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Goal Forge

Community
michaelpersonal
goal-forge

Turn a rough coding or repo idea into a spec, executable plan, and long-running Codex /goal contract. Use when the user wants to create a coding spec, spec out a repo feature, tighten a coding spec, turn an implementation idea into a /goal-ready plan, prepare for autonomous coding, forge a goal, start a /goal, generate GOAL.md, prepare measurable done_when criteria, or check Codex config readiness.

Overview

Publishermichaelpersonal
Repositorygoal-forge
Skill namegoal-forge
Stars
213
Forks
14
Bundled files
9
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.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by michaelpersonal on GitHub. Read the source before you install it.

Installation

Install the Goal Forge 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/michaelpersonal/goal-forge/tree/main
  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/michaelpersonal/goal-forge.git \
  .claude/skills/goal-forge
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Goal Forge 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 Forge 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 Forge 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.

Goal Forge

Goal Forge converts a rough coding idea into an executable Codex /goal contract.

Default pipeline:

  1. Interview rough intent into SPEC.md.
  2. Tighten SPEC.md until scope, scoring, feedback, and verification are concrete.
  3. Compile SPEC.md into GOAL.md.
  4. Check Codex config readiness before running /goal.

Use this skill for coding and repo work first. If the task is mostly exploratory, research-only, security-critical, or lacks verifiable completion criteria, keep the user in the loop instead of compiling a /goal.

Long-running goals need a runtime harness, not just a task description. Every /goal contract should make four things explicit:

  • Scorecard: how Codex repeatedly scores progress toward the goal.
  • Feedback loop: the fastest representative check Codex can run while iterating.
  • Working memory: markdown files Codex maintains so multi-hour runs do not rely only on conversation context.
  • Human control surface: small, task-specific knobs and sidecar inputs the user can inspect or adjust while the goal runs.

Modes

Choose the smallest mode that satisfies the request.

  • Interview: User gives a rough idea, asks to create a spec, or the spec lacks user-approved success criteria.
  • Tighten: User has a SPEC.md and wants it challenged, clarified, or made executable.
  • Compile: User has a good enough SPEC.md and wants a /goal prompt or GOAL.md.
  • Check config: User asks whether their Codex setup can run long goals.

Interview Mode

When the user gives a rough idea or asks to create a spec:

  1. Read the existing SPEC.md if present. Otherwise treat the rough idea as the interview seed; do not draft SPEC.md until step 6.
  2. Interview the user in detail before finalizing the spec.
  3. Ask about anything relevant: technical implementation, UI/UX, architecture, data model, edge cases, tradeoffs, rollout, scoring, feedback-loop speed, verification, risks, and non-goals.
  4. Do not ask obvious checklist questions. Ask questions that force decisions or expose hidden ambiguity.
  5. Continue interviewing until the spec is complete enough to compile into an executable /goal.
  6. Then write or update SPEC.md.

Use structured user-question tooling when available. If it is not available, ask concise batches of questions in chat. Keep each batch focused on unresolved blockers.

Hard gate: do not compile a /goal prompt until done_when contains concrete, user-approved success criteria. After the interview has covered scope, architecture, scoring, and verification, Codex may propose candidate done_when criteria as a starting point. The user must confirm or edit product and acceptance criteria before the spec is considered complete.

For long-running or exploratory goals, also identify:

  • the primary score or checklist Codex should optimize
  • the threshold or stop condition that means the goal is complete
  • the fastest useful check Codex can run repeatedly while working
  • the slower final check required before completion
  • whether Codex should create or maintain PLAN.md, ATTEMPTS.md, and NOTES.md
  • what the user may need to observe or tune while the goal runs, such as phase/status visibility, scope limits, resource budgets, review gates, sidecar inputs, or pivot approvals

Tighten Mode

Read SPEC.md skeptically before any execution prompt is created.

For each ambiguity:

  • explain why it matters
  • give two distinct interpretations
  • recommend the interpretation that produces the more maintainable result
  • ask the user when the choice defines product intent
  • update the spec only after the choice is clear

Do not add scope. Remove or mark anything that cannot be verified. Preserve clear existing decisions.

The tightened spec should include:

  • goal and non-goals
  • user-visible behavior
  • architecture and implementation constraints
  • files, modules, or systems likely involved
  • explicit edge cases
  • risk boundaries
  • scorecard with metric, threshold, regression checks, and stop condition
  • fast feedback loop with expected runtime and proxy validity
  • working-memory files for long-running goals
  • a minimal human control surface when the user may need visibility or steering during the run
  • verification commands or manual checks
  • user-approved done_when criteria

Compile Mode

Compile SPEC.md into GOAL.md using the block structure in references/goal_prompt_blocks.md. Load references/standard_execution_rules.md for default <execution_rules> content. If scaffolding long-run tracking files, load references/working_memory_templates.md. If the goal benefits from human steering during a long run, load references/control_surface_templates.md.

Before writing GOAL.md, reject weak specs that lack:

  • measurable done_when
  • a scorecard for how Codex evaluates progress during the run
  • scope boundaries or non-goals
  • a fast feedback loop, or an explicit reason one is impossible
  • concrete verification commands or checks
  • enough context for an agent to start reading the repo

If the spec is weak, route the user back to Interview or Tighten mode with a specific list of missing decisions. Do not write GOAL.md until the gaps are resolved.

Concrete gate: each done_when item must name a command, file artifact, or user-observable behavior. Otherwise mark it as weak and ask for clarification.

Scorecard gate: each scorecard must name the primary metric or checklist, the passing threshold, regression checks, the scoring command or inspection path, and the stop condition. If the score is model-judged, require a checklist or rubric that makes the judgment auditable.

Feedback-loop gate: each feedback_loop must name a fast check, expected runtime, run cadence, why the proxy is representative enough, and the slower escalation or final check. If the only available check is slow, say so explicitly and adjust the workflow cadence.

Working-memory gate: if the goal may run for hours, involves repeated experiments, or has high context churn, include a working_memory block. Prefer PLAN.md, ATTEMPTS.md, and NOTES.md unless the repo already has equivalent files. If no working-memory files are needed, state why the goal is short and linear enough to skip them.

Human-control gate: if a goal may run while the user or another Codex session reviews in parallel, may consume scarce resources, may require strategic pivots, or may benefit from human priority changes, include a human_control_surface block and scaffold a compact CONTROL.md. Keep CONTROL.md small: only include knobs relevant to this goal. Do not create a second GOAL.md full of static instructions.

Map the spec into the goal blocks:

  • spec summary -> <goal>
  • relevant files/modules/discovery commands -> <context>
  • architecture rules, non-goals, anti-patterns -> <constraints>
  • scoring metric, threshold, regression checks, stop condition -> <scorecard>
  • acceptance criteria -> <done_when>
  • fast iterative check and slower escalation check -> <feedback_loop>
  • implementation phases -> <workflow>
  • long-run tracking files and update cadence -> <working_memory>
  • user-visible status, tunable knobs, sidecar inputs, and pivot/resource gates -> <human_control_surface>
  • test/build/manual checks -> <verification_loop>
  • references/standard_execution_rules.md plus repo-specific rules -> <execution_rules>
  • final artifacts and completion signal -> <output_contract>

After writing GOAL.md, self-check it:

  • done_when is measurable and user-approved
  • scorecard tells Codex how to score progress and stop
  • feedback_loop is fast enough to run repeatedly, or its limits are explicit
  • working_memory is present for long-running or exploratory work
  • human_control_surface is present when the user needs visibility or tuning knobs during a long run, and absent when it would be ceremony
  • context names files or discovery commands
  • constraints prevent obvious shortcuts
  • verification is executable or explicitly manual
  • the task is execution-oriented enough for /goal

Config Check

Use scripts/inspect_codex_config.py for a read-only report:

bash
python3 ~/.codex/skills/goal-forge/scripts/inspect_codex_config.py --project-path /path/to/project

Run it from the target project root, or pass --project-path. The script reports the installed Codex version, selected config keys, trusted-project status, and gaps against the autonomous /goal configuration.

Before telling the user a long-running /goal is ready, verify the config against references/config_checklist.md. The important autonomous settings are:

  • model = "gpt-5.5"
  • model_context_window = 1050000
  • model_auto_compact_token_limit = 997500
  • model_reasoning_effort = "high"
  • plan_mode_reasoning_effort = "xhigh"
  • approval_policy = "never"
  • sandbox_mode = "danger-full-access"
  • [features] goals = true

Do not edit ~/.codex/config.toml unless the user explicitly asks for config changes. If asked to change config, prefer applying the dangerous approval_policy = "never" and sandbox_mode = "danger-full-access" settings only for explicitly trusted project paths rather than globally.

Read references/config_checklist.md when explaining config tradeoffs.

Output

Depending on the request, produce one or more of:

  • updated SPEC.md
  • generated GOAL.md
  • optional PLAN.md, ATTEMPTS.md, and NOTES.md scaffolds for long-running goals
  • optional CONTROL.md scaffold for long-running goals that need human visibility or tuning knobs
  • config readiness report
  • the exact /goal prompt body to paste into Codex

When the user asks to run the goal, verify that their Codex version supports /goal first.

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 Goal Forge AI skill do?

Turn a rough coding or repo idea into a spec, executable plan, and long-running Codex /goal contract. Use when the user wants to create a coding spec, spec out a repo feature, tighten a coding spec, turn an implementation idea into a /goal-ready plan, prepare for autonomous coding, forge a goal, start a /goal, generate GOAL.md, prepare measurable done_when criteria, or check Codex config readiness.

Why use Goal Forge on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/michaelpersonal/goal-forge/tree/main. 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 Goal Forge?

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 Forge?

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

Is the Goal Forge AI skill free?

Yes. It is published on GitHub by michaelpersonal 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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