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Thoroughness Scoring

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rohitg00
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.

Overview

Publisherrohitg00
Repositorypro-workflow
Skill namethoroughness-scoring
Stars
2.9K
Forks
286
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Thoroughness Scoring AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rohitg00/pro-workflow.git /tmp/pro-workflow
mkdir -p .claude/skills
cp -r /tmp/pro-workflow/skills/thoroughness-scoring .claude/skills/thoroughness-scoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thoroughness Scoring in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Thoroughness Scoring on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Thoroughness Scoring is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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):

ScoreWhat It Means
T:10All edge cases handled, full test coverage, docs updated, error messages helpful
T:9Edge cases covered, tests pass, types solid, no shortcuts
T:8Happy path + error paths, good tests, clean types
T:7Happy path works, basic tests, no docs
T:5Works for the demo, fragile, manual testing only
T:3Quick hack, no tests, tech debt accruing
T:1Copy-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.

Frequently asked questions

What does the Thoroughness Scoring AI skill do?

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.

Why use Thoroughness Scoring on TypingMind?

Because you install it once and use it with any model. Thoroughness Scoring is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Thoroughness Scoring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/pro-workflow/tree/main/skills/thoroughness-scoring. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Thoroughness Scoring?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Thoroughness Scoring?

As many as you like. As long as a model supports skills, you can use Thoroughness Scoring with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Thoroughness Scoring AI skill free?

It is published on GitHub by rohitg00. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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