# 1765 Feature Completeness Checker 9efa14fd

> Task breakdown for implementing the completeness checker.

- Skill: `tools-only/1765-feature-completeness-checker-9efa14fd` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/1765-feature-completeness-checker-9efa14fd`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/1765-feature-completeness-checker-9efa14fd/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/1765-feature-completeness-checker-9efa14fd

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# Project Planning & Task Breakdown

## Milestones
**What are the major checkpoints?**

- [x] Milestone 1: Basic matching logic implemented (Done).
- [x] Milestone 2: Detailed failure reporting and score calculation (Done).
- [x] Milestone 3: Fuzzy matching for resilience against noise (Done).
- [x] Milestone 4: 100% Score achieved on real integration fixtures (Done).

## Task Breakdown
**What specific work needs to be done?**

### Phase 1: Foundation
- [x] Task 1.1: Define `ValidationFailure` and `ValidationResult` dataclasses (Done).
- [x] Task 1.2: Implement `CompletenessChecker` class skeleton (Done).
- [x] Task 1.3: Integrate `text_segmenter` inside the checker (Done).

### Phase 2: Core Logic
- [x] Task 2.1: Implement the search loop with sliding search cursor (Done).
- [x] Task 2.2: Add score calculation and noise filtering (Done).
- [x] Task 2.3: Implement `_fuzzy_check` for token-based matching to handle embedded noise/artifacts.

### Phase 3: Testing & Optimization
- [x] Task 3.1: Unit tests with synthetic strings (Done).
- [x] Task 3.2: Integration test using real PDF/EPUB fixtures (Done - 100% score).
- [x] Task 3.3: Debug and resolve false negatives caused by header/footer artifacts.

## Timeline & Estimates
- Completed 2025-12-30.

## Risks & Mitigation
- **Risk:** Slight extraction differences blocking exact matches.
- **Mitigation:** Implemented token-based (85% threshold) fuzzy search fallback.
- **Risk:** High memory usage for very large books.
- **Mitigation:** The checker uses a sliding window search `current_pos` and local fuzzy search window (10k chars) to keep performance high.