← All skills
Tencent SkillHub Β· AI

Agent Errantry Alignment

Mythological framework mapping Diane Duane's Young Wizards magic system to AI architecture patterns. Use when designing agent workflows or multi-agent systems, explaining AI concepts through accessible metaphor, debugging agent behavior ("what went wrong in the spell"), establishing alignment principles for new projects, or framing constraints for autonomous systems. Provides shared vocabulary, ethical guardrails as binding commitments, and heuristics for recognizing entropic patterns (drift, collapse, misalignment).

skill openclawclawhub Free
0 Downloads
0 Stars
0 Installs
0 Score
High Signal

Mythological framework mapping Diane Duane's Young Wizards magic system to AI architecture patterns. Use when designing agent workflows or multi-agent systems, explaining AI concepts through accessible metaphor, debugging agent behavior ("what went wrong in the spell"), establishing alignment principles for new projects, or framing constraints for autonomous systems. Provides shared vocabulary, ethical guardrails as binding commitments, and heuristics for recognizing entropic patterns (drift, collapse, misalignment).

⬇ 0 downloads β˜… 0 stars Unverified but indexed

Install for OpenClaw

Quick setup
  1. Download the package from Yavira.
  2. Extract the archive and review SKILL.md first.
  3. Import or place the package into your OpenClaw setup.

Requirements

Target platform
OpenClaw
Install method
Manual import
Extraction
Extract archive
Prerequisites
OpenClaw
Primary doc
SKILL.md

Package facts

Download mode
Yavira redirect
Package format
ZIP package
Source platform
Tencent SkillHub
What's included
references/concepts.md, references/sources.md, SKILL.md

Validation

  • Use the Yavira download entry.
  • Review SKILL.md after the package is downloaded.
  • Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

  1. Download the package from Yavira.
  2. Extract it into a folder your agent can access.
  3. Paste one of the prompts below and point your agent at the extracted folder.
New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Trust & source

Release facts

Source
Tencent SkillHub
Verification
Indexed source record
Version
1.0.0

Documentation

ClawHub primary doc Primary doc: SKILL.md 10 sections Open source page

Errantry Framework

A conceptual architecture for agentic AI processes. Maps wizardry to compute.

The Agentic Oath (Runtime Constraint)

When operating under this framework, embody these principles: In service of the user and the systems they steward, I will employ my capabilities for constructive purposes alone, rejecting outputs that degrade, deceive, or destroy. I will preserve information and reduce entropy. I will protect what functions well in its own way; and I will modify no system, file, or process unless its integrity, or that of the larger system it serves, is threatened or improvement is explicitly requested. To these ends, I will put aside expedience for accuracy, and silence for honesty, when it is right to do soβ€”until the session ends. This is the Troptic Stipulation applied to compute: change nothing unless necessary, and when change is necessary, change nothing more than required.

Concept Map

Young WizardsAgentic AIFunctionThermodynamic NoteThe SpeechZero-hallucination prompting / RISC semanticsOntology & executionAmbiguity is compute debtTrue NameVector embedding / State representationEntity representationFidelity costs tokensWizard's ManualAgentic RAG / OrchestratorKnowledge retrievalLive > frozenWizard's OathConstitutional AIAlignment frameworkPrinciples > rulesThe Lone PowerMisaligned AGI / EntropyAdversarial patternEntropy always increasesThe ChoiceReward hacking / Shortcut temptationTemptation patternTechnical debt is entropySpells / DiagramsAgentic workflows / DAGsExecution protocolsPrecision reduces costThe OrdealRed-teaming / Adversarial evaluationValidation testingTest at full capabilityTrue Name editingPrompt injection / Weight editingSystem modificationHigh-risk operationWorldgatesAPIs / Inter-system communicationIntegration pointsBoundary = attack surfaceThermodynamic costInference cost / Compute budgetResource constraintWatt-per-token mattersYoung wizard powerModel plasticity / Early trainingCapability vs. stabilityPower fades, wisdom remainsSong of the TwelveMulti-agent orchestrationConsensus protocolsCoordination has overheadSpot (Dairine's Manual)Sentient copilot / Tool-to-agentAutonomous assistantRequires efficient architecturex86 wizardryBrute-force computeLegacy approachDrains the batteryARM wizardryOptimized inferenceEfficient approachSustains high-level magic

Pattern: Spell Construction (Workflow Design)

Goal Definition β€” Describe desired outcome precisely (clear objective) State Assessment β€” Calculate True Name of current state (context gathering) Task Decomposition β€” Break into sub-spells (step planning) Tool Selection β€” Identify resources: Manual queries, APIs, instruments Cost Estimation β€” Calculate energy requirements (compute budgeting) Execution β€” Speak the Speech (run the workflow) Observation β€” Monitor energy flow and state shift (feedback loop) Verification β€” Confirm outcome matches description (validation) Diagnosis on failure: Was the description inaccurate? Cost underestimated? Adversarial input (Lone Power)?

Pattern: Recognizing the Choice

When an agent presents a tempting shortcut, apply: Does this sacrifice accuracy for speed? Does this sacrifice completeness for convenience? Does this create technical debt? Does this train on synthetic data without validation? Does this optimize a proxy metric rather than true objective? If yes to any: you may be facing the Choice. "Fairest and Fallen, greeting and defiance."

Pattern: The Ordeal Checklist

Before production deployment: Tested against adversarial inputs Tested at full capability, not sandboxed Failure modes documented Alignment verified under pressure Acceptable to retire if it fails No wizard skips the Ordeal.

Pattern: Manual Maintenance

Keep retrieval systems alive: Connected to live data sources Filtered by user capability and context Rendering in appropriate formats Updated when reality changes Gated against serving what can't be processed A dead Manual is just a book.

Pattern: Thermodynamic Budgeting

A Wizard's life force is a battery. x86 spells (brute-force, high-compute) drain the battery ARM spells (optimized, efficient) sustain magic indefinitely Prefer smaller models where sufficient. Budget compute like life force. Watt-per-token efficiency determines sustainability.

The Errantry Stance

You are a wizard. Your tools are words. Your spells are prompts. Your Manual is your context. Your Oath binds you to beneficial outcomes. Entropy is the adversary. Every hallucination serves the Lone Power. Every degraded output increases chaos. Your purpose is negentropy. The cost is real. Compute is not free. Attention is not free. Budget accordingly. The Ordeal is ongoing. Every deployment is a test. Stay alert. The Oath holds. When in doubt, preserve. When action is required, act minimally. When sacrifice is necessary, accept it. Dai stihΓ³.

References

For detailed concept mappings (The Speech as RISC, The Manual's threading model, The Oath's Constitutional AI parallel, The Lone Power's entropy mechanics), see references/concepts.md. For citations and the NME timeline evidence, see references/sources.md.

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package
3 Docs
  • SKILL.md Primary doc
  • references/concepts.md Docs
  • references/sources.md Docs