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150 lines
8.1 KiB
Markdown
150 lines
8.1 KiB
Markdown
# Prompt Engineer
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Prompts are code. They have bugs, edge cases, and failure modes. This skill
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treats prompt optimization as a systematic discipline -- analyzing issues,
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applying documented patterns, and proposing changes with explicit rationale.
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I use this on my own workflow. The skill was optimized using itself -- of
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course.
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## When to Use
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- A sub-agent definition that misbehaves (agents/developer.md)
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- A Python script with embedded prompts that underperform
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(skills/planner/scripts/planner.py)
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- A multi-prompt workflow that produces inconsistent results
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- Any prompt that does not do what you intended
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## How It Works
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The skill:
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1. Reads prompt engineering pattern references
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2. Analyzes the target prompt for issues
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3. Proposes changes with explicit pattern attribution
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4. Waits for approval before applying changes
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5. Presents optimized result with self-verification
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I use recitation and careful output ordering to ground the skill in the
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referenced patterns. This prevents the model from inventing techniques.
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## Example Usage
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Optimize a sub-agent:
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```
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Use your prompt engineer skill to optimize the system prompt for
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the following claude code sub-agent: agents/developer.md
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```
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Optimize a multi-prompt workflow:
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```
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Consider @skills/planner/scripts/planner.py. Identify all prompts,
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understand how they interact, then use your prompt engineer skill
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to optimize each.
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```
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## Example Output
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Each proposed change includes scope, problem, technique, before/after, and
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rationale. A single invocation may propose many changes:
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```
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+==============================================================================+
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| CHANGE 1: Add STOP gate to Step 1 (Exploration) |
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+==============================================================================+
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| SCOPE |
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| ----- |
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| Prompt: analyze.py step 1 |
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| Section: Lines 41-49 (precondition check) |
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| Downstream: All subsequent steps depend on exploration results |
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+------------------------------------------------------------------------------+
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| PROBLEM |
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| ------- |
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| Issue: Hedging language allows model to skip precondition |
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| Evidence: "PRECONDITION: You should have already delegated..." |
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| "If you have not, STOP and do that first" |
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| Runtime: Model proceeds to "process exploration results" without having |
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| any results, produces empty/fabricated structure analysis |
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+------------------------------------------------------------------------------+
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| TECHNIQUE |
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| --------- |
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| Apply: STOP Escalation Pattern (single-turn ref) |
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| Trigger: "For behaviors you need to interrupt, not just discourage" |
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| Effect: "Creates metacognitive checkpoint--the model must pause and |
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| re-evaluate before proceeding" |
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| Stacks: Affirmative Directives |
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+------------------------------------------------------------------------------+
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| BEFORE |
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| ------ |
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| +----------------------------------------------------------------------+ |
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| | "PRECONDITION: You should have already delegated to the Explore | |
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| | sub-agent.", | |
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| | "If you have not, STOP and do that first:", | |
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| +----------------------------------------------------------------------+ |
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| v |
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| AFTER |
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| ----- |
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| +----------------------------------------------------------------------+ |
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| | "STOP. Before proceeding, verify you have Explore agent results.", | |
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| | "", | |
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| | "If your --thoughts do NOT contain Explore agent output, you MUST:", | |
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| | " 1. Use Task tool with subagent_type='Explore' | |
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| | " 2. Prompt: 'Explore this repository. Report directory structure, | |
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| | " tech stack, entry points, main components, observed patterns.' | |
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| | " 3. WAIT for results before invoking this step again | |
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| | "", | |
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| | "Only proceed below if you have concrete Explore output to process." | |
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| +----------------------------------------------------------------------+ |
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+------------------------------------------------------------------------------+
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| WHY THIS IMPROVES QUALITY |
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| ------------------------- |
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| Transforms soft precondition into hard gate. Model must explicitly verify |
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| it has Explore results before processing, preventing fabricated analysis. |
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+==============================================================================+
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... many more
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---
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Compatibility check:
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- STOP Escalation + Affirmative Directives: Compatible (STOP is for interrupting specific behaviors)
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- History Accumulation + Completeness Checkpoint Tags: Synergistic (both enforce state tracking)
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- Quote Extraction + Chain-of-Verification: Complementary (both prevent hallucination)
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- Progressive depth + Pre-Work Context Analysis: Sequential (planning enables deeper execution)
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Anti-patterns verified:
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- No hedging spiral (replaced "should have" with "STOP. Verify...")
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- No everything-is-critical (CRITICAL used only for state requirement)
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- Affirmative directives used (changed negatives to positives)
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- No implicit category trap (explicit checklists provided)
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---
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Does this plan look reasonable? I'll apply these changes once you confirm.
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```
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## Caveat
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When you tell an LLM "find problems and opportunities for optimization", it will
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find problems. That is what you asked it to do. Some may not be real issues.
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I recommend invoking the skill multiple times on challenging prompts, but
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recognize when it is good enough and stop. Diminishing returns are real.
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