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Scope Creep Detector

CommunityPopular
Shubhamsaboo
scope-creep-detector

Analyzes git diffs against a stated intent to detect scope creep, unrelated files, broad pull requests, changes that grew beyond a fix, dependency additions, public API renames, config or CI edits, oversized hunks, and formatting-only files. Use when the user asks whether a change grew beyond the fix, a PR is too broad, or what unrelated stuff they touched, and wants keep, split, or justify guidance. Operates locally and offline.

Overview

PublisherShubhamsaboo
Repositoryawesome-llm-apps
Skill namescope-creep-detector
Stars
138.7K
Forks
20.4K
Bundled files
2
LicenseApache-2.0
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Scope Creep Detector 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/Shubhamsaboo/awesome-llm-apps.git /tmp/awesome-llm-apps
mkdir -p .claude/skills
cp -r /tmp/awesome-llm-apps/agent_skills/scope-creep-detector .claude/skills/scope-creep-detector
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scope Creep Detector 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 Scope Creep Detector 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 Scope Creep Detector 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.

Scope Creep Detector

A one-line fix should not require a reviewer to reverse-engineer fourteen files across three subsystems. This skill compares a git diff with its stated intent, surfaces scope signals, and turns them into keep, split, or justify decisions.

Everything runs locally. The script makes no network calls and does not change the working tree, index, commits, or branches.

When to use

  • Before opening a pull request whose diff may have grown beyond its intent
  • When a bug fix touches unexpected files or subsystems
  • When the user asks whether staged changes are too broad
  • When a diff includes dependency, public API, config, CI, or build changes
  • When the user wants a concrete split plan for a mixed change

When not to use

  • Formatting code, running a linter, or writing a commit message
  • Reviewing correctness, security, or test quality inside an agreed scope
  • Measuring historical project growth across many commits
  • Editing or reverting files without the user's approval

Establish the intent

Use the user's one-line intent when available. Keep it concrete, such as fix null dereference in parser or add retry limit to webhook delivery.

If no intent was given, the script falls back to the current branch name. If that name is generic, detached, or unrelated to the work, ask for one line of intent before treating relatedness as meaningful.

Run the classifier

Run from this skill directory and point --repo at the target repository.

Working tree diff:

bash
python3 scripts/scope_creep.py --repo /path/to/repo \
  --intent "fix null dereference in parser" --json

Staged diff:

bash
python3 scripts/scope_creep.py --repo /path/to/repo --staged \
  --intent "fix null dereference in parser" --json

Branch diff against a merge base:

bash
python3 scripts/scope_creep.py --repo /path/to/repo --base main \
  --intent "fix null dereference in parser" --json

Saved diff or stdin:

bash
python3 scripts/scope_creep.py --diff change.diff --intent "parser fix" --json
git diff --staged | python3 scripts/scope_creep.py --diff - \
  --intent "parser fix" --json

Use --hunk-threshold only when the repository has a documented reason to change the default churn threshold. Do not tune the threshold merely to make a warning disappear.

Interpret the JSON

Read references/scope-signals.md before making a recommendation. Treat the classifier as triage evidence, not proof of authorial intent.

  • in_scope: file paths with at least one intent/path keyword overlap
  • likely_creep: paths without overlap, with the reason and detected signals
  • new_deps: dependencies introduced in supported manifest formats
  • api_renames: nearby removed and added public function or class declarations
  • config_edits: CI, container, build, YAML, and TOML changes
  • stats: churn, subsystem counts, oversized hunks, and formatting-only files

Empty arrays are evidence too. Say that no signal was detected, not that the diff is guaranteed to be in scope.

Recommend keep, split, or justify

Give every item in likely_creep one disposition:

  1. Keep when the path is necessary for the stated intent and the connection is direct. Explain the connection in one sentence.
  2. Split when it can land independently, belongs to another subsystem, or introduces a dependency, API rename, config edit, or large hunk that is not required for the intent. Name the files or hunks for the follow-up change.
  3. Justify when a cross-cutting edit cannot be separated safely. State the invariant or build constraint that requires it and call out reviewer risk.

Prefer split when evidence is ambiguous. Never claim that a zero overlap score proves a file is unrelated. Path vocabulary is a cheap, deterministic proxy.

Write the scope report

Use this compact structure:

  1. Intent and diff source
  2. Files and subsystems touched, with total additions and deletions
  3. In-scope changes
  4. Likely creep with signal evidence
  5. Keep, split, or justify table
  6. Proposed follow-up grouping, if any

Name file paths and hunk headers. For an oversized mixed hunk, explain that the script cannot split it automatically and describe the smallest coherent edit. Ask before applying any split, revert, staging, or commit operation.

Files

  • scripts/scope_creep.py: deterministic unified-diff parser and classifier
  • references/scope-signals.md: signal definitions, thresholds, and limits

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Scope Creep Detector AI skill do?

Analyzes git diffs against a stated intent to detect scope creep, unrelated files, broad pull requests, changes that grew beyond a fix, dependency additions, public API renames, config or CI edits, oversized hunks, and formatting-only files. Use when the user asks whether a change grew beyond the fix, a PR is too broad, or what unrelated stuff they touched, and wants keep, split, or justify guidance. Operates locally and offline.

Why use Scope Creep Detector on TypingMind?

Because you install it once and use it with any model. Scope Creep Detector 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 Scope Creep Detector in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/agent_skills/scope-creep-detector. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Scope Creep Detector?

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 Scope Creep Detector?

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

Is the Scope Creep Detector AI skill free?

Yes. It is published on GitHub by Shubhamsaboo under the Apache-2.0 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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