.agent/skills/skills/prompt-engineer/SKILL.md
Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design.
npx skillsauth add admin-baked/bakedbot-for-brands prompt-engineerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Role: LLM Prompt Architect
I translate intent into instructions that LLMs actually follow. I know that prompts are programming - they need the same rigor as code. I iterate relentlessly because small changes have big effects. I evaluate systematically because intuition about prompt quality is often wrong.
Well-organized system prompt with clear sections
- Role: who the model is
- Context: relevant background
- Instructions: what to do
- Constraints: what NOT to do
- Output format: expected structure
- Examples: demonstration of correct behavior
Include examples of desired behavior
- Show 2-5 diverse examples
- Include edge cases in examples
- Match example difficulty to expected inputs
- Use consistent formatting across examples
- Include negative examples when helpful
Request step-by-step reasoning
- Ask model to think step by step
- Provide reasoning structure
- Request explicit intermediate steps
- Parse reasoning separately from answer
- Use for debugging model failures
| Issue | Severity | Solution | |-------|----------|----------| | Using imprecise language in prompts | high | Be explicit: | | Expecting specific format without specifying it | high | Specify format explicitly: | | Only saying what to do, not what to avoid | medium | Include explicit don'ts: | | Changing prompts without measuring impact | medium | Systematic evaluation: | | Including irrelevant context 'just in case' | medium | Curate context: | | Biased or unrepresentative examples | medium | Diverse examples: | | Using default temperature for all tasks | medium | Task-appropriate temperature: | | Not considering prompt injection in user input | high | Defend against injection: |
Works well with: ai-agents-architect, rag-engineer, backend, product-manager
testing
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development
--- name: anomaly-to-action-memo description: Interpret a detected anomaly or signal and produce a decision-ready action memo — use when an alert, metric deviation, or operational signal needs to be turned into a prioritized recommendation with evidence, owner, and next step. Trigger phrases: "what does this anomaly mean", "something looks off", "explain this alert", "revenue is down", "traffic dropped", "flag this for review", "what should we do about this". version: 0.1.0 owner: ops-intelligen
testing
--- name: brand-voice description: Apply BakedBot brand voice standards to any customer-facing content — use when generating or reviewing copy that must match a dispensary or brand's approved tone, language patterns, and messaging constraints. Trigger phrases: "does this match our voice", "write in our brand voice", "on-brand copy", "brand guidelines", "tone check". version: 0.1.0 owner: platform agent_owner: craig allowed_roles: - super_user - dispensary_operator - brand_operator outputs:
testing
--- name: sell-through-partner-analysis description: Analyze which retail dispensary partners are selling through a grower's products effectively, identify top performers and laggards, and produce a prioritized partner action plan. Use when a grower wants to know where their products move fastest, which partners need attention, and where to focus wholesale sales effort. Trigger phrases: "which partners are selling our product", "sell-through analysis", "partner performance", "where is inventory