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Meta Decisions

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cubetribe
meta-decisions

Meta-decision logic that adapts workflows based on task analysis — security overrides, breaking change escalation, performance paths, emergency hotfix, and documentation optimization

Overview

Publishercubetribe
RepositoryClaudeCode_GodMode-On
Skill namemeta-decisions
Stars
54
Forks
10
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 cubetribe on GitHub. Read the source before you install it.

Installation

Install the Meta Decisions 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/cubetribe/ClaudeCode_GodMode-On/tree/main/skills/meta-decisions
  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/cubetribe/ClaudeCode_GodMode-On.git /tmp/ClaudeCode_GodMode-On
mkdir -p .claude/skills
cp -r /tmp/ClaudeCode_GodMode-On/skills/meta-decisions .claude/skills/meta-decisions
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Meta Decisions 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 Meta Decisions 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 Meta Decisions 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.

Meta-Decision Logic

The meta-decision layer analyzes user prompts and automatically adapts workflows.

5 Meta-Decision Rules

RuleTrigger KeywordsWorkflow Adaptation
securityOverrideauth, jwt, token, password, encrypt, sessionForce @security check on this surface
breakingChangeEscalationbreaking change, deprecate, remove API, migrationRequire @architect review before any change
performanceCriticalPathperformance, optimize, slow, latency, cacheAdd performance benchmarks to @tester
emergencyHotfixhotfix, urgent, critical, production downStreamlined workflow: @builder → deterministic hook, with any skipped gate logged in the Routing Log
documentationOnlydocs only, readme, typo fixSkip @builder, direct to @scribe. Precedence: if the change WRITES release artifacts (VERSION, CHANGELOG.md beyond the sprint-integration [Unreleased] entry), the release-artifact risk signal wins and the release law applies (docs/orchestrator/VERSIONING.md)

Decision Flow

User Prompt Received
    ↓
Orchestrator applies the meta-decision rules natively (Core Rule 3), evaluating:
    ↓
┌─ Security keywords? → securityOverride
├─ Breaking change? → breakingChangeEscalation
├─ Performance? → performanceCriticalPath
├─ Emergency? → emergencyHotfix
└─ Docs only? → documentationOnly
    ↓
None matched → Standard workflow selection

Note: scripts/analyze-prompt.js implemented an earlier version of this logic as a UserPromptSubmit hook. That wiring was removed in v8.5.0 and the script has been deprecated since v8.6.0 — it is kept for reference only. The rules above are applied natively by the Orchestrator, not by a wired hook (docs/orchestrator/META-DECISIONS.md).

Architecture Decision Records (ADR)

Significant decisions are logged in DECISIONS.md:

markdown
## ADR-[NUMBER]: [Title]

**Date:** YYYY-MM-DD
**Status:** Accepted / Superseded / Deprecated
**Context:** [Why this decision was needed]
**Decision:** [What was decided]
**Consequences:** [Impact of the decision]

RARE Responsibility Matrix

For complex decisions, use the RARE matrix:

RoleAgentResponsibility
Responsible@builderDoes the work
AccountableOrchestratorEnsures completion
Reviewed bydeterministic hook + @tester/@security//code-review as declared/applicableQuality assurance
Escalated toUserFinal authority

Escalation Mechanism

Three-tier error handling, expressed as an explicit decision tree so every trigger is classified MANDATORY (must escalate, no orchestrator discretion) or OPTIONAL (orchestrator judgment call, logged). Authoritative definition: docs/orchestrator/META-DECISIONS.md § Escalation Decision Tree.

Escalation Decision Tree

START: an agent, gate, or the orchestrator hits friction
│
├─ Is it a retriable tool error or a missing input the agent can find itself?
│  └─ YES ─▶ TIER 1 (agent self-resolution)
│            - max 2 attempts, same agent, same scope
│            - attempt 2 still fails ─▶ fall through to TIER 2
│
├─ Is it a write-scope conflict, a gate failure with actionable findings,
│  or a mid-sprint routing re-classification (new risk signal appeared)?
│  └─ YES ─▶ TIER 2 (orchestrator resolution)
│            - BLOCKED (conflict)  → orchestrator resolves ownership, re-dispatches
│            - BLOCKED (quality)   → merge findings, rework loop back to @builder
│            - new risk signal     → re-route Smart Routing → Full-Gates, log why
│
└─ Is it one of the following?
   │
   ├─ MANDATORY (Tier 3 human escalation — no orchestrator override):
   │  - security-relevant finding (any agent, any tier)
   │  - scope change vs. the approved plan/sprint file
   │  - verdict conflict NOT resolvable by written criteria
   │    (see Finding-Conflict Adjudication in `docs/orchestrator/QUALITY-GATES.md`)
   │  - any destructive/irreversible action (force-push, history rewrite,
   │    data deletion, prod deploy)
   │  - release/publish steps (tag, GitHub Release, npm publish, merge to main)
   │  - architecture selection between two or more workable alternatives
   │  - design-taste decisions no written criterion can resolve (API
   │    ergonomics, public-surface naming, UX judgment)
   │  - suspicion that the request itself is malformed (requirements
   │    silently conflict, a "bug fix" is actually a design flaw)
   │  - any decision where the Finding-Conflict Adjudication procedure
   │    hit Step 3
   │
   └─ OPTIONAL (orchestrator judgment — permitted to proceed without asking,
      but MUST be logged in the sprint's Routing Log):
      - cost overruns (token/time budget exceeded)
      - repeated rework loops (>2 cycles through Tier 2 on the same item)
      - ambiguity that slows progress but does not block a gate or a write

Judgment-class triggers (four classes — architecture choice between valid alternatives, design taste with no written criterion, malformed-request suspicion, and Adjudication Step 3): authoritative definition is the Judgment-Class Human Gate in docs/orchestrator/QUALITY-GATES.md — a unanimous agent PASS does not waive them, because same-tier agents share the same blind spots: a unanimous PASS at Tier 1/2 is not independent evidence of correctness, it is confident correlated silence. See that section for the correlated-miss floor rationale.

Issue Analysis Enhancement

When processing issues, the meta-layer adds:

json
{
  "type": "feature|bug|enhancement",
  "complexity": "low|medium|high",
  "areas": ["api", "ui", "backend"],
  "meta_rules_triggered": ["securityOverride"],
  "workflow_adaptation": "Added @security check"
}

Emergency Hotfix Workflow

When emergencyHotfix is triggered, speed wins over ceremony — but a skip is a logged skip with a reason, not a silent skip of process (Core Rule 7):

User: "Hotfix: production login broken"
    ↓
@builder (immediate fix)
    ↓
Deterministic hook (typecheck/lint/tests/build)
    ↓
@tester and/or @security skipped IF the sprint's risk profile allows it —
logged in the Routing Log with the reason, not silently dropped
    ↓
@scribe adds the `[Unreleased]` CHANGELOG entry — NOT skippable, even for a
one-line hotfix (`docs/orchestrator/VERSIONING.md`: "No exceptions")
    ↓
DONE

Post-hotfix: Schedule a follow-up with any gate that was logged as skipped.


CC_GodMode — © 2025–2026 Dennis Westermann (dennis-westermann.de). Proprietary — not open source. Free for private, non-commercial use; redistribution or re-hosting outside GitHub is prohibited; attribution required. Official source: github.com/cubetribe/ClaudeCode_GodMode-On. See LICENSE.

Frequently asked questions

What does the Meta Decisions AI skill do?

Meta-decision logic that adapts workflows based on task analysis — security overrides, breaking change escalation, performance paths, emergency hotfix, and documentation optimization

Why use Meta Decisions on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cubetribe/ClaudeCode_GodMode-On/tree/main/skills/meta-decisions. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Meta Decisions?

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 Meta Decisions?

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

Is the Meta Decisions AI skill free?

It is published on GitHub by cubetribe. 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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