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Context And Decisions

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affaan-m
context-and-decisions

Govern stable domain context with a narrowly scoped CONTEXT.md when needed, and route hard-to-reverse decisions to ECC's existing architecture-decision-records skill. Use when domain terms require repeated explanation, one term has multiple meanings, unclear language causes module-boundary mistakes, or domain semantics must remain separate from architecture decisions.

Overview

Publisheraffaan-m
RepositoryECC
Skill namecontext-and-decisions
Stars
277.3K
Forks
41.4K
Bundled files
2
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.

  • 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 affaan-m on GitHub. Read the source before you install it.

Installation

Install the Context And Decisions 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/affaan-m/ECC/tree/main/.agents/skills/context-and-decisions
  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/affaan-m/ECC.git /tmp/ECC
mkdir -p .claude/skills
cp -r /tmp/ECC/.agents/skills/context-and-decisions .claude/skills/context-and-decisions
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Context And Decisions 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 Context And Decisions 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 Context And Decisions 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.

Domain Context and Decision Handoff

Govern “what these business terms mean” separately from “why this option was chosen.” Do not create an empty shell when no stable content exists.

Domain Context Artifact: Domain Language Only

When terms require repeated explanation, one term has multiple meanings, or unclear language causes module-boundary errors, first reuse the repository's existing glossary/domain-model document or the context role in .governance/docs-map.json. Only when no equivalent exists should you lazily create the default CONTEXT.md from templates/context.example.md.

Record only:

  • domain terms and precise definitions;
  • relationships among core concepts;
  • confirmed ambiguities and the chosen interpretation;
  • ambiguities still awaiting confirmation from a domain owner.

Do not include implementation details, current state, task schedules, full requirements, decision rationale, or history. Keep those in code/MAP, STATUS/Issue Tracker, Spec, ADR, and LOG respectively. Terms must be traceable to code, contracts, or business evidence. Mark unconfirmed items as needs confirmation; do not guess.

ADR Handoff: Reuse the Existing Capability

This skill does not define a second ADR directory, numbering scheme, lifecycle, or template. When a decision is hard to reverse, would confuse a future reader without context, and involves a real tradeoff:

  1. Read and execute architecture-decision-records, preserving the repository's existing ADR location and format.
  2. If no convention exists, let that skill choose the minimum viable location; do not create a separate system here.
  3. Link to the ADR from CONTEXT.md, the project map, or the relevant Spec/Issue instead of copying the decision body.
  4. When a decision is accepted, superseded, or deprecated, append an event to project history according to the living-document rules.

Routine implementation details, easily reversible changes, and short-lived experiments do not need ADRs.

When to Activate

  • Domain terms are repeatedly re-explained or ambiguous language causes implementation drift.
  • The project needs a stable domain-language entry point but has no equivalent document.
  • Context work uncovers a durable decision that should be handed to architecture-decision-records.

When to Use

Use this skill when stable domain language needs one evidence-backed home, or when context work reveals a hard-to-reverse decision that belongs in the repository's existing ADR system.

How It Works

  1. Reuse the mapped context or glossary artifact when one exists; create CONTEXT.md only when no equivalent source of truth is available.
  2. Record precise domain definitions, relationships, confirmed interpretations, and explicitly unconfirmed ambiguities without mixing in implementation or task state.
  3. Route durable tradeoffs to architecture-decision-records and link the resulting ADR instead of copying its rationale into context documents.
  4. Keep schedules, blockers, and execution status in the existing issue tracker.

Examples

  • Define an overloaded business term once and link it to the code or contract that proves its meaning.
  • Mark a domain interpretation as needs confirmation until a domain owner resolves it.
  • Hand off a durable storage or boundary decision to the existing ADR workflow and link it from the context artifact.

Anti-Patterns

  • Copying current tasks, schedules, full requirements, or implementation details into CONTEXT.md.
  • Creating empty context files or a second ADR system for completeness.
  • Treating unconfirmed domain interpretations as facts, or copying ADR bodies into maps and logs.

Related Skills

  • architecture-decision-records: the sole ADR methodology for hard-to-reverse decisions.
  • living-docs-governance: lifecycle maintenance for the project map, status, and history.
  • docs-governance: cross-capability routing and closeout order.

Scheduling and Task Boundary

Keep tasks, status, blockers, and schedules in the project's existing tracker, such as GitHub Issues or Linear, as the single source of truth. Use local .scratch/ only when no external tracker exists and the project explicitly adopts it. Do not put schedules in CONTEXT.md, ADRs, PROJECT_STATUS, or the history index database.

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 Context And Decisions AI skill do?

Govern stable domain context with a narrowly scoped CONTEXT.md when needed, and route hard-to-reverse decisions to ECC's existing architecture-decision-records skill. Use when domain terms require repeated explanation, one term has multiple meanings, unclear language causes module-boundary mistakes, or domain semantics must remain separate from architecture decisions.

Why use Context And Decisions on TypingMind?

Because you install it once and use it with any model. Context And Decisions 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 Context And Decisions in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/affaan-m/ECC/tree/main/.agents/skills/context-and-decisions. 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 Context And Decisions?

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 Context And Decisions?

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

Is the Context And Decisions AI skill free?

Yes. It is published on GitHub by affaan-m 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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