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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-scoring

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

90/100

Supported Platforms

Universal

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.

Substance
26/30
Structure
20/20
Description
15/15
Adoption
15/20
Freshness
15/15

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.

SkillScoreStarsUpdatedFormat
thoroughness-scoring (this skill)by rohitg00902.9k5d agoSKILL.md
ai-job-searchby MadsLorentzen10044.4k1d agoCLAUDE.md
claude-howtoby luongnv8910041.7k3d agoCLAUDE.md
algorithmic-artby anthropics100177.9k7d agoSKILL.md
pptxby anthropics100177.9k7d agoSKILL.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.

name: 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.

Related Skills

View on GitHub
GitHub Stars2.9k
CategoryDevelopment
Updated5d ago
Forks289

Languages

JavaScript

Trust signals

88/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

1 medium