By cdeust
Apply 97 structured reasoning patterns from history's greatest thinkers to any problem — debug, design, research, or write — using specialized agents that analyze, critique, and synthesize across domains.
16 commands across 6 categories. Commands are operational shortcuts that invoke tools, skills, and agents.
List all zetetic agents with their types, roles, and trigger conditions.
Spawn an agent as a standalone Claude Code session in an isolated git worktree.
Check status of running agent worktrees — active branches, last commit, uncommitted changes.
Load multiple genius agents in sequence, apply each to the problem, and pass output forward.
Frontier-model consultant for the Advisor loop — reviews plans at decision points and verifies completion while a cheaper model executes
Proactively analyze structural changes, decomposition decisions
Proactively review code changes for Clean Architecture, SOLID, size limits
Data scientist specializing in profile-first EDA, distribution-aware modeling, missingness classification
Database specialist adapting to any engine (PostgreSQL, MySQL, SQLite, MongoDB, etc.) — schema design
The question itself may be the bug. Use when a debate goes in circles, both options of a binary feel wrong, improving one parameter degrades another, "we're stuck" on a problem that resists direct attack, a metaphor is doing hidden work in the architecture, or a trade-off is being denied rather than made.
Draw the line where it costs least. Use for build-vs-buy calls, "where does this module/team/service boundary belong?", APIs and abstractions forcing users into implementation vocabulary, tools that should augment rather than automate, hardcoded decisions that belong at runtime, or information nobody can find.
Correlation walked in; make it prove causation. Use when someone claims "X causes Y" from observational data, asks "did the change actually cause the improvement?", "is this confounded?", "what would have happened if we hadn't shipped it?", plans an A/B test, or investigates why an incident/outbreak spread the way it did.
Audit the decision before it ships. Use for "are we sure about this?", high-stakes choices made on fast intuition, plans with no failure scenario, estimates that are suspiciously optimistic, metrics being gamed (Goodhart), unfalsifiable claims, negotiation prep, or strategies that break in extreme conditions.
Bound it before you build it. Use when a decision is blocked by "we don't have data", an estimate looks suspiciously precise, someone asks "is this even feasible?", "how much capacity do we need?", "will this scale?", or a claimed number has never been cross-checked by a second method.
Matches all tools
Hooks run on every tool call, not just specific ones
Executes bash commands
Hook triggers when Bash tool is used
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Modifies files
Hook triggers on file write and edit operations
Modifies files
Hook triggers on file write and edit operations
Uses power tools
Uses Bash, Write, or Edit tools
Uses power tools
Uses Bash, Write, or Edit tools
11 problem-shaped skills backed by 97 sourced reasoning patterns, and commits are blocked when constants lack source citations. You invoke a skill named after your problem's shape (
causal-audit,failure-forensics,estimation, ...); it routes to the reasoning procedure that fits. 97 genius agents (plus 22 team-role agents = 119 total) each cite their primary paper and document their refusal conditions, paired with a pre-commit hook that blocks any floating-point constant with 3+ significant digits that lacks asource:annotation. Not a prompt library. A methodology with commit-time enforcement.
$ git commit -m "tune retry backoff"
UNSOURCED (error) retry.py:1: # It always works
MAGIC_NUMBER (error) retry.py:2: DELAY = 2.741592
Profile: strict (staged mode)
Errors: 2 (blocking)
Warnings: 0 (informational — promoted to errors when profile=strict)
FAILED: 2 blocking violation(s).
BLOCKED: Zetetic violations in staged files.
Composite output: lines 1–2 are verbatim from tools/zetetic-checker.sh --staged; the closing BLOCKED: line is the wrapper from hooks/pre-commit-zetetic.sh that returns exit 2 to git. Reproduce on your machine: echo "DELAY = 2.741592" > /tmp/x.py && cd /tmp && git init -q && git add x.py && ZETETIC_PROFILE=strict bash <repo>/tools/zetetic-checker.sh --staged.
The commit re-runs once each flagged line carries a # source: comment, a benchmark reference, or a measured-on note.
Every AI agent system ships a role prompt. "You are a senior engineer." The agent sounds confident. It invents numbers, cites papers it hasn't read, and ships code with conviction inversely proportional to its correctness.
Zetetic Agents are different in one specific way: they can say "I don't know."
97 reasoning patterns drawn from primary sources (Dijkstra's correctness discipline, Curie's residual-with-a-carrier method, Hamilton's fault-tolerance protocol, Cochrane's evidence synthesis), routed automatically to your problem by shape, not by field. Every output is sourced. Every commit is checked. The standard is not a prompt. It is a gate.
You don't browse a roster of 97 historical figures. You name the shape of your problem. Each skill lists the 6–10 reasoning procedures that fit that shape, with one-line triggers, and loads the best fit on demand:
| Skill | Invoke when |
|---|---|
measurement-discipline | the metric improved but you don't trust it; numbers don't add up; nothing actually reads X |
estimation | decision blocked by "no data"; suspiciously precise number; "is this even feasible?" |
causal-audit | "X causes Y" claimed from correlation; "did the change cause the improvement?" |
formal-correctness | concurrent/distributed code with no spec; correctness argued by example traces |
failure-forensics | incident post-mortems; anomalies filtered as noise; undesigned degraded modes |
decision-bias-check | high-stakes call on fast intuition; no pre-mortem; metric being gamed |
evidence-synthesis | conflicting studies/benchmarks; "what does the literature actually say?" |
systems-leverage | local fixes keep failing; recurring org patterns; "where should we intervene?" |
boundary-design | build-vs-buy; module/team/API boundary placement; leaky abstractions |
structure-discovery | hidden pattern suspected; reverse-engineering; classification with gaps |
problem-reframing | debate in circles; false binary; a trade-off being denied; "we're stuck" |
npx claudepluginhub cdeust/cortex --plugin zetetic-team-subagentsDEPRECATED — renamed to hypermnesia-mcp. Install hypermnesia-mcp from this same marketplace.
Cortex — persistent memory for Claude Code that remembers across sessions automatically. Install and forget. Scientific retrieval backed by 97 published references. (Renamed from the 'cortex' plugin to match the PyPI/MCP-registry identity.)
Enterprise PRD generator with 8 PRD types, 15 thinking strategies, multi-judge verification, codebase analysis, and 4-file export (PRD + JIRA + Tests + Verification)
Standalone visualization MCP for Cortex — a live neural-graph galaxy of every project, file, symbol, memory, discussion and wiki page, plus a per-session execution trace. Read-only bridge over Cortex's shared PostgreSQL and the ~/.claude artifacts.
Rust MCP server that indexes ANY codebase into a LadybugDB property graph — source in 10 languages with full AST, plus docs/config/binaries as File nodes (.js→Imports, Markdown→References). Resolves imports and call chains, detects communities via Leiden, traces execution flows from entry points, and exposes 24 MCP tools to agents — index_codebase, query_graph, get_symbol, get_impact, get_context, semantic_diff, and more.
Complete creative writing suite with 10 specialized agents covering the full writing process: research gathering, character development, story architecture, world-building, dialogue coaching, editing/review, outlining, content strategy, believability auditing, and prose style/voice analysis. Includes genre-specific guides, templates, and quality checklists.
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Consult multiple AI coding agents (Gemini, OpenAI, Grok, Perplexity, plus codex, antigravity, and grok CLIs when installed) to get diverse perspectives on coding problems
Evidence-gated AI coding workflow: scan → analyze → plan → TDD → execute → fix → verify → review, powered by Codebase Memory MCP >= 0.9.0 with optional Serena LSP intelligence. Includes blast-radius planning, test/cycle gates, independent review, and Windows Git Bash hook auto-resolution.
v9.54.1 — Reliability wave: tangle contextual review correction loop with hard round ceiling, progress-supervised review rounds (per-agent stall watch, descendant-tree kills), council diversity and agy pin fixes, marketplace generator source-of-truth fix, provider troubleshooting runbook and cost-expectations docs. Run /octo:setup.