Code Correction

Code Correction - Fix Failed Verification

Correct code that failed verification so it passes acceptance criteria.

Critical Distinction: This corrects code to meet requirements (stories remain unchanged). See CLAUDE.md section "Quality Assurance Terminology" for definitions.

Announce: On activation, say: "I'm using the code-correction skill to fix the implementation so it passes verification."

Database: .claude/data/story-tree.db

Critical: Use Python sqlite3 module, NOT sqlite3 CLI.

Purpose

Bridge the gap between failed verification and passing verification by correcting the code implementation. Stories and acceptance criteria remain the source of truth - only the code changes.

When to Use

• After code-verification reports FAIL
• When story stage is verifying with hold_reason='broken'
• When acceptance criteria tests fail
• When user says "fix verification failures", "correct the code", "make tests pass"

Workflow

Step 1: Load Verification Failure Report

Query story with failed verification:

python -c "
import sqlite3, json
conn = sqlite3.connect('.claude/data/story-tree.db')
conn.row_factory = sqlite3.Row
stories = [dict(row) for row in conn.execute('''
    SELECT s.id, s.title, s.description, s.stage, s.hold_reason, s.notes,
        (SELECT MIN(depth) FROM story_paths WHERE descendant_id = s.id) as node_depth
    FROM story_nodes s
    WHERE s.stage = 'verifying'
      AND s.hold_reason = 'broken'
      AND s.terminus IS NULL
    ORDER BY node_depth ASC
''').fetchall()]
print(json.dumps(stories, indent=2))
conn.close()
"

Extract failure details from notes:

• Parse verification report embedded in notes
• Identify which criteria failed
• Identify which tests failed
• Extract error messages and stack traces

Step 2: Analyze Failure Root Cause

For each failed criterion/test:

1. Read the failing test code to understand expected behavior
2. Read the implementation code to understand current behavior
3. Identify the gap between expected and actual

Failure categories:

Step 3: Plan Corrections

Before making changes, create a correction plan:

## Correction Plan for Story [ID]

### Failure 1: [Criterion/Test name]
- **Root Cause:** [Description of why it fails]
- **Files to Modify:** [List of files]
- **Changes Required:** [Description of changes]
- **Verification Method:** [How to confirm fix]

### Failure 2: ...

Planning rules:

• Address failures in dependency order (if A depends on B, fix B first)
• Keep changes minimal - fix only what's broken
• Avoid refactoring - this is correction, not improvement
• Don't change tests - the tests define expected behavior

Step 4: Implement Corrections

For each failure in the plan:

1. Make minimal code changes to fix the specific failure
2. Run the specific failing test to verify the fix
3. Run related tests to check for regressions
4. Commit the fix with a clear message

Commit message format:

fix: [criterion/test name] - [brief description]

Story: [ID]
Criterion: [number]

Step 5: Re-verify

After all corrections:

python .claude/skills/code-verification/generate_report.py <story_id>

If verification passes:

• Update story stage and clear hold
• Report success

If verification still fails:

• Analyze new failures
• Return to Step 2 (max 3 iterations)
• If still failing after 3 attempts, escalate to human

Step 6: Update Story Status

On successful correction:

python -c "
import sqlite3
from datetime import datetime
conn = sqlite3.connect('.claude/data/story-tree.db')
conn.execute('''
    UPDATE story_nodes
    SET hold_reason = NULL,
        notes = notes || '\n[Correction ' || datetime('now') || '] Fixed verification failures',
        updated_at = datetime('now')
    WHERE id = ?
''', ('STORY_ID',))
conn.commit()
conn.close()
"

On correction failure (max attempts reached):

python -c "
import sqlite3
from datetime import datetime
conn = sqlite3.connect('.claude/data/story-tree.db')
conn.execute('''
    UPDATE story_nodes
    SET hold_reason = 'escalated',
        human_review = 1,
        notes = notes || '\n[Correction ' || datetime('now') || '] Unable to fix after 3 attempts - escalating',
        updated_at = datetime('now')
    WHERE id = ?
''', ('STORY_ID',))
conn.commit()
conn.close()
"

Mode Detection

CI Mode activates when:

• Environment variable CI=true is set, OR
• Trigger phrase includes "(ci)" like "correct code (ci)"

CI Mode behavior:

• No confirmation prompts
• Structured JSON output
• Auto-escalate after max attempts

Interactive Mode (default):

• Present correction plan for approval
• Pause between corrections for review
• Allow human to guide approach

Output Format

Correction Report (YAML)

correction_report:
  story_id: "1.2.3"
  title: "Feature Title"
  corrected_at: "2025-12-29T10:30:00Z"

  failures_addressed:
    - criterion: "User can export data as CSV"
      root_cause: "Missing CSV header row"
      files_modified: ["src/exporter.py"]
      tests_fixed: ["tests/test_export.py::test_csv_header"]

    - criterion: "Export includes timestamps"
      root_cause: "Timestamp format incorrect"
      files_modified: ["src/exporter.py"]
      tests_fixed: ["tests/test_export.py::test_timestamp_format"]

  verification_result: pass  # pass | fail
  attempts: 1
  commits: ["abc1234"]

CI Mode Output (JSON)

Success:

{
  "story_id": "1.7",
  "status": "corrected",
  "failures_fixed": 3,
  "attempts": 1,
  "commits": ["abc1234", "def5678"],
  "verification_result": "pass"
}

Failure (escalated):

{
  "story_id": "1.7",
  "status": "escalated",
  "failures_remaining": 1,
  "attempts": 3,
  "reason": "Unable to fix criterion 2 after 3 attempts",
  "recommendation": "human_review"
}

Correction Principles

DO

• Fix the code - never modify acceptance criteria or tests
• Keep changes minimal - only fix what's broken
• Preserve test intent - tests define correct behavior
• Document root causes - help future debugging
• Verify after each fix - catch regressions early

DO NOT

• Modify acceptance criteria to match broken code
• "Fix" tests to pass with broken implementation
• Refactor unrelated code while correcting
• Add features while correcting bugs
• Skip verification after corrections

Exit Conditions

Related Skills

• code-verification: Identifies failures this skill corrects
• debug-orchestrator: Handles complex debugging scenarios
• code-review: Reviews code quality (separate from verification)
• human-validation: Human judgment after verification passes

References

• Verification Skill: .claude/skills/code-verification/SKILL.md
• Database: .claude/data/story-tree.db
• QA Terminology: CLAUDE.md (Quality Assurance Terminology section)