enhance

name	enhance
description	Enhance a prompt for better outputs with analysis and optimization
allowed-tools	Read, Grep, Glob
capabilities	{"schema_version":1,"read_files":true,"search_code":true,"write_files":false,"execute_commands":false,"web_access":false,"user_interaction":false,"agent_spawn":false,"task_management":false}
cost-profile	lightweight

Enhancing Prompts

Analyze prompt quality, classify task type, and enhance with optimized templates while preserving original intent. Provides transparent feedback showing what was enhanced and why. - `prompt`: The user's original prompt to analyze and enhance - `--analyze-only`: (optional) Show analysis without enhancement - `--task-type `: (optional) Force a specific task type

Phase 1: Analysis

Analyze the prompt for components using the PTCF framework (Persona + Task + Context + Format).

Component Detection

Component	Patterns	Weight
Persona	^(act as|you are|as a|pretend|imagine you're)	2
Task	Action verbs: (create|review|analyze|fix|summarize|write|debug|refactor|optimize|draft|investigate|compare)	3 (required)
Context	@\w+, file paths (\.ts|\.js|\.py|\.md), "given that", "based on", "from the", "in the"	3
Format	(as bullets|in JSON|formatted as|limit to|with examples|step by step|list|table)	2

Quality Score Calculation

score = 0
if has_task_verb: score += 3
if has_context: score += 3
if has_format: score += 2
if has_persona: score += 2
return score  # 0-10

Gap Identification

For each missing component, generate specific suggestions:

• Missing Persona → "Consider specifying who the AI should act as"
• Missing Context → "Add relevant files, constraints, or background"
• Missing Format → "Specify desired output structure"

Phase 2: Classification

Classify the prompt into a task type for template selection.

Classification Rules (priority order)

Task Type	Trigger Patterns	Confidence Boost
debugging	"fix", "debug", "error", "broken", "not working", "fails"	+0.3 if has error message
code_review	"review", "check", "audit" + code file refs	+0.2 if has file paths
refactoring	"refactor", "improve", "optimize", "clean up"	+0.2 if mentions patterns
summarization	"summarize", "tldr", "brief", "key points", "overview"	+0.2 if has source
research	"analyze", "investigate", "compare", "research", "explore"	+0.2 if multiple subjects
generation	"create", "write", "draft", "generate", "make"	default
general	(fallback)	0.5

Classification Algorithm

classify_prompt(prompt):
  scores = {}
  for task_type, patterns in CLASSIFICATION_RULES:
    base_score = count_pattern_matches(prompt, patterns) / len(patterns)
    boost = calculate_boost(prompt, task_type)
    scores[task_type] = min(1.0, base_score + boost)

  best = max(scores, key=scores.get)
  confidence = scores[best]

  if confidence < 0.3:
    return ("general", 0.5)
  return (best, confidence)

Phase 3: Enhancement

Load the task-specific template and merge with original prompt.

Template Loading

Templates are located at: resources/templates/{task_type}.yaml

Merge Algorithm

enhance_prompt(original, template, analysis):
  enhanced = original

  # Add persona if missing (prepend)
  if not analysis.components.persona:
    enhanced = template.persona.default + "\n\n" + enhanced

  # Add format if missing (append)
  if not analysis.components.format:
    enhanced = enhanced + "\n\n" + template.format.default

  # Add constraints (append as list)
  for constraint in template.constraints:
    if constraint not in enhanced:
      enhanced = enhanced + "\n- " + constraint

  # Preserve original intent - original text always wins
  return enhanced

Phase 4: Output

Present the enhancement results transparently.

Output Structure

## Prompt Enhancement Analysis

### Quality Score: {before}/10 → {after}/10

### Original Prompt
> {original text}

### Detected Components
| Component | Status | Details |
|-----------|--------|---------|
| Persona | ❌/✅/⚠️ | {details} |
| Task | ❌/✅/⚠️ | {details} |
| Context | ❌/✅/⚠️ | {details} |
| Format | ❌/✅/⚠️ | {details} |

### Task Type
`{task_type}` (confidence: {confidence})

### Enhanced Prompt
> {enhanced text}

### Suggestions for Next Time
1. {suggestion 1}
2. {suggestion 2}
3. {suggestion 3}

Status Icons

• ✅ Present - Component detected
• ⚠️ Partial - Component weak or incomplete
• ❌ Missing - Component not found, will be added

When `--analyze-only` is specified: 1. Perform Phase 1 (Analysis) and Phase 2 (Classification) 2. Skip Phase 3 (Enhancement) 3. Output analysis only: - Quality score - Component detection table - Task type with confidence - Suggestions for improvement - No enhanced prompt shown Load templates from: `resources/templates/{task_type}.yaml`

Available task types:

• code_review
• summarization
• research
• generation
• refactoring
• debugging
• general (fallback)

Check `.loa.config.yaml` for settings: ```yaml prompt_enhancement: enabled: true auto_enhance_threshold: 4 show_analysis: true max_refinement_iterations: 3 ```

If enabled: false, return original prompt unchanged. If score >= auto_enhance_threshold, skip enhancement (prompt is good enough).

When output fails (detected via error patterns or user rejection): 1. Log feedback type (runtime_error, verification_failure, user_rejection, partial_success) 2. Map to refinement action per `resources/feedback.md` 3. Re-analyze with feedback context 4. Apply refinement up to `max_refinement_iterations` ## Feedback-Driven Refinement Loop

When a prompt produces unsatisfactory output, the refinement loop improves the prompt iteratively.

Trigger Detection

Detect failure signals from:

• runtime_error: Exception patterns, exit codes, stack traces
• verification_failure: Test failures, validation errors
• user_rejection: "no", "wrong", "try again", negative feedback
• partial_success: "almost", "close but", "except for"

Refinement Process

1. Detect feedback type from user response or tool output
2. Load refinement actions for that feedback type:
   - runtime_error → add error context, specify constraints
   - verification_failure → add test requirements, specify behavior
   - user_rejection → request clarification, narrow scope
   - partial_success → focus on gap, add targeted constraint
3. Apply actions in priority order
4. Re-analyze refined prompt
5. If quality >= 7: return refined prompt
6. Else if iterations < max: repeat from step 1
7. Else: return best attempt with suggestions

Iteration Limits

• Default maximum: 3 iterations
• Configurable via prompt_enhancement.max_refinement_iterations
• Hard stop prevents infinite loops
• Each iteration logged to trajectory

Refinement History

When showing output, include refinement history if applicable:

### Refinement History
| Iteration | Feedback | Action | Score |
|-----------|----------|--------|-------|
| 1 | user_rejection | Added persona | 4→6 |
| 2 | partial_success | Added format | 6→8 |

Success Criteria

Refinement succeeds when:

• Quality score reaches 7+ (good prompt)
• User accepts the output
• Task completes without errors

Refinement fails when:

• Maximum iterations reached
• Same feedback type repeats 3 times (stuck)
• User requests to stop refining