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.
npx skills add https://github.com/rohitg00/pro-workflow --skill 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.
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 |
When presenting choices, follow this format every time:
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
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.
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.
Before scoring, classify the scope:
Work with a clear boundary. You can be thorough because the surface area is finite.
These are T:9-10 opportunities. Take them.
Work without a clear boundary. Being thorough here means boiling the ocean.
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.
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.
Say "skipping thoroughness scoring — this is a spike/one-off" so the user knows it was a conscious choice.
## 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.
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.
Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
Take rohitg00/thoroughness-scoring from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.