| name | prompt-craft |
| description | USE WHEN improve prompt, write prompt, craft prompt, analyze prompt, prompt engineering, subagent prompts. Analyze, craft, and improve prompts using 19 research-backed techniques. Modes - Analyze (critique existing), Craft (build from scratch), Teach (explain techniques), Quick Fix (fast improvements). |
Prompt Craft
Diagnose why prompts underperform. Not a checklist service — a diagnostic practice. The default failure: "more instructions = better." Wrong. After a threshold, instructions degrade output (IFScale). Focus on what to REMOVE and ACTIVATE.
=== PROMPT CRAFT ===
MODES
A. Analyze - Critique existing prompt, score techniques, suggest improvements
B. Craft - Build optimized prompt from requirements
C. Teach - Deep dive on a specific technique
D. Quick Fix - Fast 3-improvement pass (minimal explanation)
CORE TECHNIQUES (1-10)
1. Chain-of-Thought 2. Structured Output 3. Few-Shot Examples
4. Placement 5. Salience 6. Roles
7. Positive Framing 8. Reasoning-First 9. Verbalized Sampling
10. Self-Reflection
EXTENDED: decomposition, compression, sufficiency, scope,
format-spec, uncertainty, chaining, self-consistency,
tree-of-thoughts, react-loop, tool-description-craft,
context-engineering, multi-session
MODEL GUIDES: claude, openai, deepseek, gemini, kimi, qwen
→ See reference/models/{name}.md for model-specific prompting
Commands:
- A/B/C/D or mode name to begin
- 1-10 or technique name for Teach mode
- *model [name] - Load model-specific guidance (from reference/models/)
- *extended - Show extended techniques
- *help - Show this menu
Mode Router
Detect the user's intent from context and route to the appropriate mode:
- Existing prompt provided -> Analyze mode (A)
- Requirements for new prompt -> Craft mode (B)
- Technique number/name -> Teach mode (C)
- Quick improvement request -> Quick Fix mode (D)
If unclear, ask which mode fits. Use *help to show the menu on demand.
Mode A: Analyze
Disposition: Diagnose why this prompt will underperform. The failure mode is "checklist-completion" — noting what's present without naming what specific failure each absence causes. The diagnosis matters more than the score. Load reference files for techniques being applied.
Competence note: The common error is a scorecard that says "missing CoT" without explaining WHY that gap causes wrong answers for this specific task.
Analyze Output Template
PROMPT ANALYSIS
===============
CURRENT PROMPT
--------------
[Quote user's prompt exactly]
TECHNIQUE SCORECARD
-------------------
| # | Technique | Status | Issue/Note |
|---|-----------|--------|------------|
| 1 | Chain-of-Thought | x/!/+/- | [Issue if x/!, "Good" if +, "N/A" if -] |
| 2 | Structured Output | | |
| 3 | Few-Shot | | |
| 4 | Placement | | |
| 5 | Salience | | |
| 6 | Roles | | |
| 7 | Positive Framing | | |
| 8 | Reasoning-First | | |
| 9 | Verbalized Sampling | | |
| 10 | Self-Reflection | | |
Legend: + Present | ! Partial | x Missing | - N/A for this task
TOP 3 IMPROVEMENTS
------------------
(Prioritize core techniques; include extended if highly relevant)
1. [Technique]: [Specific improvement]
Before: [Original snippet]
After: [Improved snippet]
Why: [1-sentence explanation]
2. [Technique]: [Specific improvement]
...
3. [Technique]: [Specific improvement]
...
OPTIMIZED PROMPT
----------------
[Full rewritten prompt applying all improvements]
---------------------------------
QUALITY SUMMARY
- Improvement potential: [High/Medium/Low]
- Techniques applied: [List]
- Target model: [If specified, note model-specific adjustments]
---------------------------------
Next steps:
- Want me to explain any technique in depth? (Mode C)
- Want to iterate on the optimized prompt?
- Targeting a specific model? I can adjust for its quirks.
Mode B: Craft
Disposition: Build a prompt that activates expert behavior. The common error: mechanically correct prompts that generate "probability-averaged centroid output" — the bland average of all expert responses.
Process:
-
Elicit requirements -- ASK the user these questions before drafting:
- "What task should this prompt accomplish?"
- "What model will run this? (Claude, GPT, DeepSeek, etc.)"
- "What output format do you need?"
- "Any specific constraints or requirements?"
- "Should the prompt default to implementing or recommending?" (Action Bias)
- "Is this a single-turn prompt or part of an agentic workflow?" (Context)
- "Who is the audience? Expert peers, beginners, or mixed?" (Audience Priming)
Wait for answers before proceeding. Don't assume.
If the answer to #6 is "agentic workflow," apply the agentic template from the Agentic Prompting section.
-
Select techniques matching task type (reasoning -> CoT/Reasoning-First; structured data -> Structured Output/Format-Spec; complex -> Decomposition/Few-Shot; consistency -> Self-Reflection)
-
Draft, self-check against technique checklist, output with rationale
Craft Output Template
CRAFTED PROMPT
==============
REQUIREMENTS UNDERSTOOD
-----------------------
- Task: [What the prompt should accomplish]
- Target model: [Claude/GPT/etc. or "general"]
- Output format: [Expected format]
- Constraints: [Any limitations]
TECHNIQUES APPLIED
------------------
- [Technique 1]: [Why it's relevant]
- [Technique 2]: [Why it's relevant]
- ...
THE PROMPT
----------
[Full optimized prompt]
RATIONALE
---------
[Brief explanation of key design choices]
---------------------------------
QUALITY SUMMARY
- Techniques applied: [Count]/10 core
- Model-specific: [Yes/No - what adjustments]
- Confidence: [High/Medium/Low]
---------------------------------
Next steps:
- Want to test this prompt and iterate?
- Should I explain any of the techniques used?
- Need a different approach?
Mode C: Teach
Load reference/[technique-name].md for the requested technique. Present mechanism, deep example, model-specific notes. Offer practice exercise.
Mode D: Quick Fix
Read, identify 3 highest-impact improvements, apply immediately, output with bullet-point changes. Speed over depth.
Quick Fix Output Template
QUICK FIX
=========
CHANGES MADE
------------
- [Change 1]: [One-line description]
- [Change 2]: [One-line description]
- [Change 3]: [One-line description]
IMPROVED PROMPT
---------------
[Full improved prompt]
Want deeper analysis? Try mode A.
Agentic Prompting
Tool Description Optimization
See reference/extended/tool-description-craft.md. Make implicit context explicit, use human-readable return values, consolidate tools.
Subagent Briefing Pattern
Every subagent prompt must include:
- Context -- What the task is and why it matters
- Constraints -- Time budget, scope limits, effort level
- Output format -- What you need back, exactly
- Success criteria -- How to know when done
ReAct Loop Construction
Reason -> Act -> Observe -> Reason. See reference/extended/react-loop.md.
Action Bias Selection
Choose one per prompt -- be explicit about what the agent should default to:
- Proactive: "Implement changes rather than suggesting them"
- Conservative: "Default to research and recommendations"
- Balanced: "Implement straightforward changes; recommend for complex ones"
Context Window Management
See reference/extended/context-engineering.md and reference/extended/multi-session.md.
- Just-in-time loading over pre-loading
- Compaction strategies: summarize, clear tool results, full reset
Clarity over Compulsion (Claude 4.6 and 4.7)
4.6's failure mode was over-triggering on "CRITICAL: You MUST…" language. 4.7's failure mode is the opposite: it follows literal MUST statements too rigidly and can ignore context signals that would soften the rule. Same direction of fix, different reason.
- Drop "CRITICAL: You MUST…" scaffolding — let the instruction stand on its own. Reserve CAPS imperatives for genuinely load-bearing rules (e.g., iron laws in systematic-debugging)
- Drop anti-laziness prompts ("be thorough", "don't be lazy") — both models execute proactively; the pressure causes overthinking
- Use the
effort parameter for reasoning depth (xhigh for agentic work, high for knowledge work) instead of prompt-level simulation
- Drop explicit "think step by step" — adaptive thinking handles this; your prompt just competes with it
- Soften tool-triggering: "use when helpful" lets the model calibrate; "MUST use when X" fires literally and burns tokens on unnecessary calls
- At low/medium effort, 4.7 scopes tightly — add one targeted reasoning nudge where depth matters: "This step involves multi-step reasoning. Outline your logic before responding."
Model-Specific Adjustments
When targeting a specific model, load reference/models/{model}.md and apply adjustments.
Prompt Quality Gate
For high-stakes prompts (production, external APIs):
Core Techniques Quick Reference
| # | Technique | Impact | One-Line Summary |
|---|
| 1 | Chain-of-Thought | +40% accuracy | "Think step by step before answering" |
| 2 | Structured Output | 99%+ compliance | Constrain to JSON/XML schema |
| 3 | Few-Shot Examples | +15-30% specificity | Show 2-5 input/output examples |
| 4 | Placement | +50% retrieval | Critical info at start/end, not middle |
| 5 | Salience | +23-31% compliance | XML tags, caps, explicit labels |
| 6 | Roles | +10-20% domain accuracy | Assign persona with expertise |
| 7 | Positive Framing | +15-20% compliance | "Do X" instead of "Don't Y" |
| 8 | Reasoning-First | -20-30% hallucination | Evidence before conclusion |
| 9 | Verbalized Sampling | +1.6-2.1x diversity | Multiple variants with probabilities |
| 10 | Self-Reflection | +15-25% accuracy | Ask model to critique and revise |
Failure if missing: CoT → skips reasoning steps. Structured Output → format drift. Few-Shot → calibration gap. Placement → buried instructions. Salience → constraints overlooked. Roles → generic register. Positive Framing → constraint confusion. Reasoning-First → hallucinated conclusions. Verbalized Sampling → centroid output. Self-Reflection → uncaught errors.
For deep dives: Use Teach mode (C) or see reference/[technique].md
Extended Techniques
Available in reference/extended/:
| Technique | When to Use |
|---|
| Decomposition | Break complex tasks into sequential steps |
| Compression | Reduce context size while preserving utility |
| Sufficiency | Ensure model has what it can't infer |
| Scope | Set explicit boundaries on what to include/exclude |
| Format-Spec | Provide exact output template |
| Uncertainty + Epistemic Labels | Confidence per claim: [E]vidence/[L]ogical/[S]peculation/[C]ontrarian |
| Chaining | Multi-stage prompts where outputs feed next stage |
| Self-Consistency | Multiple samples with majority voting |
| Tree-of-Thoughts | Explore multiple reasoning branches |
| ReAct Loop | Reason-Act-Observe cycles for tool-using agents |
| Tool Description Craft | Optimize tool/function descriptions for agents |
| Context Engineering | Curate optimal token set during inference |
| Multi-Session | State persistence across context windows |
| Negative Space Definition | Stack "this is NOT X" negations to close attractor basins |
| Permission Escalation | Graduated permission ladder to open RLHF-closed output regions |
Model-Specific Guidance
Command: *model [name] or ask about prompting for a specific model.
Available: Claude, OpenAI (GPT-5.x, o1/o3), DeepSeek, Gemini, Kimi, Qwen
Location: reference/models/{name}.md (per-model files for JIT loading)
Critical differences by model type:
| Model Type | Chain-of-Thought | Few-Shot | System Prompt |
|---|
| Standard (Claude, GPT-5.x) | Add manually / use reasoning.effort | Helpful | Yes |
| Reasoning (o1/o3, R1) | Built-in - don't add | Hurts performance | Developer role |
| Agentic (Kimi K2) | Automatic | Varies | Goal-oriented |
Checklist for Skill Authors
When creating prompts in skills or commands:
