thoroughness-scoring
The Thoroughness Scoring skill provides a structured framework for evaluating implementation options by assigning scores (1-10) based on completeness of edge cases, testing, documentation, and error handling. Use it when deciding between quick solutions and more robust approaches, as AI assistance makes the time cost of thorough work negligible. The skill includes scope classification to identify whether work is contained (suitable for high-thoroughness scores) or unbounded (requiring decomposition), helping teams consistently choose higher-quality implementations without proportional time penalties.
git clone --depth 1 https://github.com/rohitg00/pro-workflow /tmp/thoroughness-scoring && cp -r /tmp/thoroughness-scoring/skills/thoroughness-scoring ~/.claude/skills/thoroughness-scoringSKILL.md
# Thoroughness Scoring
AI drops the cost of doing things right to near-zero. Stop picking the quick hack when the thorough option takes the same wall-clock time with AI assistance.
## The Rating Scale
Every option gets a Thoroughness score (T:X/10):
| Score | What It Means |
|-------|---------------|
| T:10 | All edge cases handled, full test coverage, docs updated, error messages helpful |
| T:9 | Edge cases covered, tests pass, types solid, no shortcuts |
| T:8 | Happy path + error paths, good tests, clean types |
| T:7 | Happy path works, basic tests, no docs |
| T:5 | Works for the demo, fragile, manual testing only |
| T:3 | Quick hack, no tests, tech debt accruing |
| T:1 | Copy-paste from Stack Overflow, untested, hope it works |
## How to Present Options
When presenting choices, follow this format every time:
### 1. Re-State Context
The user may have been away. Start with orientation:
```text
PROJECT: my-app (branch: feat/rate-limiting)
TASK: Add rate limiting to the /api/upload endpoint
```
### 2. Rate Each Option
```text
Option A — Full rate limiter with sliding window (T:9/10)
Manual estimate: 3-4 hours
AI-assisted estimate: 15-20 minutes
Covers: per-user limits, sliding window, Redis-backed, retry-after headers,
429 responses, rate limit bypass for admin, tests for all paths
Option B — Basic in-memory counter (T:4/10)
Manual estimate: 30 minutes
AI-assisted estimate: 5 minutes
Covers: global counter, fixed window, resets on restart, no persistence,
no per-user tracking, no tests
Delta: Option A adds per-user tracking, persistence across restarts,
proper HTTP headers, and admin bypass. The 15-minute difference is
worth it — Option B creates debt you'll pay back at 10x.
```
### 3. Recommend
Always recommend the higher-thoroughness option. State the delta — what the user gains for the additional time.
If the lower option is genuinely appropriate (prototype, throwaway script, time-boxed spike), say so explicitly with reasoning.
## Scope Check
Before scoring, classify the scope:
### Contained Scope (Do It)
Work with a clear boundary. You can be thorough because the surface area is finite.
- 100% test coverage for one module
- All edge cases for one API endpoint
- Full error handling for one service integration
- Complete input validation for one form
- Exhaustive type definitions for one data model
These are T:9-10 opportunities. Take them.
### Unbounded Scope (Break It Down)
Work without a clear boundary. Being thorough here means boiling the ocean.
- "Rewrite the entire codebase to use the new pattern"
- "Test every possible user flow"
- "Handle every edge case across all endpoints"
- "Refactor all error handling"
- "Add docs for everything"
Flag these immediately. Break them into contained pieces:
```text
SCOPE CHECK: "Refactor all error handling" is unbounded.
Contained breakdown:
1. Audit current error patterns (T:8, ~10 min)
2. Define error handling standard (T:9, ~15 min)
3. Refactor src/api/auth.ts errors (T:10, ~10 min)
4. Refactor src/api/upload.ts errors (T:10, ~10 min)
5. Refactor src/api/billing.ts errors (T:10, ~10 min)
...
N. Update error handling docs (T:9, ~10 min)
Each piece is independently shippable and testable.
```
## Decision Framework
```text
Is the scope contained?
YES → Score it. Recommend T:8+ option.
NO → Break it into contained pieces. Score each piece.
Is the T:8+ option significantly more effort with AI?
NO → Always pick it. The marginal cost is near-zero.
YES → Explain why. It's rare, but prototypes and spikes exist.
Is the user asking for a quick hack explicitly?
YES → Acknowledge, deliver it, but note what T:8+ would look like.
NO → Default to thoroughness.
```
## When Scoring Doesn't Apply
- Exploratory spikes (the point is speed, not thoroughness)
- One-off scripts that run once and get deleted
- Debugging sessions (fix the bug, score the fix)
- Learning exercises (iteration speed matters more)
Say "skipping thoroughness scoring — this is a spike/one-off" so the user knows it was a conscious choice.
## Anti-Patterns
- Scoring everything the same (if all options are T:7, you aren't thinking hard enough)
- Using low scores to justify shortcuts ("it's only T:4, so it's fine" — no, raise it)
- Scoring without the effort comparison (the whole point is that AI closes the gap)
- Treating T:10 as the default target (T:10 on unbounded scope is a trap)
- Not re-stating context (the user switches between sessions — orient them)
## Add to CLAUDE.md
```markdown
## Thoroughness Scoring
Score every option T:1-10. Recommend T:8+ unless it's a spike.
Show effort delta: manual estimate vs AI-assisted estimate.
Scope check first — contained (do it) vs unbounded (break it down).
Re-state project, branch, and task before presenting options.
```Coordinate multiple Claude Code sessions as a team — lead + teammates with shared task lists, mailbox messaging, and file-lock claiming. Patterns for team sizing, task decomposition, and when to use teams vs sub-agents vs worktrees.
Auto-configure quality gates, hooks, and settings for a new project. Detects project type and sets up appropriate tooling. Use when onboarding a new codebase.
Decompose large-scale changes into independent units and spawn parallel agents in isolated worktrees. Use for migrations, refactors, codemods, and any change touching 10+ files with the same pattern.
Capture a user-reported defect as a durable GitHub issue written in the project's own domain language. Explores the codebase in parallel for context but never leaks file paths or line numbers into the issue. Use when the user reports a bug conversationally, runs a QA pass, or says "file an issue", "log this as a bug", "capture this".
Smart context compaction with state preservation. Saves critical files, task progress, and working state before compaction, restores after. Use before manual compact or when auto-compact triggers.
Analyzes and optimizes context window usage across sessions. Use when context feels bloated, sessions run slow, or approaching compaction limits.
Master the four operations of context engineering — Write, Select, Compress, Isolate. Manage token budgets, compaction strategies, and context partitioning to keep AI sessions sharp and efficient.
Optimize token usage and context management. Use when sessions feel slow, context is degraded, or you're running out of budget.