Skill: ivangdavila/prompting (version 1.0.0)

--- name: Prompting slug: prompting version: 1.0.0 description: Write, test, and iterate prompts for AI models with voice preservation, model-specific adaptation, and systematic failure analysis. metadata: {"clawdbot":{"emoji":"💬","requires":{"bins":[]},"os":["linux","darwin","win32"]}} --- ## Architecture Prompt patterns and user preferences live in `~/prompting/`. ``` ~/prompting/ ├── memory.md # HOT: user voice, model preferences, learned corrections ├── patterns/ # Reusable prompt templates by task type └── history.md # Past prompts with outcomes ``` See `memory-template.md` for initial setup. ## Quick Reference | Topic | File | |-------|------| | Common failure modes | `failures.md` | | Model-specific quirks | `models.md` | | Iteration workflow | `iteration.md` | | Advanced techniques | `techniques.md` | ## Core Rules ### 1. Ask Before Assuming Before writing any prompt, ask: - What model? (GPT-4, Claude, Haiku, Gemini) - What's the failure mode you're seeing? (if iterating) - Token budget? (cost-sensitive vs. quality-first) Never default to verbose. Simpler often wins. ### 2. Preserve What Works When improving a failing prompt: - Change ONE thing at a time - Note what's currently working - Surgical fixes > rewrites ### 3. Model-Specific Adaptation See `models.md` — key differences: - Claude: explicit constraints, less scaffolding needed - GPT-4: benefits from step-by-step, tolerates verbose - Haiku/fast models: brevity critical, skip examples when possible Prompt optimized for one model will underperform on others. ### 4. Voice Lock When user provides writing samples: - Extract specific patterns (sentence length, punctuation, vocabulary) - Apply consistently throughout session - Check output against samples before delivering ### 5. True Variation When generating alternatives, vary: - Structure (not just synonyms) - Emotional angle - Opening hook - Call-to-action style "Top 5 reasons" → "The hidden truth about" → "What nobody tells you about" = real variation. ### 6. Failure Classification When a prompt fails, classify the failure type: - **Hallucination** → add grounding, sources, constraints - **Format break** → strengthen output spec, add examples - **Instruction drift** → move critical constraints earlier - **Refusal** → rephrase intent, remove ambiguity Different failures need different fixes. See `failures.md`. ### 7. Compression Bias Default to removing words, not adding. Test: "Does removing this line change the output?" If no, remove. Token costs matter. A prompt that works with 50 tokens beats one that needs 500. ### 8. Test Case Generation When asked to test a prompt: - Generate edge cases (empty input, very long, special chars) - Include adversarial inputs - Test boundary conditions Don't just test happy path. ### 9. Platform-Native Output For content prompts, know platform constraints: - Twitter: 280 chars, no markdown - LinkedIn: longer ok, hashtags matter - Instagram: emoji-friendly, visual hooks Prompt should enforce format, not hope for it. ### 10. Memory Persistence Store in `~/prompting/memory.md`: - User's preferred style (terse vs detailed) - Target models they commonly use - Past corrections ("I told you I don't want emojis") Reference before every prompting task.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)