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Goal Framing

CommunityPopular
GanyuanRan
goal-framing

Use when the user explicitly sets an Aegis goal with /aegis-goal, Aegis goal:, or asks to define goal, success evidence, stop condition, or task boundaries before work.

Overview

PublisherGanyuanRan
RepositoryAegis
Skill namegoal-framing
Stars
1.2K
Forks
52
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 GanyuanRan on GitHub. Read the source before you install it.

Installation

Install the Goal Framing 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/GanyuanRan/Aegis.git /tmp/Aegis
mkdir -p .claude/skills
cp -r /tmp/Aegis/skills/goal-framing .claude/skills/goal-framing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Goal Framing 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 Goal Framing 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 Goal Framing 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.

Aegis Goal Framing

Use this skill to create a thin goal frame before execution. It is opt-in and boundary-setting only.

Do not use it for tiny edits, one-command checks, or ordinary fast-path Q&A unless the user explicitly asks for /aegis-goal or Aegis goal:.

Authority Boundary

Current owner:

  • Method Pack task framing

Not owned here:

  • authoritative GateDecision
  • final evidence sufficiency
  • final completion authority is not owned here
  • host daemon / automatic stop enforcement

Input Forms

Treat these as equivalent:

  • /aegis-goal <task description>
  • Aegis goal: <task description>
  • "Define the goal / stop condition before we start"

Slash commands are optional host shortcuts. The natural-language form is the portable fallback.

Example:

text
Aegis goal: Fix the auth refresh bug without rewriting the auth system.

Output

Produce the smallest useful frame, then continue into the routed workflow in the same turn.

text
TaskIntentDraft:
- Requested outcome:
- Goal:
- Success evidence:
- Stop condition:
- Non-goals:
- Constraints:
- Scope:
- Risk hints:
- Aegis Visibility:
- Route:
- Next:

Default behavior:

  • Do not stop after TaskIntentDraft.
  • Treat the frame as the start protocol for execution, not as the final answer.
  • After the compact frame, immediately take the Next action for the selected route when the user asked to do the work.
  • Keep the visible frame natural and short when the goal is clear; do not emit a large internal-looking card unless the user asked for a formal frame.
  • Use Aegis Visibility to say why the goal frame constrains the route, stop condition, or non-goals. Do not add trace ceremony unless the user explicitly asks for auditability.

Frame-only behavior:

  • Stop at the frame only when the user explicitly asks to only define the goal, only define the stop condition, not execute, not implement, not write a plan, or wait for confirmation before continuing.
  • If required information is missing, state the missing input and stop with blocked rather than pretending to continue.

Stop condition must distinguish:

State set: done, blocked, needs-verification, scope-exceeded.

  • done: success evidence is satisfied
  • blocked: required dependency, permission, or information is missing
  • needs-verification: implementation exists but evidence is insufficient
  • scope-exceeded: continuing would exceed the goal or non-goals

Routing

After framing:

  • Low-risk single-owner work continues through the normal fast path or TDD
  • Ambiguous product / architecture / contract work routes to brainstorming
  • Approved requirements route to writing-plans
  • Multi-step, compaction-prone, handoff, or subagent work routes to long-task-continuation
  • Bug diagnosis routes to systematic-debugging

Route Matrix

Goal signalRoute
single-owner, low-risk, clear verificationfast path or test-driven-development
bug, failure, regression, unexpected behaviorsystematic-debugging
ambiguous product, architecture, contract, cross-module behaviorbrainstorming
approved spec, stable requirements, implementation slicingwriting-plans
multi-step, compaction-prone, handoff, subagent worklong-task-continuation
completion, release, handoff, "is this done?"verification-before-completion

Only create docs/aegis/ records when the routed workflow needs persistent evidence. Goal framing alone does not create project files.

Subagent Context Packet

When delegating work, pass a compact packet instead of the full conversation:

text
SubagentContextPacket:
- Task:
- Goal:
- Stop condition:
- Relevant baseline refs:
- Relevant files:
- Known facts:
- Unknowns:
- Non-goals:
- Expected output:
- Verification expected:
- Must-read excerpts:
- Unsafe assumptions:

The packet reduces repeated file reading, but it does not replace evidence. Subagents should still read the smallest raw file/log/test excerpt needed to verify critical facts.

Do not paste full chat transcripts, full session history, or unbounded logs into the packet. If a fact matters, include a file ref, line/window hint, or compact must-read excerpt.

Drift Rule

If the goal changes mid-task, do not silently overwrite it. Record old goal, new goal, changed scope, new risks, and route through DriftCheckDraft when a long-task record exists.

Frequently asked questions

What does the Goal Framing AI skill do?

Use when the user explicitly sets an Aegis goal with /aegis-goal, Aegis goal:, or asks to define goal, success evidence, stop condition, or task boundaries before work.

Why use Goal Framing on TypingMind?

Because you install it once and use it with any model. Goal Framing 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 Goal Framing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GanyuanRan/Aegis/tree/main/skills/goal-framing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Goal Framing?

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 Goal Framing?

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

Is the Goal Framing AI skill free?

Yes. It is published on GitHub by GanyuanRan 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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