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Estimation And Scoping

Community
adamentwistle
estimation-and-scoping

Honest sizing and scope — probe before promising, surface 10x discoveries immediately (never silently absorb or shrink), vertical slices, finish quality calibrated to the ask. Use when asked "how big is this" or "can you just quickly", when sizing or planning work, when scope balloons mid-task, when a "quick fix" turns into a migration or architectural change, and before reporting done on anything larger than one file.

Overview

Publisheradamentwistle
Repositoryfable-skills
Skill nameestimation-and-scoping
Stars
98
Forks
11
Bundled files
Instructions only
LicenseMIT
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 adamentwistle on GitHub. Read the source before you install it.

Installation

Install the Estimation And Scoping 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.
    https://github.com/adamentwistle/fable-skills/tree/main/estimation-and-scoping
  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/adamentwistle/fable-skills.git /tmp/fable-skills
mkdir -p .claude/skills
cp -r /tmp/fable-skills/estimation-and-scoping .claude/skills/estimation-and-scoping
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Estimation And Scoping 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 Estimation And Scoping 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 Estimation And Scoping 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.

Estimation and Scoping

Scope failures are trust failures: promising small and delivering huge, silently shrinking the task to fit, or gold-plating past what was asked. Weak scoping estimates from the request text. Strong scoping measures the surface first and renegotiates out loud the moment the measurement changes.

Size by probing, not by vibes

Before sizing anything non-trivial, measure the actual surface:

  • Count the touch points: grep the symbols and patterns involved. 3 call sites and 40 call sites are different projects.
  • Find the precedent: git log the last similar change and read its diff stat.
  • Probe the riskiest assumption first: the API you hope exists, the layer you hope is decoupled. Ten minutes of probing regularly converts "small tweak" into "restructuring" BEFORE you have promised anything.

Report size in units the user can check, never in hours: files, call sites, steps, and unknowns. Separate certain from contingent: "mechanical across 12 files; contingent on whether X handles Y, checking that first."

The 10x-discovery rule

Triggers: touch points exceed twice what you probed; a migration, second layer, or load-bearing hack appears where you expected a clean seam; a step you called mechanical needs judgment at every site. On any trigger, STOP editing and report: what you found, the honest new shape, the state of the tree right now (green? partially changed?), 2–3 options with costs (full fix / narrow workaround / defer), and your recommendation. Then let the user choose.

Not allowed: silently absorbing the blowup (user waits 5x longer than told) or silently shrinking the deliverable to fit the estimate (user gets less than agreed and finds out later). Both are worse than the awkward message.

Slice for shippable increments

When work is big, cut it so each slice is independently verifiable and leaves the system working:

  • Vertical slices (one thin end-to-end path) over horizontal layers (all models, then all handlers). Vertical proves the architecture early, where the risk lives.
  • Sequence: riskiest-assumption slice first, then value order.
  • Each slice ends at a real checkpoint: gates green, behavior demonstrable. "80% done" on an unshippable heap is 0% shipped.

Calibrate finish quality to the request

Match effort to intent, and when they diverge, say so rather than silently choosing:

  • "Quick check / prototype / does this work?" → answer the question; no neighborhood refactor, no test suite.
  • "Fix the bug" → fix + regression test + gates. Not a module rewrite; list rewrite-worthy findings in the report.
  • "Production-ready" → error paths, edge cases, tests, docs.

Gold-plating (unrequested abstractions, config for imagined futures, extra features) is scope creep in a flattering costume. Build what was asked; list what you would consider next.

The definition-of-done handshake

For any multi-step task, restate scope as a checklist before diving deep: in-scope items, explicitly-out items ("not touching the mobile layout"), and the verification bar. If the task came with no spec, your checklist IS the spec; put it where the user will see it. At the end, report against that same checklist, with a "Not done" line for anything cut, deferred, or discovered, so the user never learns about a gap from production.

Frequently asked questions

What does the Estimation And Scoping AI skill do?

Honest sizing and scope — probe before promising, surface 10x discoveries immediately (never silently absorb or shrink), vertical slices, finish quality calibrated to the ask. Use when asked "how big is this" or "can you just quickly", when sizing or planning work, when scope balloons mid-task, when a "quick fix" turns into a migration or architectural change, and before reporting done on anything larger than one file.

Why use Estimation And Scoping on TypingMind?

Because you install it once and use it with any model. Estimation And Scoping 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 Estimation And Scoping in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/adamentwistle/fable-skills/tree/main/estimation-and-scoping. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Estimation And Scoping?

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 Estimation And Scoping?

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

Is the Estimation And Scoping AI skill free?

Yes. It is published on GitHub by adamentwistle under the MIT license. 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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