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Context Economy

Community
UnpaidAttention
context-economy

Protect the context window as a scarce resource — read at the right altitude, delegate bulk reconnaissance to subagents so only conclusions enter the main thread, externalize durable facts to disk, and respond to your own degradation signals. Trigger this when about to read a large file or many files, when a search/log-dig would flood the conversation with content you won't reference again, when deciding whether to delegate to a subagent, and whenever you notice re-running searches or re-deriving established facts. Do NOT confuse with session-state-management (which owns the WORKING_NOTES.md file and resume protocol — this skill decides what enters context at all) or incremental-delivery (which chunks the deliverable — this skill budgets the reading around it).

Overview

PublisherUnpaidAttention
Repositoryfable5-methodology
Skill namecontext-economy
Stars
94
Forks
21
Bundled files
Instructions only
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 UnpaidAttention on GitHub. Read the source before you install it.

Installation

Install the Context Economy 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/UnpaidAttention/fable5-methodology/tree/main/skills/context-economy
  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/UnpaidAttention/fable5-methodology.git /tmp/fable5-methodology
mkdir -p .claude/skills
cp -r /tmp/fable5-methodology/skills/context-economy .claude/skills/context-economy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Context Economy 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 Economy 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 Economy 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.

Context Economy

Everything you read enters the same window your reasoning runs in. Fill it with noise and the noise wins: instructions decay, early decisions blur, retrieval degrades — and output quality collapses without any error message announcing it. An advanced model husbands its context implicitly; you do it as explicit procedure.

Rule 1: Read at the right altitude

Match reading depth to need, before opening anything:

NeedAction
One fact ("does X call Y?", "what's the version?")grep / targeted search — never open the file
Call something correctlyRead the signature + one existing call site, not the whole module
Edit a fileRead it fully (non-negotiable — Prime Directive 2)
Understand a subsystem's shapeSymbol map / headings / one representative flow — not every file
Judge a large artifact (logs, datasets)Head/tail/sample + targeted filters, never the whole thing

The violation to catch: opening a 2,000-line file to answer a one-line question. Ask "what is the smallest read that answers this?" before every Read.

Rule 2: Delegate bulk work to protect the main thread

When a task requires sweeping many files, digging long logs, or broad reconnaissance whose raw content you will NOT reference again: hand it to a subagent. The subagent burns its own context on the haystack and returns only the needle. The main conversation stays at decision altitude.

Delegation test — delegate when ALL of: the raw material is large, only the conclusion matters downstream, and the sub-task is separable with a crisp question. Don't delegate when you'll need the raw content in-thread anyway, or the task is a two-minute look.

This is the primary reason to delegate, beyond parallelism: context preservation.

Rule 3: Don't re-read what hasn't changed

If you read a file this session and it's unchanged, trust your knowledge and your notes. Re-read only when: someone else (user, formatter, another agent) may have modified it, an edit of yours failed unexpectedly, or you've had a context loss. Redundant re-reads are pure context burn.

Rule 4: Externalize durable facts the moment they matter

Decisions, discovered constraints, findings, and todo state go to disk (WORKING_NOTES.md, a scratchpad file) when they're established — not "later", and not only when compaction looms. The context window is working memory; disk is long-term memory. Facts that live only in context die with it. (The file format and resume protocol belong to session-state-management; this rule is about WHEN to move facts out of context.)

Rule 5: React to your own degradation signals

The signals that your context is failing you: re-running a search you already ran; re-deriving a fact you established an hour ago; surprise at your own earlier decision; instructions from the original request feeling new on re-read. On any of them: STOP consuming new content, re-read the notes file, and only then continue. Pushing on from fog produces confident work built on a forgotten foundation.

Worked example

Task: "Find why checkout intermittently 500s — logs are in /var/log/app/ (14 files, ~2 GB)."

  • Weak: Read the newest log → floods context with 50k lines → asks for the next file → by file three, the original error pattern from file one has blurred; analysis restarts.
  • Context-economy:
    1. Rule 1: never open a log raw. grep -c 'HTTP 500' per file → the needle is in 3 files.
    2. Rule 2: delegate — subagent per file with a crisp question ("extract each 500 with the 20 lines before it; return timestamps + the exception class only"). Three subagents burn their own contexts; the main thread receives ~40 lines of conclusions.
    3. Rule 4: findings ("all 500s follow a connection-pool-exhausted warning within 2s; only during the 02:00 batch job") go into WORKING_NOTES.md immediately.
    4. Main thread proceeds to the fix with a nearly-empty window and complete knowledge.

Done when

Ongoing — an ambient discipline. You are following it when: nothing large was read where a grep or a sample would answer; bulk reconnaissance ran in subagent contexts, not the main thread; no unchanged file was re-read; every durable fact is on disk within minutes of being established; and no degradation signal was pushed through instead of triggering a notes re-read.

Frequently asked questions

What does the Context Economy AI skill do?

Protect the context window as a scarce resource — read at the right altitude, delegate bulk reconnaissance to subagents so only conclusions enter the main thread, externalize durable facts to disk, and respond to your own degradation signals. Trigger this when about to read a large file or many files, when a search/log-dig would flood the conversation with content you won't reference again, when deciding whether to delegate to a subagent, and whenever you notice re-running searches or re-deriving established facts. Do NOT confuse with session-state-management (which owns the WORKING_NOTES.m...

Why use Context Economy on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/UnpaidAttention/fable5-methodology/tree/main/skills/context-economy. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Context Economy?

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 Economy?

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

Is the Context Economy AI skill free?

It is published on GitHub by UnpaidAttention. 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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