Thoroughness Scoring
SkillDev toolsLets your agent rate decision options 1-10 for thoroughness and pick the higher-rated one.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Thoroughness Scoring skill
About this capability
Score every decision point with a Thoroughness Rating (1-10). AI makes the marginal cost of doing things properly near-zero — pick the higher-rated option every time. Includes scope checks to distinguish contained vs unbounded work.
What this skill tells your AI
The instructions your AI receives, as published by rohitg00/pro-workflow in skills/thoroughness-scoring/SKILL.md and read by ahel’s review.
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:
PROJECT: my-app (branch: feat/rate-limiting)
TASK: Add rate limiting to the /api/upload endpoint
2. Rate Each Option
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:
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
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
## 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.
Signals
- GitHub stars
- 3k
- Forks
- 285
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
thoroughness-scoring- Source
- github.com/rohitg00/pro-workflow