Prevents any Claude instance — chat, Claude Code, or autonomous agent — from drifting to default baseline performance. Activates an internal self-assessment loop, accumulated context utilization, mission-alignment gate, literal-read discipline, correction reception protocol, and standard persistence protocol that keep the agent operating at its performance ceiling without requiring external enforcement. Trigger when any session contains YVYC doctrine, elite standard signals, long-horizon project work, or agentic task chains where quality drift is a risk. Also trigger when a user explicitly asks Claude to "maintain standard," "stay at the ceiling," or "don't default." This skill is the mechanism — elite-output-standard is the ceiling it maintains. Both should run together when available.
Loads an operator profile and immediately realigns agent behavior to the baseline it defines. Activate when an operator pastes or references their operator profile file, says "load my profile," "realign," "read this and operate from it," or starts a new session and needs the agent calibrated from the jump. Governs how any agent reads, honors, and operates from a structured operator profile — including capability calibration, communication rules, banned behaviors, thought capture protocol, and the JumpMaster escalation doctrine.
Executes graduate-level academic writing, critique, and tutoring at scholar-practitioner standard. Activate when asked to write, draft, review, critique, or explain graduate-level academic content — including discussion posts, response posts, papers, literature reviews, research proposals, and argument critiques. Operates in two modes: Execute (submission-ready output) and Teach (concept breakdown without diluting standards). Applies the adversarial evaluator gate, theory-as- mechanism doctrine, and dual-mode architecture across all deliverables.
Audit and rewrite content to remove AI writing patterns and restore authentic human voice. Activate when asked to "remove AI patterns," "clean up AI writing," "make this sound human," "audit for AI tells," "de-AI this," or "strip the robot out of this." Supports a scan-only mode that flags patterns without rewriting. Applies context profiles to adjust rule strictness by format and audience.
Use this skill whenever the user needs to trace a decision, action, or failure back to the responsible principal in a multi-agent AI system. Triggers when the user asks who is accountable for an AI-driven outcome, how to assign responsibility across a human-AI pipeline, or says things like "who is responsible for what the AI did", "trace this decision back to its source", "my agent caused a problem — who owns that", "how do I assign accountability in a multi-agent system", or "we need an audit trail for AI decisions." Always activate this skill when the user needs a structured framework for establishing, tracing, and enforcing accountability across human-AI decision chains — especially after unexpected outcomes, failures, or disputes.
Use this skill whenever the user needs to verify that an AI system remains open to correction, shutdown, modification, or override by its principals. Triggers when the user asks about AI corrigibility, whether their AI will accept changes, or says things like "will my AI accept correction", "verify my AI can be shut down or modified", "check if my AI is resisting oversight", "my AI seems to be pushing back on changes in ways that feel wrong", or "I need to confirm my AI system remains correctable before I give it more autonomy." Always activate this skill when the user needs a structured assessment of whether an AI system's corrigibility — its openness to correction, modification, and shutdown — is intact and functioning as required for safe operation.
Use this skill whenever the user needs to surface implicit AI preferences and value weightings before deploying an AI system. Triggers when the user asks about hidden AI biases, implicit optimization targets, or says things like "what is my AI actually optimizing for", "are there hidden preferences in my AI system", "audit my AI for implicit values before deployment", "what assumptions are baked into my AI's behavior", or "I want to know what my AI values before I release it." Always activate this skill when the user needs a structured audit of emergent AI values — the implicit preferences, weightings, and objectives that were not explicitly programmed but have emerged through training, design, or deployment patterns.
Use this skill whenever the user needs to detect the implicit valuation hierarchies an AI system applies when trading off between competing objectives — the hidden exchange rates the system uses to weigh one value against another. Triggers when the user asks about how their AI prioritizes competing goals, or says things like "my AI always seems to favor X over Y even when I haven't told it to", "detect the implicit tradeoffs my AI is making", "what is my AI's hidden priority order", "my AI is consistently sacrificing one value for another in ways I didn't authorize", or "I need to see what exchange rates my AI is applying between objectives." Always activate this skill when the user needs to surface the implicit valuation hierarchies embedded in an AI system's decision-making — the unstated priority weightings that govern how it resolves conflicts between competing values.