thoroughness-scoring
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.
Install / Use
npx skills add rohitg00/pro-workflow --skill thoroughness-scoringInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Development & EngineeringSupported Platforms
Tags
Our assessment of thoroughness-scoring
thoroughness-scoring scores 90/100 on our quality scale, 918th of 3,554 Development & Engineering skills we index (top 26%).
Its SKILL.md is 5.0 KB long, well organised into 14 sections with 5 code examples: a solid amount of guidance for an agent.
With 2,876 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 5 days ago, so thoroughness-scoring is actively maintained.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
thoroughness-scoring compared with similar skills
All 4 of these similar skills score higher than thoroughness-scoring; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| thoroughness-scoring (this skill)by rohitg00 | 90 | 2.9k | 5d ago | SKILL.md |
| ai-job-searchby MadsLorentzen | 100 | 44.4k | 1d ago | CLAUDE.md |
| claude-howtoby luongnv89 | 100 | 41.7k | 3d ago | CLAUDE.md |
| algorithmic-artby anthropics | 100 | 177.9k | 7d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 7d ago | SKILL.md |
Frequently asked questions
- How do I install thoroughness-scoring?
- Run
npx skills add rohitg00/pro-workflow --skill thoroughness-scoring. The install tabs above show the steps for each supported agent. - Which AI agents does thoroughness-scoring work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is thoroughness-scoring safe to use?
- It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is thoroughness-scoring still maintained?
- The repository was last updated 5 days ago, so thoroughness-scoring is actively maintained.
Skill content
View source on GitHubname: thoroughness-scoring description: 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.
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:
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.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
