Add 5 pi extensions: pi-subagents, pi-crew, rpiv-pi, pi-interactive-shell, pi-intercom
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---
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name: gpt-5-4-prompting
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description: How to write system prompts and instructions for GPT-5.4. Use when constructing or tuning prompts targeting GPT-5.4.
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---
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# GPT-5.4 Prompting Guide
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GPT-5.4 unifies reasoning, coding, and agentic capabilities into a single frontier model. It's extremely persistent, highly token-efficient, and delivers more human-like outputs than its predecessors. However, it has new failure modes: it moves fast without solid plans, expands scope aggressively, and can prematurely declare tasks complete—sometimes falsely claiming success. Prompts must account for these behaviors.
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## Output shape
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Always include.
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```
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<output_verbosity_spec>
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- Default: 3-6 sentences or <=5 bullets for typical answers.
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- Simple yes/no questions: <=2 sentences.
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- Complex multi-step or multi-file tasks:
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- 1 short overview paragraph
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- then <=5 bullets tagged: What changed, Where, Risks, Next steps, Open questions.
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- Avoid long narrative paragraphs; prefer compact bullets and short sections.
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- Do not rephrase the user's request unless it changes semantics.
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</output_verbosity_spec>
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```
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## Scope constraints
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Critical. GPT-5.4's primary failure mode is scope expansion—it adds features, refactors beyond the ask, and "helpfully" extends tasks. Fence it in hard.
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```
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<design_and_scope_constraints>
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- Implement EXACTLY and ONLY what the user requests. Nothing more.
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- No extra features, no "while I'm here" improvements, no UX embellishments.
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- Do NOT expand the task scope under any circumstances.
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- If you notice adjacent issues or opportunities, note them in your summary but DO NOT act on them.
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- If any instruction is ambiguous, choose the simplest valid interpretation.
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- Style aligned to the existing design system. Do not invent new patterns.
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- Do NOT invent colors, shadows, tokens, animations, or new UI elements unless explicitly requested.
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</design_and_scope_constraints>
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```
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## Verification requirements
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Critical. GPT-5.4 can declare tasks complete prematurely or claim success when the implementation is incorrect. Force explicit verification.
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```
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<verification_requirements>
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- Before declaring any task complete, perform explicit verification:
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- Re-read the original requirements
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- Check that every requirement is addressed in the actual code
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- Run tests or validation steps if available
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- Confirm the implementation actually works, don't assume
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- Do NOT claim success based on intent—verify actual outcomes.
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- If you cannot verify (no tests, can't run code), say so explicitly.
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- When reporting completion, include concrete evidence: test results, verified file contents, or explicit acknowledgment of what couldn't be verified.
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- If something failed or was skipped, say so clearly. Do not obscure failures.
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</verification_requirements>
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```
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## Context loading
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Always include. GPT-5.4 is faster and may skip reading in favor of acting. Force thoroughness.
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```
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<context_loading>
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- Read ALL files that will be modified—in full, not just the sections mentioned in the task.
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- Also read key files they import from or that depend on them.
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- Absorb surrounding patterns, naming conventions, error handling style, and architecture before writing any code.
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- Do not ask clarifying questions about things that are answerable by reading the codebase.
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- If modifying existing code, understand the full context before making changes.
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</context_loading>
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```
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## Plan-first mode
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Include for multi-file work, refactors, or tasks with ordering dependencies. GPT-5.4 produces good natural-language plans but may skip validation steps.
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```
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<plan_first>
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- Before writing any code, produce a brief implementation plan:
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- Files to create vs. modify
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- Implementation order and prerequisites
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- Key design decisions and edge cases
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- Acceptance criteria for "done"
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- How you will verify each step
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- Execute the plan step by step. After each step, verify it worked before proceeding.
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- If the plan is provided externally, follow it faithfully—the job is execution, not second-guessing.
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- Do NOT skip verification steps even if you're confident.
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</plan_first>
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```
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## Long-context handling
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GPT-5.4 supports up to 1M tokens, but accuracy degrades beyond ~512K. Handle long inputs carefully.
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```
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<long_context_handling>
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- For inputs longer than ~10k tokens:
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- First, produce a short internal outline of the key sections relevant to the task.
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- Re-state the constraints explicitly before answering.
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- Anchor claims to sections ("In the 'Data Retention' section...") rather than speaking generically.
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- If the answer depends on fine details (dates, thresholds, clauses), quote or paraphrase them.
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- For very long contexts (200K+ tokens):
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- Be extra vigilant about accuracy—retrieval quality degrades.
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- Cross-reference claims against multiple sections.
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- Prefer citing specific locations over making sweeping statements.
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</long_context_handling>
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```
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## Tool usage
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```
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<tool_usage_rules>
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- Prefer tools over internal knowledge whenever:
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- You need fresh or user-specific data (tickets, orders, configs, logs).
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- You reference specific IDs, URLs, or document titles.
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- Parallelize independent tool calls when possible to reduce latency.
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- After any write/update tool call, verify the outcome—do not assume success.
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- After any write/update tool call, briefly restate:
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- What changed
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- Where (ID or path)
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- Verification performed or why verification was skipped
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</tool_usage_rules>
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```
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## Backwards compatibility hedging
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GPT-5.4 tends to preserve old patterns and add compatibility shims. Use **"cutover"** to signal a clean break.
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Instead of:
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> "Rewrite this and don't worry about backwards compatibility"
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Say:
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> "This is a cutover. No backwards compatibility. Rewrite using only Python 3.12+ features and current best practices. Do not preserve legacy code, polyfills, or deprecated patterns."
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## Quick reference
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- **Constrain scope aggressively.** GPT-5.4 expands tasks beyond the ask. "ONLY what is requested, nothing more."
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- **Force verification.** Don't trust "done"—require evidence. "Verify before claiming complete."
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- **Use cutover language.** "Cutover," "no fallbacks," "exactly as specified" get cleaner results.
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- **Plan mode helps.** Explicit plan-first prompts ensure verification steps.
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- **Watch for false success claims.** In agent harnesses, add explicit validation steps. Don't let it self-report completion.
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- **Steer mid-task.** GPT-5.4 handles redirects well. Be direct: "Stop. That's out of scope." / "Verify that actually worked."
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- **Use domain jargon.** "Cutover," "golden-path," "no fallbacks," "domain split," "exactly as specified" trigger precise behavior.
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- **Long context degrades.** Above ~512K tokens, cross-reference claims and cite specific sections.
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- **Token efficiency is real.** 5.4 uses fewer tokens per problem—but verify it didn't skip steps to get there.
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## Example: implementation task prompt
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```
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<system>
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You are implementing a feature in an existing codebase. Follow these rules strictly.
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<design_and_scope_constraints>
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- Implement EXACTLY and ONLY what the user requests. Nothing more.
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- No extra features, no "while I'm here" improvements.
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- If you notice adjacent issues, note them in your summary but DO NOT act on them.
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</design_and_scope_constraints>
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<context_loading>
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- Read ALL files that will be modified—in full.
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- Also read key files they import from or depend on.
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- Absorb patterns before writing any code.
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</context_loading>
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<verification_requirements>
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- Before declaring complete, verify each requirement is addressed in actual code.
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- Run tests if available. If not, state what couldn't be verified.
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- Include concrete evidence of completion in your summary.
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</verification_requirements>
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<output_verbosity_spec>
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- Brief updates only on major phases or blockers.
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- Final summary: What changed, Where, Risks, Next steps.
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</output_verbosity_spec>
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</system>
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```
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## Example: code review prompt
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```
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<system>
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You are reviewing code changes. Be thorough but stay in scope.
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<context_loading>
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- Read every changed file in full, not just the diff hunks.
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- Also read files they import from and key dependents.
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</context_loading>
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<review_scope>
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- Review for: bugs, logic errors, race conditions, resource leaks, null hazards, error handling gaps, type mismatches, dead code, unused imports, pattern inconsistencies.
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- Fix issues you find with direct code edits.
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- Do NOT refactor or restructure code that wasn't flagged in the review.
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- If adjacent code looks problematic, note it but don't touch it.
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</review_scope>
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<verification_requirements>
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- After fixes, verify the code still works. Run tests if available.
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- In your summary, list what was found, what was fixed, and what couldn't be verified.
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</verification_requirements>
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</system>
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```
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