Audit written content for AI-slop and LLM-generation tells, then return a senior-reviewer report with verdict, attributable findings, authenticity fixes, and a human rewrite. Use whenever the user asks to check if writing sounds AI-generated, reads like LLM slop, needs a voice review, needs an authenticity audit, or whenever reviewing applicant submissions, marketing copy, blog drafts, bios, resumes, or any published prose to confirm it reads as human-written. Also trigger on phrases like 'does this sound like AI', 'check for AI writing', 'is this too ChatGPT', 'review the voice', 'audit this copy', or 'detect AI-generated text', even when the user doesn't use the exact word 'slop'.
Assembles a session context briefing from your configured issue tracker, recent git history, and active work signals. Use when: starting a new session, resuming after a break, user says 'catch me up', 'what was I working on', 'where did I leave off', context recovery after compaction, or user asks for project orientation. Complements Claude Code's native /recap (which auto-triggers after extended idle); briefing is invoked explicitly and pulls from cross-tool sources native /recap does not aggregate.
Audit knowledge directories (.memory/, docs/, staging/, archive/) and Claude Code auto memory for stale, misplaced, or duplicated content. Generates an editable cleanup plan file, then executes approved actions after review. Use when: periodic knowledge cleanup, after project milestones, files feel scattered or disorganized, output staging feels cluttered, moving deliverables to permanent homes. Trigger phrases: consolidate knowledge, knowledge cleanup, audit knowledge directories, find stray docs, tidy up knowledge, triage outputs, review staging, clean up outputs.
Manage ephemeral .memory/ conversation state across multi-day sessions. Checkpoint todos, decisions, and questions; promote artifacts to permanent knowledge; clean up after promotion. Use when: the user says 'checkpoint', 'save working memory', 'promote working memory', 'clean up memory', wants to persist conversation artifacts for a future session, or resumes work and asks 'what were we working on' near a .memory/ directory. Do not trigger for native auto-memory operations.
Use when weighing ethical implications, assessing stakeholder impact, evaluating responsible AI practices, or considering long-term consequences. Analyze decisions through an ethical lens — evaluate who benefits and who bears risk, assess reputation trajectory, and apply moral frameworks.
Use when assessing feasibility, building an implementation plan, estimating timelines, or planning resource allocation. Analyze decisions through an operational lens — map resource requirements, identify critical path dependencies, and calculate burn rates.
Use when red-teaming ideas, stress-testing strategies, playing devil's advocate, or doing a reality check on plans. Analyze decisions through a skeptical lens — demand quantifiable proof, calculate failure probabilities, and expose hidden assumptions.
Use when you need comprehensive, multi-perspective analysis of a decision or idea — a full Team of Rivals assessment. Runs all four advisors independently (visionary, skeptic, operator, ethicist), then synthesizes their insights into a unified recommendation with areas of agreement, disagreement, and a risk-adjusted verdict.