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Grillcraft

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grp06
grillcraft

Compile a completed Grill Me session, transcript, or decision discussion into a durable work item under `.agent/work/`: write `decision.md`, initialize `meta.json`, create an ExecPlan, run configurable `execplan-improve` passes, and invoke Goalcraft to activate a Codex `/goal` that executes the ExecPlan lifecycle. Use after grilling, when the user says "GrillCraft", "turn this grill into a goal", "create the ExecPlan and goal", "run Goalcraft after this grill", or asks for N ExecPlan improvements.

Overview

Publishergrp06
Repositoryuseful-codex-skills
Skill namegrillcraft
Stars
78
Forks
6
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by grp06 on GitHub. Read the source before you install it.

Installation

Install the Grillcraft 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/grp06/useful-codex-skills/tree/main/grillcraft
  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/grp06/useful-codex-skills.git /tmp/useful-codex-skills
mkdir -p .claude/skills
cp -r /tmp/useful-codex-skills/grillcraft .claude/skills/grillcraft
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Grillcraft 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 Grillcraft 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 Grillcraft 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.

GrillCraft

Core Contract

Turn resolved intent into a durable execution handoff. GrillCraft is a compiler from a Grill Me-style decision conversation into:

  • .agent/work/<slug>/decision.md
  • .agent/work/<slug>/meta.json
  • .agent/work/<slug>/execplan.md
  • an activated Goalcraft /goal that executes the work item until evidence says the ExecPlan and decisions are satisfied or honestly blocked.

GrillCraft owns setup and activation, not product implementation. After the Goalcraft goal is active, the goal is the persistent executor. The goal must absorb the implementation protocol that implement-execplan previously owned: set implementation state, execute milestones, keep the ExecPlan living sections current, validate after slices, revise the plan when evidence changes the path, and finish only after review evidence satisfies both decision.md and execplan.md.

Input Resolution

Resolve the source of intent in this order:

  1. explicit Grill Me transcript, decision ledger, or work-item path from the user
  2. the current conversation when it clearly contains a completed grill session
  3. explicit decision.md or decisions.md supplied by the user
  4. .agent/active when it points to a work item with a decision artifact

If the source is missing, obviously incomplete, or mostly a vague brief rather than resolved intent, stop and ask for the smallest missing artifact. Do not invent decisions to keep the pipeline moving.

Improvement Count

Default to 3 execplan-improve attempts.

If the user says N improvements, N improve passes, or equivalent, use that integer. 0 improvements means create the ExecPlan and skip improvement.

Treat the count as "up to N useful attempts." If execplan-improve returns exactly skip, or its own low-value short-circuit applies, stop improvement early and continue to Goalcraft with the current plan.

Workflow

Step 1: Create or reuse the work item

Prefer the work-item format:

text
.agent/work/<slug>/
  meta.json
  decision.md
  execplan.md

If updating an existing work item, read meta.json, decision.md, and execplan.md if present before writing. Preserve unrelated artifacts and do not rename directories to represent lifecycle state.

Use .agent/active only as a convenience symlink. The authoritative lifecycle state lives in meta.json.

Step 2: Write decision.md

Distill the grill into a provenance-aware decision artifact. Use decision.md singular, even if the user says decisions.md, because downstream ExecPlan skills already prefer that path.

decision.md should contain:

  • Objective
  • Confirmed user decisions
  • Agent-recommended defaults
  • Assumptions
  • Open questions or user judgments
  • Accepted risks and failure modes
  • Validation expectations
  • Source notes naming the transcript, files, or conversation basis

Do not dump the whole transcript. Keep decisions compact enough that a future agent can recover intent quickly, while preserving whether each item is confirmed, inferred, assumed, or still open.

Step 3: Initialize metadata

Write or update meta.json with:

  • stage="decision"
  • state="completed"
  • timestamps
  • artifacts.decision="decision.md"

If the work item came from a parent meta-plan, preserve meta_plan_id and meta_plan_slice_id.

Step 4: Create the ExecPlan

Use the existing $execplan-create protocol on the work item. The resulting plan must live at .agent/work/<slug>/execplan.md and follow .agent/PLANS.md.

The ExecPlan must be self-contained per PLANS.md, but it may cite decision.md as the provenance record. Preserve hard constraints from confirmed decisions, clearly label assumptions and open judgments, and make the validation surface observable.

Step 5: Improve the ExecPlan

Run $execplan-improve up to the requested number of attempts, defaulting to 3. Each pass must be code-grounded. Do not ask for cosmetic rewrites.

After improvement, keep the work item at:

  • stage="plan"
  • state="completed"
  • artifacts.execplan="execplan.md"

Step 6: Invoke Goalcraft

Use $goalcraft with a compact brief that references the work item and tells the goal to execute the ExecPlan lifecycle. Include this execution contract in the Goalcraft input:

text
Operate the work item at .agent/work/<slug>. Treat decision.md as the
intent/provenance source and execplan.md as the executable contract governed by
.agent/PLANS.md. Before coding, set meta.json to stage="implementation" and
state="active". Execute execplan.md milestone by milestone. Keep Progress,
Surprises & Discoveries, Decision Log, and Outcomes & Retrospective current.
After each meaningful slice, run the plan's validation or the nearest targeted
check. If evidence changes the path, revise execplan.md before continuing. Do
not mark complete because of elapsed time, budget exhaustion, partial
implementation, or proxy checks alone. Done only after implementation,
validation, and a fresh review-style pass satisfy both decision.md and
execplan.md. If blocked, record the blocker in execplan.md and meta.json,
including what user input or external change would unblock progress.

If the user requested draft-only, planning-only, no-goal mode, or no activation, prepare the Goalcraft-ready objective but do not activate it.

If another unfinished goal already exists, do not silently replace it. Let Goalcraft handle the conflict, and return the validated goal text as the fallback artifact if activation cannot proceed.

Anti-Patterns

  • implementing product code during GrillCraft setup
  • flattening agent recommendations into confirmed user decisions
  • dumping transcript history into decision.md
  • creating a meta-plan unless the user explicitly asks for parent/child slices
  • forcing all requested improvement passes after execplan-improve says skip
  • treating the Goal as the plan instead of a compact execution loop around the work item
  • marking the work item implementation-active before Goalcraft actually begins execution

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 Grillcraft AI skill do?

Compile a completed Grill Me session, transcript, or decision discussion into a durable work item under `.agent/work/`: write `decision.md`, initialize `meta.json`, create an ExecPlan, run configurable `execplan-improve` passes, and invoke Goalcraft to activate a Codex `/goal` that executes the ExecPlan lifecycle. Use after grilling, when the user says "GrillCraft", "turn this grill into a goal", "create the ExecPlan and goal", "run Goalcraft after this grill", or asks for N ExecPlan improvements.

Why use Grillcraft on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/grp06/useful-codex-skills/tree/main/grillcraft. 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 Grillcraft?

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 Grillcraft?

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

Is the Grillcraft AI skill free?

Yes. It is published on GitHub by grp06 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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