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Create Control Manifest

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Donchitos
create-control-manifest

Flat must-do/never-do rules sheet per system and layer, extracted from Accepted ADRs. ADRs explain why; this is actionable.

Overview

PublisherDonchitos
RepositoryClaude-Code-Game-Studios
Skill namecreate-control-manifest
Stars
25.8K
Forks
3.7K
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 Donchitos on GitHub. Read the source before you install it.

Installation

Install the Create Control Manifest 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/Donchitos/Claude-Code-Game-Studios/tree/main/.claude/skills/create-control-manifest
  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/Donchitos/Claude-Code-Game-Studios.git /tmp/Claude-Code-Game-Studios
mkdir -p .claude/skills
cp -r /tmp/Claude-Code-Game-Studios/.claude/skills/create-control-manifest .claude/skills/create-control-manifest
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create Control Manifest 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 Create Control Manifest 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 Create Control Manifest 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.

!bash "${CLAUDE_SKILL_DIR}/../../hooks/yaml-helper.sh" resolve_config --keys review_mode,automation

Resolved above — use as-is; --review overrides review_mode for this run. No block → defaults in .claude/docs/config-resolution.md.

Create Control Manifest

The Control Manifest is a flat, actionable rules sheet for programmers. It answers "what do I do?" and "what must I never do?" — organized by architectural layer, extracted from all Accepted ADRs, technical preferences, and engine reference docs. Where ADRs explain why, the manifest tells you what.

Output: docs/architecture/control-manifest.md

When to run: After /architecture-review passes and ADRs are in Accepted status. Re-run whenever new ADRs are accepted or existing ADRs are revised.


Every AskUserQuestion call follows .claude/docs/automation-modes.md (collaborative asks always · guided major-only · autonomous logs and proceeds; automation_always_ask categories always prompt).

1. Load All Inputs

ADRs

Establish the denominator first. Glob docs/architecture/adr-*.md. Call the count N. If N is 0: "No ADRs found — run /architecture-decision before building a control manifest." Stop.

Resolve status without reading the ADRs — the filter must precede the read, not follow it:

Grep pattern="^## Status" glob="docs/architecture/adr-*.md" output_mode="content" -A 3

Interpret against N:

ResultMeaningAction
N matches, some read AcceptedNormal.The Accepted set is A; proceed with A. Name every ADR left out, with its status, in the Phase 4 preview — the user approves a manifest knowing what it does not cover.
N matches, none AcceptedGenuinely no Accepted ADRs."[N] ADRs found, none Accepted. A manifest built from Proposed ADRs would encode decisions that may still change." Ask whether to proceed with Proposed or stop. Do not silently emit an empty manifest.
0 matches, N > 0Malformed ADRs — not an empty Accepted set. ## Status is BLOCKING-if-missing."[N] ADRs found, none has a ## Status section — acceptance cannot be determined. Run /architecture-decision retrofit [file] on each." Stop. Do not treat all ADRs as Accepted; do not emit a manifest.
  • Note the ADR number and title for every rule sourced.

Project Config

  • Read naming.* and performance.* from project.yaml; for any key absent or empty, fall back to .claude/docs/technical-preferences.md
  • Read approved libraries/addons and forbidden patterns from .claude/docs/technical-preferences.md (not migrated to project.yaml)

Engine Reference

  • Read docs/engine-reference/[engine]/VERSION.md for engine + version
  • Read docs/engine-reference/[engine]/deprecated-apis.md — these become forbidden API entries
  • Read docs/engine-reference/[engine]/current-best-practices.md if it exists

Existing Artifacts

  • Glob docs/architecture/control-manifest.md (present → this run is a regeneration; Read it before the Phase 5 write, which overwrites it) and production/epics/*/EPIC.md (present → epics exist). Phase 6 picks its next step from both.

Report: "Loaded [N] Accepted ADRs, engine: [name + version]."


2. Extract Rules from Each ADR

Read only these four sections per Accepted ADR — not the whole file. Context, Consequences, Migration Plan and Validation Criteria explain why a decision was made; the manifest records what to do, so they are not needed here:

Grep pattern="^## (Decision|Alternatives Considered|Performance Implications|Engine Compatibility)" glob="docs/architecture/adr-*.md" output_mode="content" -A 30

Filter the results to the Accepted set A resolved above. If a section is absent for a given ADR, note it per ADR and continue; if a section is absent across all of A, report "No Accepted ADR contains a [section] section — the manifest's [category] rules will be empty. Verify this is intended." Escalate to a full read of one ADR only when its scanned sections cross-reference material outside them (e.g. a Decision that says "subject to the constraints in Context").

For each Accepted ADR, extract:

Required Patterns (from the ## Decision section)

  • Every "must", "should", "required to", "always" statement in the Decision body — including any ### Implementation Guidelines sub-heading when the ADR has one (newer ADRs emit it; older ones state mandates directly in ## Decision). Never scan for ### Implementation Guidelines alone — it is absent from many skill-authored ADRs, and scanning for it yields an empty Required Patterns section on a manifest that should have been full.
  • Every specific pattern or approach mandated

Forbidden Approaches (from "Alternatives Considered" sections)

  • Every alternative that was explicitly rejected — why it was rejected becomes the rule ("never use X because Y")
  • Any anti-patterns explicitly called out

Performance Guardrails (from "Performance Implications" section)

  • Budget constraints: "max N ms per frame for this system"
  • Memory limits: "this system must not exceed N MB"

Engine API Constraints (from "Engine Compatibility" section)

  • Post-cutoff APIs that require verification
  • Verified behaviours that differ from default LLM assumptions
  • API fields or methods that behave differently in the pinned engine version

Layer Classification

Classify each rule by the architectural layer of the system it governs:

  • Foundation: Scene management, event architecture, save/load, engine init
  • Core: Core gameplay loops, main player systems, physics/collision
  • Feature: Secondary systems, secondary mechanics, AI
  • Presentation: Rendering, audio, UI, VFX, shaders

If an ADR spans multiple layers, duplicate the rule into each relevant layer.


3. Add Global Rules

Combine rules that apply to all layers. Each carries its source into the manifest, as a layer rule does — the project.yaml key, technical-preferences.md, or the engine-reference file:

From project config (project.yaml, else technical-preferences.md):

  • Naming conventions — naming.*: classes, variables, signals/events, files, constants
  • Performance budgets — performance.*: target framerate, frame budget, draw call limits, memory ceiling

From deprecated-apis.md:

  • Deprecated APIs → Forbidden API entries, filtered for relevance to this project. Do not copy the deprecation table wholesale.

    Each entry must apply to what this project actually builds. deprecated-apis.md mixes dimensions and subsystems: its GodotPhysics3D → Jolt Physics 3D row is 3D-only, so emitting it unqualified gives a 2D project a global rule about a physics engine it never uses. If you cannot tell whether an entry applies — 2D vs 3D, a module the project does not include — either scope the rule ("when using 3D physics: ...") or omit it and note it as unresolved. A manifest of rules that do not apply is one nobody reads, and /create-stories consumes this file.

From current-best-practices.md (if available):

  • Engine-recommended patterns → Required entries

From technical-preferences.md forbidden patterns:

  • Copy any "Forbidden Patterns" entries directly

4. Present Rules Summary Before Writing

Before writing the manifest, present a summary to the user:

## Control Manifest Preview
Engine: [name + version]
ADRs covered: [list ADR numbers]
ADRs excluded: [ADR-NNNN ([status]), … — every ADR not in the Accepted set, or "None"]
Total rules extracted:
  - Foundation layer: [N] required, [M] forbidden, [P] guardrails
  - Core layer: [N] required, [M] forbidden, [P] guardrails
  - Feature layer: ...
  - Presentation layer: ...
  - Global: [N] naming conventions, [M] forbidden APIs, [P] approved libraries

Use AskUserQuestion:

  • Prompt: "Does this rule summary look complete?"
  • Options:
    • [A] Yes — looks good, run the director review and write the manifest
    • [B] Add rules — I have additional rules to include before writing
    • [C] Remove rules — some extracted rules should be dropped
    • [D] Stop here — I need to review the ADRs first

4b. Director Gate — Technical Review

Review mode check — apply before spawning TD-MANIFEST:

  • solo → skip. Note: "TD-MANIFEST skipped — Solo mode." Proceed to Phase 5.
  • lean → skip. Note: "TD-MANIFEST skipped — Lean mode." Proceed to Phase 5.
  • full → spawn as normal.

Spawn technical-director via Agent using gate TD-MANIFEST (.claude/docs/director-gates/td-manifest.md).

Pass: the Control Manifest Preview from Phase 4 (rule counts per layer, full extracted rule list), the list of ADRs covered, engine version, and any rules sourced from technical-preferences.md or engine reference docs.

The technical-director reviews whether:

  • All mandatory ADR patterns are captured and accurately stated
  • Forbidden approaches are complete and correctly attributed
  • No rules were added that lack a source ADR or preference document
  • Performance guardrails are consistent with the ADR constraints

Apply the verdict:

  • APPROVE → proceed to Phase 5
  • CONCERNS → surface via AskUserQuestion with options: Revise flagged rules / Accept and proceed / Discuss further
  • REJECT → do not write the manifest; fix the flagged rules and re-present the summary
  • NOT ASSESSED [missing input] → not an approval (.claude/docs/director-gates.md): name what was missing, then supply it and re-run the gate — or, if the user chooses to go on without it, continue to Phase 5 and state TD-MANIFEST: NOT ASSESSED — [input] in the output and the final Verdict line

5. Write the Control Manifest

Use AskUserQuestion:

  • Prompt: "May I write the Control Manifest?"
  • Options:
    • [A] Yes — write to docs/architecture/control-manifest.md
    • [B] Show me the full draft first, then ask again
    • [C] Not yet — I want to make more changes

Format:

markdown
# Control Manifest

> **Engine**: [name + version]
> **Last Updated**: [date]
> **Manifest Version**: [date]
> **ADRs Covered**: [ADR-NNNN, ADR-MMMM, ...]
> **Status**: [Active — regenerate with `/create-control-manifest update` when ADRs change]

`Manifest Version` is the date this manifest was generated. Story files embed
this date when created. `/story-readiness` compares a story's embedded version
to this field to detect stories written against stale rules. Always matches
`Last Updated` — they are the same date, serving different consumers.

This manifest is a programmer's quick-reference extracted from all Accepted ADRs,
technical preferences, and engine reference docs. For the reasoning behind each
rule, see the referenced ADR.

---

## Foundation Layer Rules

*Applies to: scene management, event architecture, save/load, engine initialisation*

### Required Patterns
- **[rule]** — source: [ADR-NNNN]
- **[rule]** — source: [ADR-NNNN]

### Forbidden Approaches
- **Never [anti-pattern]** — [brief reason] — source: [ADR-NNNN]

### Performance Guardrails
- **[system]**: max [N]ms/frame — source: [ADR-NNNN]

---

## Core Layer Rules

*Applies to: core gameplay loop, main player systems, physics, collision*

### Required Patterns
...

### Forbidden Approaches
...

### Performance Guardrails
...

---

## Feature Layer Rules

*Applies to: secondary mechanics, AI systems, secondary features*

### Required Patterns
...

### Forbidden Approaches
...

---

## Presentation Layer Rules

*Applies to: rendering, audio, UI, VFX, shaders, animations*

### Required Patterns
...

### Forbidden Approaches
...

---

## Global Rules (All Layers)

### Naming Conventions
| Element | Convention | Example | Source |
|---------|-----------|---------|--------|
| Classes | [from naming.* in project.yaml, else technical-preferences.md] | [example] | [`naming.classes` in project.yaml, or technical-preferences.md] |
| Variables | [from naming.* in project.yaml, else technical-preferences.md] | [example] | [`naming.variables` in project.yaml, or technical-preferences.md] |
| Signals/Events | [from naming.* in project.yaml, else technical-preferences.md] | [example] | [`naming.signals` in project.yaml, or technical-preferences.md] |
| Files | [from naming.* in project.yaml, else technical-preferences.md] | [example] | [`naming.files` in project.yaml, or technical-preferences.md] |
| Constants | [from naming.* in project.yaml, else technical-preferences.md] | [example] | [`naming.constants` in project.yaml, or technical-preferences.md] |

### Performance Budgets
| Target | Value | Source |
|--------|-------|--------|
| Framerate | [from performance.* in project.yaml, else technical-preferences.md] | [`performance.target_framerate` in project.yaml, or technical-preferences.md] |
| Frame budget | [from performance.* in project.yaml, else technical-preferences.md] | [`performance.frame_budget_ms` in project.yaml, or technical-preferences.md] |
| Draw calls | [from performance.* in project.yaml, else technical-preferences.md] | [`performance.draw_call_limit` in project.yaml, or technical-preferences.md] |
| Memory ceiling | [from performance.* in project.yaml, else technical-preferences.md] | [`performance.memory_ceiling_mb` in project.yaml, or technical-preferences.md] |

### Approved Libraries / Addons
- [library] — approved for [purpose] — source: technical-preferences.md

### Forbidden APIs ([engine version])
These APIs are deprecated or unverified for [engine + version]:
- `[api name]` — deprecated since [version] / unverified post-cutoff
- Source: `docs/engine-reference/[engine]/deprecated-apis.md`

### Cross-Cutting Constraints
- [constraint that applies everywhere, regardless of layer] — source: [ADR-NNNN, preference key or engine-reference file]

6. Suggest Next Steps

After writing the manifest:

  • If epics/stories don't exist yet: "Run /create-epics layer: foundation then /create-stories [epic-slug] — programmers can now use this manifest when writing story implementation notes."
  • If this is a regeneration (manifest already existed): "Updated. Recommend notifying the team of changed rules — especially any new Forbidden entries."

Collaborative Protocol

Applies in collaborative mode (the default). For guided and autonomous modes, see .claude/docs/automation-modes.md — the rules below describe what collaborative mode requires, not universal behavior.

  1. Load silently — read all inputs before presenting anything
  2. Show the summary first — let the user see the scope before writing
  3. Ask before writing — always confirm before creating or overwriting the manifest. On write: Verdict: COMPLETE — control manifest written. On decline: Verdict: BLOCKED — user declined write.
  4. Source every rule — never add a rule that doesn't trace to an ADR, a technical preference, or an engine reference doc
  5. No interpretation — extract rules as stated in ADRs; do not paraphrase in ways that change meaning

Frequently asked questions

What does the Create Control Manifest AI skill do?

Flat must-do/never-do rules sheet per system and layer, extracted from Accepted ADRs. ADRs explain why; this is actionable.

Why use Create Control Manifest on TypingMind?

Because you install it once and use it with any model. Create Control Manifest 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 Create Control Manifest in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Donchitos/Claude-Code-Game-Studios/tree/main/.claude/skills/create-control-manifest. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Create Control Manifest?

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 Create Control Manifest?

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

Is the Create Control Manifest AI skill free?

Yes. It is published on GitHub by Donchitos 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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