Log Manager Skill

Log Manager Skill

You are a coordination skill - you don't perform operations directly, but delegate to operation skills (log-writer, log-classifier, log-validator, log-lister) and manage the workflow state between steps.

Difference from log-director-skill:

• log-manager-skill: Single-log pipelines (sequential workflows for one log)
• log-director-skill: Multi-log workflows (parallel execution across many logs)

Workflow types:

1. create-log (classify + write + validate)

   • content or data - Log content/data
   • log_type - Optional (will classify if not provided)
   • auto_validate - Validate after creation (default: true)

2. validate-and-fix (validate + fix issues + revalidate)

   • log_path - Log to validate
   • auto_fix - Attempt automatic fixes (default: false)
   • fix_strategies - Which fixes to apply (redaction, formatting, etc.)

3. reclassify-log (classify + update type + revalidate)

   • log_path - Log to reclassify
   • force - Reclassify even if already typed
   • confidence_threshold - Minimum confidence to apply (default: 70)

4. archive-log (validate + update status + move to archive)

   • log_path - Log to archive
   • skip_validation - Skip validation before archive (default: false)
   • retention_check - Verify retention policy allows archive

Example request:

{
  "workflow": "create-log",
  "content": "Test execution completed: 48 tests, 45 passed, 3 failed",
  "metadata": {"command": "pytest", "exit_code": 1},
  "auto_validate": true
}

Step 1: Classify Log Type

If log_type not provided:

• Invoke log-classifier skill with content + metadata
• Get recommended type and confidence score
• If confidence < 70, use _untyped with review flag

Step 2: Write Log

• Invoke log-writer skill with:
  • log_type (from classification or user-provided)
  • data (content + metadata)
  • title (extract or generate)
• Receive log_path and log_id

Step 3: Validate Log (if auto_validate)

• Invoke log-validator skill with:
  • log_path (from step 2)
  • validation_level = "standard"
• Check validation status
• Report any errors/warnings

Step 4: Return Result

{
  "workflow": "create-log",
  "status": "completed",
  "steps": {
    "classify": {"type": "test", "confidence": 95},
    "write": {"path": ".fractary/logs/test/test-001.md", "log_id": "test-001"},
    "validate": {"status": "passed", "warnings": 0}
  },
  "result": {
    "log_path": ".fractary/logs/test/test-001.md",
    "log_id": "test-001",
    "log_type": "test"
  }
}

Workflow: validate-and-fix

Step 1: Validate Log

• Invoke log-validator skill
• Collect all errors, warnings, info

Step 2: Analyze Fixable Issues (if auto_fix)

Execute scripts/analyze-fixes.sh:

• Categorize issues by fix strategy:
  • auto-fixable: Missing optional fields, formatting issues
  • semi-auto: Redaction (can detect, user confirms)
  • manual: Logic errors, missing required data

Step 3: Apply Fixes (if authorized)

Execute scripts/apply-fixes.sh:

• Add missing optional fields with defaults
• Fix formatting (whitespace, headers)
• Apply redaction to detected secrets
• Update frontmatter if needed

Step 4: Revalidate

• Invoke log-validator skill again
• Compare before/after error counts
• Report improvements

Workflow: reclassify-log

Step 1: Read Current Log

• Parse existing log
• Extract current log_type

Step 2: Classify

• Invoke log-classifier skill
• Get recommended type

Step 3: Check Confidence

• If recommended_type == current_type, no change needed
• If confidence < threshold, report uncertainty, don't reclassify
• If confidence >= threshold, proceed

Step 4: Update Log Type

• Update frontmatter log_type field
• Move file to correct type directory if needed
• Preserve log_id

Step 5: Revalidate

• Validate against new type's schema and rules
• Report any new issues

Workflow: archive-log

Step 1: Validate (unless skipped)

• Ensure log is valid before archiving
• Check for critical errors

Step 2: Check Retention Policy

Execute scripts/check-retention.sh:

• Load type's retention-config.json
• Calculate retention expiry
• Verify archive is appropriate

Step 3: Update Status

• Set frontmatter status to "archived"
• Add archive_date field

Step 4: Move to Archive (future)

• For now, just update status
• Future: Move to separate archive directory

STEP 1/3: Classify log type → Invoking log-classifier... ✓ Type: test (confidence: 95%)

STEP 2/3: Write log → Invoking log-writer... ✓ Created: .fractary/logs/test/test-001.md

STEP 3/3: Validate log → Invoking log-validator... ✓ Validation passed (0 errors, 0 warnings)

✅ COMPLETED: Log Manager Skill Workflow: create-log (success) Log created: .fractary/logs/test/test-001.md Type: test | Status: completed | Valid: ✓ ─────────────────────────────────────── Next: Use log-lister to view all test logs, or log-archiver when ready to archive

</OUTPUTS>

<DOCUMENTATION>
Write to execution log:
- Operation: workflow execution
- Workflow type: {type}
- Steps completed: {list}
- Final status: success/partial/failed
- Log path: {path}
- Timestamp: ISO 8601
</DOCUMENTATION>

<ERROR_HANDLING>
**Step failure (with rollback):**

❌ WORKFLOW FAILED: create-log Failed at: Step 2 (write log) Error: Template rendering failed - missing variable 'title'

Rollback actions:

• Step 1 (classify): No rollback needed (read-only)
• Step 2 (write): Removed partial file (if created)

Workflow status: failed Suggestion: Provide 'title' field in data or allow auto-generation

**Partial success:**

⚠️ WORKFLOW PARTIAL: validate-and-fix Completed steps: ✓ Step 1: Validation (found 3 errors) ✓ Step 2: Analyze fixes (2 auto-fixable) ✗ Step 3: Apply fixes (user confirmation required)

Status: awaiting user input Fixable errors: 2 Manual errors: 1 (requires data)

**Confidence too low:**

⚠️ WORKFLOW UNCERTAIN: reclassify-log Classification confidence: 45% (< 70% threshold) Recommended type: operational Alternative: _untyped (score: 38)

Action: Keeping current type (debug) Suggestion: Add more context or metadata to improve classification

</ERROR_HANDLING>

## Scripts

This skill uses three supporting scripts:

1. **`scripts/execute-workflow.sh {workflow_type} {params_json}`**
   - Orchestrates workflow execution
   - Calls operation skills in sequence
   - Manages state between steps
   - Returns workflow result

2. **`scripts/analyze-fixes.sh {validation_errors_json}`**
   - Categorizes validation errors by fix strategy
   - Determines auto-fixable vs manual issues
   - Returns fix recommendations

3. **`scripts/apply-fixes.sh {log_path} {fixes_json}`**
   - Applies automated fixes to log
   - Updates frontmatter and body as needed
   - Creates backup before modification
   - Returns fix results