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Llm Friendly Context

Community
shinpr
llm-friendly-context

Clarifies inputs, outputs, success criteria, decisions, and unresolved conditions so downstream consumers can execute without guessing. Use when writing or revising LLM-facing prompts, handoffs, planning artifacts, reviews, reports, or generated instructions.

Overview

Publishershinpr
Repositoryclaude-code-workflows
Skill namellm-friendly-context
Stars
682
Forks
103
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 shinpr on GitHub. Read the source before you install it.

Installation

Install the Llm Friendly Context 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/shinpr/claude-code-workflows.git /tmp/claude-code-workflows
mkdir -p .claude/skills
cp -r /tmp/claude-code-workflows/dev-skills/skills/llm-friendly-context .claude/skills/llm-friendly-context
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Llm Friendly Context 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 Llm Friendly Context 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 Llm Friendly Context 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.

LLM-Friendly Context

The goal is stable downstream execution: the next consumer should know what to read, what to do, what counts as success, and which unresolved decisions can change the result.

Core Rules

  1. Use positive, executable instructions

    • State what the next consumer should do.
    • Convert quality policies into positive criteria.
    • Keep a prohibition only when it protects an irreversible boundary or shipped contract. Name the protected condition and the allowed action.
    • Example: "Preserve existing public API behavior across the documented compatibility cases."
  2. Make vague instructions concrete

    • Replace subjective terms with observable conditions, paths, commands, schemas, examples, or decision rules.
    • Terms that often need clarification when they leave a decision to the next consumer: appropriate, proper, related, existing behavior, optional, as needed, if needed, per convention, unresolved alternatives, TBD, placeholder.
  3. Specify output shape

    • Use the sections, fields, table columns, JSON keys, or checklist items the consumer uses.
    • For handoffs, include only produced artifact paths and status fields that control the next transition.
  4. Provide the smallest sufficient context

    • Sufficient means sufficient for the assigned action, not complete background: every item you include is consumed by that action or by the result it must produce.
    • Include the purpose, source artifacts, hard constraints, accepted decisions, and unresolved conditions that action consumes.
    • Prefer concrete file paths and section hints over broad module names.
    • Follow references while they can change an in-scope decision, action, or verification result.
  5. Decompose complex work into verifiable steps

    • Split work with 3+ objectives or sequential dependencies into ordered steps.
    • Each step needs a checkpoint: what evidence proves it is complete.
  6. Permit uncertainty explicitly

    • Resolve missing operational detail from referenced artifacts and repository evidence before treating it as unresolved.
    • Record remaining uncertainty with its effect, required input, and decision owner. Make reversible repository-local choices when governing evidence resolves them.
  7. Keep constraints proportionate

    • Add only constraints that reduce ambiguity or preserve a real requirement.
    • Keep simple downstream tasks lightweight when the target action, context, and success criteria are already clear.
    • Apply minimal, a few lines, and explicit line estimates to the completed diff as one total budget.

Rewrite Patterns

Use these rewrites before treating a prompt, handoff, or artifact as complete.

Ambiguous formRewrite as
optional used as an unresolved choiceRequired, omitted, or required only under a named condition
Multiple alternatives that the next consumer must choose betweenThe selected option, or a deterministic decision rule
as needed / if neededThe triggering condition and required action
per conventionThe file, function, test, or documented convention to follow
related filesSpecific paths, globs, or search hints
existing behaviorThe observable behavior, source file, test, API response, or UI state to preserve
placeholderExact temporary value/behavior, allowed dependencies, and verification expectation
TBD used as a placeholder for required informationA blocking unresolved item with owner, required input, and decision effect
appropriate / properA measurable criterion or checklist

Handoff Checklist

Before sending a prompt or artifact to another consumer, verify:

  • The target action is explicit.
  • Required input paths, source artifacts, and decision-relevant facts are named.
  • Every included context item is consumed by the target action or its required result.
  • Accepted decisions and constraints use one canonical wording.
  • Output format or expected status fields are specified.
  • Success criteria are observable.
  • Ambiguous expressions have been rewritten or marked as unresolved.
  • Each instruction states the allowed action; each retained prohibition names the protected condition and allowed alternative.
  • The next consumer can complete its scope from the supplied purpose, sources, criteria, and evidence, or return the exact unresolved decision and owner.

Generated Artifact Checklist

Before writing or finalizing a generated document:

  • Each requirement, claim, task, test skeleton, or review finding has enough source context to trace why it exists.
  • Every executable instruction names the target, action, and expected result.
  • Verification steps say what to run or observe and what result proves success.
  • Each instruction states the allowed action; each retained prohibition names the protected condition and allowed alternative.
  • If an artifact is derived from another artifact, copied decisions stay consistent in wording and meaning.
  • If downstream work is blocked by missing information, the artifact records the missing input, decision owner, and effect.

Frequently asked questions

What does the Llm Friendly Context AI skill do?

Clarifies inputs, outputs, success criteria, decisions, and unresolved conditions so downstream consumers can execute without guessing. Use when writing or revising LLM-facing prompts, handoffs, planning artifacts, reviews, reports, or generated instructions.

Why use Llm Friendly Context on TypingMind?

Because you install it once and use it with any model. Llm Friendly Context 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 Llm Friendly Context in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shinpr/claude-code-workflows/tree/main/dev-skills/skills/llm-friendly-context. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Llm Friendly Context?

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 Llm Friendly Context?

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

Is the Llm Friendly Context AI skill free?

Yes. It is published on GitHub by shinpr 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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