:
1. GOAL — one sentence, outcome not implementation.
2. ACCEPTANCE CRITERIA — checkable statements, including the command that must pass.
3. SCOPE — files and modules it may change; explicitly list what it must NOT touch.
4. CONTEXT — existing patterns, types and conventions it must follow.
5. NON-GOALS — refactors and improvements it should deliberately skip.
6. VERIFICATION — the exact command to run and what output proves success.
7. RISKS — what could break elsewhere and how to check.
Keep the diff under 400 lines. If the task is larger, split it into ordered sub-tasks.
Request: {{REQUEST}}
Stack: {{STACK}}
Repo conventions: {{CONVENTIONS}}1. GOAL — Users can filter the orders table by status without a page reload. 2. ACCEPTANCE — `bun test` passes...
/ frequently asked
What does the Coding Agent Task Spec Prompt prompt do?
Use to convert a loose request into a spec an agent can execute and you can verify.
Which AI models work with this prompt?
It is model-agnostic: it works with any capable general model. Replace the bracketed variables with your own context before running it.
How do I use this prompt?
1) Copy the prompt with the copy button. 2) Replace every bracketed variable with your real details. 3) Paste it into your AI assistant. 4) Compare the answer with the example output and ask for changes if needed.
What output should I expect?
1. GOAL — Users can filter the orders table by status without a page reload. 2. ACCEPTANCE — `bun test` passes...
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Coding Agent
A coding agent reads a repository, plans a change, edits files, runs tests and iterates until the task passes — instead of just suggesting snippets.
- →AI Software Engineering
AI software engineering is the practice of building software where agents write most of the code and humans own architecture, review and verification.
- →Context Engineering
Context engineering is the discipline of deciding exactly what information enters a model's context window, in what order and at what cost.
Related workflows
Turn this into a repeatable process.
- →Run a Coding Agent on a Real Codebase Safely
Give an agent write access without letting it wreck main.
- →Automate Agent Task Delegation
This workflow automates the process of an initiating AI agent delegating a complex task to a specialized recipient AI agent using a standardized Agent-to-Agent (A2A) protocol, ensuring efficient task handoff and execution. It streamlines multi-agent collaboration for defined objectives.
Related tool stacks
The tools that run it in production.
- →Coding Agent Stack
Run coding agents with executable feedback and reviewable diffs.
Related prompts
Reusable prompts for this job.
- →Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
- →Browser Automation Task Spec Prompt
Converts a manual click-path into a reliable automation spec.
- →AI Code Review Rubric Prompt
A reviewer prompt that finds real defects instead of style opinions.
- →AI Test Generation Prompt
Generates behavioural tests and edge cases, not tautologies.
Related use cases
How people apply it, and what came out.
- →Two-Founder Startup Ships Like a Team of Six
Coding agents plus a hard verification gate replaced three planned hires.
Comparisons & alternatives
Pick between the options.
- →Coding Agent vs Autocomplete Copilot
Copilots accelerate typing; coding agents complete tasks. Different unit of work, different review burden.
- →Claude Code vs Cursor: Which Coding Agent to Use
Terminal-native agent vs IDE-native pair programmer — different jobs, both worth using.