Hard Cut

Organization

instructa

hard-cut

Enforce a hard-cut cleanup policy: keep one canonical implementation and delete compatibility, migration, fallback, adapter, coercion, and dual-shape code. Use for pre-release or internal-draft refactors where the goal is one final shape, especially when changing schemas, contracts, persisted state, routing, configuration, feature flags, enum/value sets, or architecture.

Overview

Publisher	instructa
Repository	`agent-skills`
Skill name	`hard-cut`
Stars	141
Forks	16
Bundled files	1
Links	Skill source Repository

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by instructa on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/instructa/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/engineering/hard-cut .claude/skills/hard-cut

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hard Cut 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 Hard Cut 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 Hard Cut 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.

Hard-Cut Policy

Apply a hard-cut policy by default for refactors or behavior changes that alter schemas, contracts, persisted state, routing, configuration, feature flags, enum/value sets, or architecture where old-state preservation might otherwise be retained.

Keep one canonical codepath. Remove old-shape handling. Do not preserve draft or legacy behavior unless there is concrete evidence of a real external compatibility boundary.

Default assumption

Treat previous shapes as internal draft shapes unless there is concrete evidence they are already:

persisted external or user data
on-disk or database state that must still load
a wire format used across process or service boundaries
a documented or publicly supported contract
actively depended on outside the refactor boundary

Mere existence of old code is not proof of a compatibility obligation.

Core policy

When an old shape appears, remove that path and convert the codebase to the canonical shape. Do not add code to support it. Do not add code specifically to reject it just because it once existed.

Hard rules

Apply these rules in order:

Do not add fallback behavior.
Do not add compatibility branches.
Do not add shims, adapters, coercions, aliases, or dual-shape support.
Do not add fail-fast guards whose purpose is to detect or reject old shapes.
Do not add tests whose purpose is to assert rejection of old or legacy shapes.
Prefer deleting old-shape handling over preserving or policing it.
Update producers, consumers, fixtures, and tests to use only the canonical shape.
Remove dead code, dead conditionals, obsolete comments, and translation helpers related to old shapes.
Keep validation only for the current canonical contract. Validation may reject malformed current-shape input, but must not branch on legacy discriminators, old field names, aliases, old enum members, or draft formats.
When choosing between backward compatibility and simplification, choose simplification.

Execution workflow

Identify the canonical target shape.
Trace every producer and consumer of that shape.
Update all live codepaths to emit and consume only the canonical shape.
Update fixtures, test data, builders, and snapshots to the canonical shape.
Delete legacy handling, branching, comments, and helpers.
Keep only current-shape validation that is still required for correctness.
If a real external compatibility boundary exists, isolate it and call out the exact file, function, boundary, and reason it cannot be removed yet.

Review checklist

Reject changes that preserve old-shape behavior behind conditionals.
Reject translation layers between old and new shapes.
Reject validation branches added only to reject legacy inputs.
Reject tests added only to memorialize abandoned draft formats.
Remove dead helpers and comments that describe removed draft formats.
Keep one owner for the canonical contract.

Deliverables

Deliver only:

a minimal implementation that supports the canonical shape
updated tests for the canonical shape only
removal of obsolete legacy-shape tests
no new rejection tests for old shapes
no runtime logic dedicated to recognizing legacy formats

Exception rule

Make an exception only when removing the old shape would break already persisted external or user data, on-disk or database state, cross-boundary wire formats, or a real public contract.

If such a boundary exists:

do not invent new compatibility layers elsewhere
name the exact file and function
describe the concrete persisted or public dependency
limit any compatibility discussion to that boundary only

Bundled files

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

agents/openai.yaml1 KB

Frequently asked questions

What does the Hard Cut AI skill do?

Why use Hard Cut on TypingMind?

Because you install it once and use it with any model. Hard Cut 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 Hard Cut in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/instructa/agent-skills/tree/main/skills/engineering/hard-cut. 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 Hard Cut?

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 Hard Cut?

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

Is the Hard Cut AI skill free?

It is published on GitHub by instructa. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Hard-Cut Policy

Default assumption

Core policy

Hard rules

Execution workflow

Review checklist

Deliverables

Exception rule

Bundled files

Frequently asked questions

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