Transform feature descriptions into well-structured project plans following conventions
Instalação
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Transform feature descriptions into well-structured project plans following conventions
argument-hint
[feature description, bug report, or improvement idea]
Create a plan for a new feature or bug fix
Introduction
Note: The current year is 2026. Use this when dating plans and searching for recent documentation.
Transform feature descriptions, bug reports, or improvement ideas into well-structured markdown files issues that follow project conventions and best practices. This command provides flexible detail levels to match your needs.
If the feature description above is empty, ask the user: "What would you like to plan? Please describe the feature, bug fix, or improvement you have in mind."
Do not proceed until you have a clear feature description from the user.
0. Idea Refinement
Check for requirements document first:
Before asking questions, look for recent requirements documents in docs/brainstorms/ that match this feature:
ls -la docs/brainstorms/*-requirements.md 2>/dev/null | head -10
Relevance criteria: A requirements document is relevant if:
The topic (from filename or YAML frontmatter) semantically matches the feature description
Created within the last 14 days
If multiple candidates match, use the most recent one
If a relevant requirements document exists:
Read the source document thoroughly — every section matters
Announce: "Found source document from [date]: [topic]. Using as foundation for planning."
Extract and carry forward ALL of the following into the plan:
Key decisions and their rationale
Chosen approach and why alternatives were rejected
Problem framing, constraints, and requirements captured during brainstorming
Outstanding questions, preserving whether they block planning or are intentionally deferred
Success criteria and scope boundaries
Dependencies and assumptions, plus any high-level technical direction only when the origin document is inherently technical
Skip the idea refinement questions below — the source document already answered WHAT to build
Use source document content as the primary input to research and planning phases
Critical: The source document is the origin document. Throughout the plan, reference specific decisions with (see origin: <source-path>) when carrying forward conclusions. Do not paraphrase decisions in a way that loses their original context — link back to the source.
Do not omit source content — if the source document discussed it, the plan must address it (even if briefly). Scan each section before finalizing the plan to verify nothing was dropped.
If Resolve Before Planning contains any items, stop. Do not proceed with planning. Tell the user planning is blocked by unanswered brainstorm questions and direct them to resume /ce:brainstorm or answer those questions first.
If multiple source documents could match:
Use AskUserQuestion tool to ask which source document to use, or whether to proceed without one.
If no requirements document is found (or not relevant), run idea refinement:
Refine the idea through collaborative dialogue using the AskUserQuestion tool:
Ask questions one at a time to understand the idea fully
Prefer multiple choice questions when natural options exist
Focus on understanding: purpose, constraints and success criteria
Continue until the idea is clear OR user says "proceed"
Gather signals for research decision. During refinement, note:
User's familiarity: Do they know the codebase patterns? Are they pointing to examples?
User's intent: Speed vs thoroughness? Exploration vs execution?
Topic risk: Security, payments, external APIs warrant more caution
Uncertainty level: Is the approach clear or open-ended?
Skip option: If the feature description is already detailed, offer:
"Your description is clear. Should I proceed with research, or would you like to refine it further?"
Main Tasks
1. Local Research (Always Runs - Parallel)
First, I need to understand the project's conventions, existing patterns, and any documented learnings. This is fast and local - it informs whether external research is needed.
Run these agents in parallel to gather local context:
Repo research: technology stack and versions (informs research decisions), architectural patterns, and implementation patterns relevant to the feature
Learnings: documented solutions in docs/solutions/ that might apply (gotchas, patterns, lessons learned)
These findings inform the next step.
1.5. Research Decision
Based on signals from Step 0 and findings from Step 1, decide on external research.
High-risk topics → always research. Security, payments, external APIs, data privacy. The cost of missing something is too high. This takes precedence over speed signals.
Strong local context -> skip external research. Codebase has good patterns, AGENTS.md has guidance, user knows what they want. External research adds little value.
Uncertainty or unfamiliar territory → research. User is exploring, codebase has no examples, new technology. External perspective is valuable.
Announce the decision and proceed. Brief explanation, then continue. User can redirect if needed.
Examples:
"Your codebase has solid patterns for this. Proceeding without external research."
"This involves payment processing, so I'll research current best practices first."
1.5b. External Research (Conditional)
Only run if Step 1.5 indicates external research is valuable.
Reference specific commits with SHA hashes when relevant
Link to code using GitHub's permalink feature (press 'y' for permanent link)
Mention relevant team members with @username if needed
Add links to external resources with descriptive text
Code & Examples:
# Good example with syntax highlighting and line references```ruby
# app/services/user_service.rb:42
def process_user(user)
# Implementation here
end
```# Collapsible error logs<details><summary>Full error stacktrace</summary>`Error details here...`</details>
AI-Era Considerations:
Account for accelerated development with AI pair programming
Include prompts or instructions that worked well during research
Note which AI tools were used for initial exploration (Claude, Copilot, etc.)
Emphasize comprehensive testing given rapid implementation
Document any AI-generated code that needs human review
6. Final Review & Submission
Origin document cross-check (if plan originated from a requirements doc):
Before finalizing, re-read the origin document and verify:
Every key decision from the origin document is reflected in the plan
The chosen approach matches what was decided in the origin document
Constraints and requirements from the origin document are captured in acceptance criteria
Open questions from the origin document are either resolved or flagged
The origin: frontmatter field points to the correct source file
The Sources section includes the origin document with a summary of carried-forward decisions
Pre-submission Checklist:
Title is searchable and descriptive
Labels accurately categorize the issue
All template sections are complete
Links and references are working
Acceptance criteria are measurable
Add names of files in pseudo code examples and todo lists
Add an ERD mermaid diagram if applicable for new model changes
Write Plan File
REQUIRED: Write the plan file to disk before presenting any options.
Use the Write tool to save the complete plan to docs/plans/YYYY-MM-DD-NNN-<type>-<descriptive-name>-plan.md (where NNN is $next_seq from the bash command above). This step is mandatory and cannot be skipped — even when running as part of LFG/SLFG or other automated pipelines.
Confirm: "Plan written to docs/plans/[filename]"
Pipeline mode: If invoked from an automated workflow (LFG, SLFG, or any disable-model-invocation context), skip all AskUserQuestion calls. Make decisions automatically and proceed to writing the plan without interactive prompts.
Output Format
Filename: Use the date, daily sequence number, and kebab-case filename from Step 2 Title & Categorization.