generate-code

A workflow that turns a feature request or issue description into code and places the code in the current note for review. It first reads the available context and existing patterns before suggesting the code structure.

In plain words
What is it for?
Use it to draft backend features such as schemas, services, repositories, and API routes, or corresponding frontend components, hooks, and stores. It can use related issue details and existing note content as requirements.
Why use it?
It reduces the work of turning an idea into a starting implementation while keeping the result available for checking and integration. It helps the generated code fit the project's language and framework.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/pilotspace/pilot-space/generate-code
Any agent
npx skills add pilotspace/pilot-space --skill generate-code
Clone the repo
git clone --depth 1 https://github.com/pilotspace/pilot-space

Made for: Claude Code, Codex.

Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,274 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00023 $0.01274
Opus 5 $0.00012 $0.00637
Sonnet 5 $0.00005 $0.00255
Haiku 4.5 $0.00002 $0.00127

Measured yesterday against content hash 3682b4cece8a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

generate-code scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

backend/src/pilot_space/ai/templates/skills/generate-code/SKILL.md · 175 lines

How it starts

The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Generate Code Skill

Generate production-ready code based on a feature description, issue context, or note content. Writes code blocks to the current note for review and integration.

Quick Start

Use this skill when:

  • User requests code generation for a feature (/generate-code)
  • Agent detects an issue needs a code scaffold
  • User provides a description of what code they need

Example:

User: "Generate a FastAPI endpoint for creating workspace invitations"

AI generates:
- Pydantic schema (InvitationCreateRequest, InvitationResponse)
- Service class (InvitationService.execute)
- Repository method stub (InvitationRepository.create)
- Router endpoint (POST /workspaces/{workspace_id}/invitations)

Workflow

  1. Gather Context

    • Read current note content for requirements and constraints
    • Fetch related issue details if issue_id is present in context
    • Identify target language/framework from workspace or note context
    • Check existing code patterns via search_note_content for consistency
  2. Plan Code Structure

    • Identify components: schema, service, repository, router (backend) or component, hook, store (frontend)
    • Determine file placement following project conventions
    • Identify dependencies and imports needed
  3. Generate Code

    • Write idiomatic, production-ready code with type hints
    • Follow project conventions: CQRS-lite (backend), MobX + TanStack Query (frontend)
    • Include docstrings for public APIs
    • No placeholders, no TODOs — complete implementations only
  4. Insert to Note

    • Use insert_block to write each code block as a separate code fence
    • Prepend with a brief explanation heading
    • Include file path comment at top of each block
  5. Request Suggestion Approval

    • Return status: pending_suggestion for user review before integration
    • User can edit blocks directly in the note before accepting

Output Format

{
  "status": "pending_suggestion",
  "skill": "generate-code",
  "blocks_inserted": 4,
  "note_id": "note-uuid",
  "summary": "Generated FastAPI endpoint with schema, service, repository stub, and router",
  "files_referenced": [
    "backend/src/pilot_space/api/v1/schemas/invitation.py",
    "backend/src/pilot_space/application/services/invitation_service.py",
    "backend/src/pilot_space/infrastructure/database/repositories/invitation_repository.py",
    "backend/src/pilot_space/api/v1/routers/invitations.py"
  ]
}

Read the full file on GitHub · 175 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 175 lines · 23 tokens per session scan A 3682b4cece8a

Subscribe to this mod's changes

generate-code is a skill published in the GitHub repository pilotspace/pilot-space (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 23 tokens to every session and 1,274 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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