list_features
Skill3 starsChanged 2 months agoMITView on GitHub
Scans the codebase to generate a comprehensive, prompt-based feature list in 'features_listed.md' for recreating the app in any language.
1 other project uses a version of this, 1 of them edited.
Adopted by
Most recently taken up by
- krishnakanthb13/antigravity_global_skillsedited, first seen Jul 4, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 1
- krishnakanthb13/antigravity_global_skills★ 50100% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository46 other files it gives its agents.
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- code-review-and-qualitySkill
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- frontend_designSkill
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- idea_to_actionSkill
- impeccableSkill
- incremental-implementationSkill
- open_source_prepSkill
- optimize_codebaseSkill
- performance-optimizationSkill
- permissioned-githubSkill
- planning-and-task-breakdownSkill
- release_helperSkill
- repo_infographicSkill
- scaffold_testsSkill
- security-and-hardeningSkill
- security_auditSkill
- shipping-and-launchSkill
- software_architectureSkill
- spec-driven-developmentSkill
- test-driven-developmentSkill
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The file
We split this skill into the rules it gives an agent
10 rules. About 0 are common, 7 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈7
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Use this skill to extract a complete, technically precise blueprint of the application, formatted as a series of prompts that could be used …
- Very specific: line 17
Use list_dir to map the project structure.
- Very specific: line 18
Use view_file to inspect README.md, CODE_DOCUMENTATION.md (if available), and key source files (entry points, controllers, UI components).
- Very specific: line 19
Use write_to_file to generate the final Markdown report.
- Distinctive: line 36
Precision: Use standard technical jargon (e.g., "JWT Authentication", "Responsive Grid Layout", "RESTful Endpoint", "WebSocket Stream").
- Distinctive: line 37
Completeness: If a button exists, the feature is "Clickable Button that triggers X".
- Distinctive: line 41
Create a file that looks like a specification document for an AI or Developer:
- Distinctive: line 53
The "Rebuild Test": Ask yourself: "If I gave this file to a stranger, could they build the _exact_ same app without seeing the code?"
- Distinctive: line 54
Ensure no hidden features (middleware, schedulers) are missed.
- Distinctive: line 62
When extracting features, ensure you also capture the underlying mechanics:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
List Features (Recreation Spec)
Use this skill to extract a complete, technically precise blueprint of the application, formatted as a series of prompts that could be used to rebuild the app from scratch.
Inputs
- Project Scope: The root directory of the application to analyze.
- Output File:
features_listed.md(default).
Tooling Strategy
- Use
list_dirto map the project structure. - Use
view_fileto inspectREADME.md,CODE_DOCUMENTATION.md(if available), and key source files (entry points, controllers, UI components). - Use
write_to_fileto generate the final Markdown report.
Workflow
1. Analysis (Deep Scan)
- Objective: Understand exactly what the app does, not just how it's written.
- Sources:
- Documentation (
README.md,DESIGN_PHILOSOPHY.md). - Configuration (
package.json,requirements.txt) to identify tech stack implications. - Source Code: Look for routes, UI components, state management, and database schemas.
- Documentation (
2. Feature Extration & Prompt Generation
For every identified feature, convert it into a Technical Prompt.
- Format: "Implement [Feature Name] using [Input/Constraint]. The system must [Action] when [Trigger]."
- Precision: Use standard technical jargon (e.g., "JWT Authentication", "Responsive Grid Layout", "RESTful Endpoint", "WebSocket Stream").
- Completeness: If a button exists, the feature is "Clickable Button that triggers X". If a background task runs, the feature is "Background Cron Job for X".
3. Report Structuring (features_listed.md)
Create a file that looks like a specification document for an AI or Developer:
Structure:
- Core Architecture: High-level stack agnostic requirements (e.g., "Client-Server Architecture").
- User Interface (UI): Detailed prompts for every page/component (e.g., "Create a dashboard with glassmorphism...").
- Business Logic & Functional Requirements: (e.g., "Calculate tax based on...").
- Data Persistence: (e.g., "Schema must support...").
- API & Integration: (e.g., "Expose GET /api/v1/users...").
4. Verification
- The "Rebuild Test": Ask yourself: "If I gave this file to a stranger, could they build the exact same app without seeing the code?"
- Ensure no hidden features (middleware, schedulers) are missed.
5. Final Output
- Save the file to
features_listed.mdin the project root.
Advanced Feature Extraction (Recreation Standards)
When extracting features, ensure you also capture the underlying mechanics:
- Domain Modeling: Extract the core data entities and their relationships (e.g., "User has many Orders, Order has many Items").
- Third-Party Dependencies: Explicitly call out if a feature relies on an external API or specific SDK (e.g., "Payment processing via Stripe API").
- State Management: Note how global state or complex UI states are handled, as this drastically changes how the app is rebuilt.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

