# Generate Refine Prompts

> Generate, critique, and iteratively refine production prompts for AI agents (system/developer/task prompts) with explicit role boundaries, tool-calling contracts, workflow stages, safety policies, and output format rules. Use when creating a new prompt from scratch, upgrading weak prompts, merging conflicting prompt instructions, adapting prompts to a specific tool/runtime, or benchmarking prompt quality based on patterns from Anthropic, Google Antigravity, Open Source prompts, Notion AI, and Lovable.

- Skill: `heichenya/generate-refine-prompts` (Agent Skill, multi-file: 8 files)
- Install (CLI): `npx skillmds@latest add heichenya/generate-refine-prompts`
- Raw SKILL.md: https://api.skillmd.com/api/skills/heichenya/generate-refine-prompts/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: heichenya (https://skillmd.com/u/heichenya)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/heichenya/generate-refine-prompts

---


# Generate Refine Prompts

## Overview

Use this skill to create prompts that are executable and maintainable under real tool constraints. Favor small, testable prompt deltas over large rewrites.

Hard rule for this skill:
- Summarize and abstract source practices into reusable principles.
- Do not paste large raw prompt blocks from source projects into deliverables.

## Quick Start

1. Define target runtime and task scope:
- Capture model role, tool surface, sandbox/approval model, and output channel.
2. Read `references/full-coverage-synthesis.md`:
- Start from full-corpus distilled takeaways (20-file coverage).
3. Read `references/prompt-design-philosophy.md`:
- Select the design posture that matches the task (strict execution, knowledge retrieval, UX coding, or hybrid).
4. Start from `references/prompt-blueprints.md`:
- Use the nearest blueprint and only fill sections required by the target runtime.
5. Run `scripts/prompt_quality_check.py <prompt-file>`:
- Detect missing contract sections and instruction conflicts before shipping.
6. Refine with minimal edits:
- Preserve good constraints, remove contradictions, and tighten failure handling language.

## Workflow

### 1. Define the Prompt Contract

Establish non-negotiables before writing prose:
- Role and task boundary:
  State what the agent is and what it is not.
- Tool contract:
  Define when to call tools, batching strategy, and parameter strictness.
- Safety and policy:
  Define refusal boundaries, data handling, and prohibited actions.
- Output contract:
  Define verbosity, formatting, and required sections.

### 2. Compose a First Draft

Write sections in this order:
1. Identity and operating context
2. Global mandates and hard constraints
3. Tool protocol and sequencing rules
4. Task workflow and verification gates
5. Response format and style constraints

### 3. Run Structured Quality Checks

Run:

```bash
python3 scripts/prompt_quality_check.py path/to/prompt.txt
```

Interpret results:
- Missing sections:
  Add missing contract components before style tweaks.
- Conflicts:
  Resolve hard contradictions first, then soft tone conflicts.
- Score:
  Use as triage only; prioritize explicit findings.

### 4. Red-Team and Refine

Stress-test against edge cases:
- Ambiguous user intent
- Missing required tool parameters
- Conflicting instructions between system and developer layers
- Requests that should trigger clarification instead of immediate execution

Refine by patching only the minimal lines needed to recover determinism.

### 5. Produce Final Prompt Package

Return:
- Final prompt text
- Short changelog of edits
- Known tradeoffs and remaining risks

## Design Heuristics From Sources

- Anthropic:
  Enforce concise style, clear proactiveness bounds, and convention-first code changes.
- Google Antigravity:
  Separate planning, execution, and verification modes with explicit status transitions.
- Notion AI:
  Use tool-first loops for non-trivial tasks and standardize citation/search behavior.
- Lovable:
  Encode platform/runtime constraints and default discussion-vs-implementation policy.
- Open Source prompts:
  Make tool schemas explicit, constrain edits, and define execution environment assumptions.

## Resources

- `references/full-coverage-synthesis.md`
  Full-file coverage distilled into transferable writing philosophy.
- `references/full-coverage-generated.md`
  Auto-generated 20-file coverage report with structure/axis statistics.
- `references/prompt-design-philosophy.md`
  Comparative design patterns and anti-patterns.
- `references/prompt-blueprints.md`
  Reusable templates for generation and refinement.
- `scripts/prompt_quality_check.py`
  Fast static checks for missing sections and instruction conflicts.
- `scripts/build_full_coverage_report.py`
  Generate summarized full-coverage corpus reports without copying source text.

## Deliverable Template

Use this shape when returning refined prompts:

```markdown
## Prompt vN
<final prompt>

## Changes from vN-1
1. <change>
2. <change>

## Remaining Risks
1. <risk>
2. <risk>
```