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name: gpt5.2-spec-writer description: Guide for writing effective prompt specifications (specs) for the GPT-5.2 Codex agent. Use when the user asks to "write a prompt spec", "create a system prompt for GPT-5.2", "design a spec for an agent", or "how to prompt GPT-5.2".

GPT-5.2 Spec Writer

Guide for writing prompt specifications tailored for GPT-5.2. The model excels at structured reasoning and instruction following but benefits from explicit constraints on verbosity and scope.

How to Write a Spec

A spec is a markdown document defining persona, constraints, architecture, and operational rules for an agent.

1. Start with references/spec-template.md
2. Fill in project-specific sections (directive, architecture, conventions)
3. Verify coverage: task structure, verbosity/scope constraints, tool usage guidance, context handling for large inputs

Key Patterns

Explicit Task Roadmaps with Checkboxes

Every spec should include an objective, actionable sequence of tasks using markdown checkboxes (- [ ]). GPT-5.2 performs best with concrete work items and clear completion criteria, not abstract guidance.

• Use - [ ] checkboxes for all tasks—GPT-5.2 tracks and checks them off as work completes
• Numbered phases with measurable objectives
• Specific file paths and function names
• Concrete steps: Read X -> Edit Y -> Run Z
• Completion criteria for each task

Example:

- [ ] 1.1 Fix null check in `parse_config()` at line 42
- [ ] 1.2 Add error handling to `load_settings()`
- [ ] 1.3 Run tests and verify all pass

Avoid vague instructions like "improve the code". Be specific: "Fix the null check in parse_config() at line 42".

XML Constraint Blocks

Use XML tags to define distinct rule sets:

• <mandatory_execution_requirements> - execution loop (Read -> Edit -> Verify)
• <verbosity_and_scope_constraints> - output size and scope control
• <design_freedom> - when new patterns/refactors are acceptable

Chain of Verification

Instruct the model to verify its work: Edit -> Build/check -> Fix -> Report only when complete.

Tool Usage

• Encourage parallel tool use for batch operations (e.g. reading multiple files)
• Require verification after edits (run build, run tests)

Context Management

For large inputs (>10k tokens), use <long_context_handling> to instruct the model to outline key sections, restate constraints, and anchor claims to specific locations.

References

• Spec Template
• GPT-5.2 Model Guide