Enhancing Prompts

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

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

code

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

code

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

code

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

markdown

## 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

</workflow> <analyze-only-mode> 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 </analyze-only-mode> <templates> Load templates from: `resources/templates/{task_type}.yaml`

Available task types:

•code_review
•summarization
•research
•generation
•refactoring
•debugging
•general (fallback) </templates>

<configuration> 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). </configuration>

<feedback-integration> 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-integration> <refinement-loop> ## 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

code

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:

markdown

### 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 </refinement-loop>