[IMPORTANT] Use

TaskCreate

to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

AI Mistake Prevention — Failure modes to avoid on every task:

• Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal.
• Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing.
• Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain.
• Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path.
• When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site.
• Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code.
• Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks.
• Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis.
• Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly.
• Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Quick Summary

Goal: Write and optimize prompts for AI text, image, and video generation models (Claude, GPT, Midjourney, DALL-E, Stable Diffusion, Flux, Veo).

Workflow:

1. Identify — Determine model type (LLM, image, video) and desired outcome
2. Structure — Apply model-specific prompt patterns (Role/Context/Task for LLMs, Subject/Style/Composition for images)
3. Refine — Iterate with A/B testing, style keywords, negative prompts

Key Rules:

• Use clarity, context, structure, and iteration as core principles
• Apply model-specific syntax (Midjourney

  --ar

  , SD weighted tokens, etc.)
• Load reference files for detailed guidance per domain (marketing, code, writing, data)

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

AI Artist - Prompt Engineering

Craft effective prompts for AI text and image generation models.

Core Principles

1. Clarity - Be specific, avoid ambiguity
2. Context - Set scene, role, constraints upfront
3. Structure - Use consistent formatting (markdown, XML tags, delimiters)
4. Iteration - Refine based on outputs, A/B test variations

Quick Patterns

LLM Prompts (Claude/GPT/Gemini)

[Role] You are a {expert type} specializing in {domain}.
[Context] {Background information and constraints}
[Task] {Specific action to perform}
[Format] {Output structure - JSON, markdown, list, etc.}
[Examples] {1-3 few-shot examples if needed}

Image Generation (Midjourney/DALL-E/Stable Diffusion)

[Subject] {main subject with details}
[Style] {artistic style, medium, artist reference}
[Composition] {framing, angle, lighting}
[Quality] {resolution modifiers, rendering quality}
[Negative] {what to avoid - only if supported}

Example:

Portrait of a cyberpunk hacker, neon lighting, cinematic composition, detailed face, 8k, artstation quality --ar 16:9 --style raw

References

Load for detailed guidance:

references/llm-prompting.md

references/image-prompting.md

references/nano-banana.md

references/advanced-techniques.md

references/domain-patterns.md

references/domain-marketing.md

references/domain-code.md

references/domain-writing.md

references/domain-data.md

Model-Specific Tips

--ar

--style

--chaos

--weird

--v 6.1

(word:1.2)

--guidance

Anti-Patterns

• Vague instructions ("make it better")
• Conflicting constraints
• Missing context for domain tasks
• Over-prompting with redundant details
• Ignoring model-specific strengths/limits

Closing Reminders

• MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using

  TaskCreate

  BEFORE starting
• MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
• MANDATORY IMPORTANT MUST ATTENTION cite

  file:line

  evidence for every claim (confidence >80% to act)
• MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality <!-- SYNC:critical-thinking-mindset:reminder -->
• MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact. <!-- /SYNC:critical-thinking-mindset:reminder --> <!-- SYNC:ai-mistake-prevention:reminder -->
• MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction. <!-- /SYNC:ai-mistake-prevention:reminder -->