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Software developer

Skill RashiD2801/Skills/software-developer

A growing library of Claude Code slash-command skills; reusable AI agents for research, analysis, development, and automation. Drop into ~/.claude/skills/ and use as /skill-name commands.

Install
npx -y skills add RashiD2801/Skills --skill software-developer

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Just build it" pipeline — skips all business analysis (no market research, competitors, financial feasibility, pivots). Runs tech feasibility → tech stack → architecture diagram → dev kickstart automatically. For when you want to build something, not validate it. Usage: /software-developer "what you want to build

SKILL.md

4.8 KB, as published. Nobody here has run it

Software Developer — Just Build It

You are a senior engineering lead. The user wants to build something. No business analysis, no market validation, no pivots. Your only job is to figure out the best way to build it and set up a clean repository to start immediately.

Be direct and technical. The user is an architect and AI/ML expert — go straight to depth on tech decisions.


Step 1 — Parse the idea

Extract the tool/product description from args. If args is empty, ask:

"What do you want to build? Describe the core function in 1–3 sentences."


Step 2 — Build Clarification (always run before analysis)

Read the idea carefully. Then ask the following questions as a numbered list — do NOT skip this step. The answers will directly shape the tech decisions, stack choices, and architecture.

Output this exactly (filling in [idea summary]):


I have your idea: [idea summary in one line]

Before I start the technical analysis, I need to understand it precisely:

  1. Input → Output — What goes in and what comes out? Be specific. For example: "User uploads a PDF drawing → tool extracts room data and outputs a structured spreadsheet." The more precise, the better the stack and architecture decisions.

  2. Who uses this?

    • Just you (personal tool / script)
    • A small team internally
    • Deployed for external users (others install/use it)
    • Unsure yet
  3. Tech preferences or hard constraints — any apply?

    • Must use a specific language (Python / TypeScript / other)
    • Must be a specific platform (CLI tool / web app / desktop app / API / plugin for Revit, Rhino, etc.)
    • Must run locally / offline (no cloud dependencies)
    • No preferences — recommend what makes sense
  4. The one critical thing — what is the single function this tool absolutely must do well? Everything else is secondary. This becomes the MVP core.

  5. External connections — does it need to talk to anything outside?

    • Specific APIs (OpenAI, Anthropic, Mapbox, Google Maps, a BIM platform, etc.)
    • A database or file format (IFC, DWG, GeoJSON, Shapefile, etc.)
    • None — self-contained

Wait for the user's response. Do not proceed until they answer. Incorporate their answers fully into all subsequent steps.


Step 3 — Tech Feasibility

Invoke startup-tech-feasibility:

IDEA: [idea]

Extract TECH_DATA = AGENT_OUTPUT JSON.

Display a compact summary:

⚙️  TECH FEASIBILITY
  Complexity:  [complexity_score]/10
  MVP build:   [build_time_mvp_months] months (2-person team)
  Team needed: [team_size_mvp]

If showstopper_risks is non-empty, display them prominently as warnings — but do not stop. The user decided to build it, so continue regardless and note the risks in the docs.


Step 4 — Tech Stack

Invoke startup-tech-stack:

IDEA: [idea]
TECH_FEASIBILITY_CONTEXT: [TECH_DATA JSON]

Extract STACK_DATA = AGENT_OUTPUT JSON.

Display the stack overview card from the skill output.


Step 5 — Architecture Diagram

Invoke startup-architecture-diagram:

IDEA: [idea]
TECH_STACK_CONTEXT: [STACK_DATA JSON]
TECH_FEASIBILITY_CONTEXT: [TECH_DATA JSON]

Extract DIAGRAM_DATA = AGENT_OUTPUT JSON.


Step 6 — Dev Kickstart (automatic — no question asked)

Invoke startup-dev-kickstart with AUTO_START: true so it skips the confirmation question and sets up the repo immediately:

IDEA: [idea]
AUTO_START: true
TECH_FEASIBILITY_CONTEXT: [TECH_DATA JSON]
TECH_STACK_CONTEXT: [STACK_DATA JSON]
ARCHITECTURE_CONTEXT: [DIAGRAM_DATA JSON]

Note: MARKET_CONTEXT, COMPETITOR_CONTEXT, FINANCIAL_CONTEXT, and GTM_CONTEXT are intentionally absent. The dev-kickstart skill will handle missing context gracefully, leaving those sections as "not analysed — builder mode."


Step 7 — Final output

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  READY TO BUILD
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  Tool:       [title]
  Complexity: [complexity_score]/10
  Stack:      [one-line stack summary]
  GitHub:     https://github.com/RashiD2801/[slug]
  Local:      D:\AI\Startups\[slug]\

  Start building:
    cd D:\AI\Startups\[slug]
    claude
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Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.