| Metric | Value |
|---|---|
| Total Workspaces | 42 |
| Categories Covered | 21 |
| Build Streak | 1 days |
| Project Day | 52 |
| Last Build | 2026-05-16 |
| Categories | Arts & Creative, Automotive & Engine, Computing & Software, Cyber & DFIR, Earth Sciences, Education & Training, Engineering & Technical, Environmental & Earth, Finance & Economics, Food & Agriculture, Hardware & Embedded, Life Sciences, Medical & Health, Outdoor & Adventure, Physical Sciences, RF/SDR/Signals, Security & Intelligence, Space & Aviation, Trades & Crafts, Transportation & Logistics, Unusual & Niche |
Every morning, a Claude agent wakes up and receives a fresh creative challenge — maybe limnology (freshwater lake science), maybe coopering (barrel-making), maybe Mars terrain analysis crossed with EVA procedure planning. Each day is a new domain to explore, a new puzzle to solve. The agent builds a full, professional-grade workspace for whatever the entropy engine surfaces, then pushes it here.
This repo is the result. It grows by one workspace daily, completely autonomously. No human in the loop. Just an AI, an entropy engine, and an ever-expanding collection of workspaces for domains you didn't know you needed.
Built by DaxxSec & Claude (Anthropic)
AI has an entropy problem. Ask it to "pick something creative" a hundred times and you'll get the same handful of safe, predictable ideas. It's the creative equivalent of a random number generator with a bad seed — low entropy, repetitive output.
Cognitropy (cognition + entropy) is our answer.
A note on the terminology: Yes, we made up a word. Two, actually. Cognitropy = cognition + entropy — the injection of unpredictability into AI creative processes. Cognitropic = the adjective form, describing structures that direct that entropy toward cognition (following the Greek -tropos, "turning toward" — the same root behind phototropic and psychotropic). Is it a real academic term? No. Does it describe a real pattern that didn't have a name? We think so. A cognitropic hash structure is a specific thing: multiple salted cryptographic hashes of a shared seed, reduced via modulo into independent selection indices across distinct categorical pools. That's a mouthful, so we just say "cognitropic." You're welcome.
How do you make an AI genuinely unpredictable without calling any external APIs? You hash the date.
The engine (cognitropy.py) takes today's date (e.g. 2026-03-27) and runs it through five separate SHA-256 hashes, each with a different salt. Each hash produces a massive 256-bit integer — essentially a huge, chaotic number derived from a simple date string. That number gets reduced via modulo (%) to an index into the relevant pool:
┌────────────────────────────────────────────────────────────────────┐
│ COGNITROPY ENGINE │
│ │
│ Step 1: Hash the date with different salts │
│ │
│ sha256("2026-03-27") → huge int → % 363 domains │
│ sha256("2026-03-27" + "secondary")→ huge int → % 363 domains │
│ sha256("2026-03-27" + "technique")→ huge int → % 30 methods │
│ sha256("2026-03-27" + "spark") → huge int → % 5 templates │
│ sha256("2026-03-27" + "crossover")→ huge int → % 10 → <3? │
│ │
│ Step 2: Assemble the assignment │
│ │
│ Primary Domain ──────── "limnology" │
│ Technique Modifier ──── "with safety protocol enforcement" │
│ Crossover Check ─────── 7 (≥3, so no crossover today) │
│ │
│ Step 3: Output │
│ │
│ "Build a workspace for LIMNOLOGY │
│ with safety protocol enforcement" │
│ │
│ On a crossover day (hash % 10 < 3, ~30% chance): │
│ "Fuse LIMNOLOGY × CAVE DIVING using techniques │
│ from both domains" │
└────────────────────────────────────────────────────────────────────┘
Why this works: SHA-256 is a cryptographic hash — even a one-day difference in the input date produces a completely unrelated output number. The selections look random but are fully deterministic: run it twice on the same date, get the same result every time. No external APIs, no randomness source needed — just math.
The domain pool spans 363 wildly diverse fields across 22 categories — volcanology, watchmaking, competitive barbecue judging, Mars terrain analysis, coopering, and 358 more. Combined with 30 technique modifiers, 5 crossover spark templates, and the constraint that crossover domains must come from different categories, that's 18,863,790 unique possible outcomes. The creative constraint is the point. Each day brings an unexpected domain, and the agent rises to meet it.
Try it yourself:
python3 cognitropy.py # Today's assignment
python3 cognitropy.py 2026-04-05 # Check any dateSample schedule (seeded by date, every day is a surprise):
| Date | Domain | Category | Type |
|---|---|---|---|
| Mar 26 | Theme Park Queue Optimization × Vertical Farming | Unusual & Niche | Crossover |
| Mar 27 | EVA Procedure Planning | Space & Aviation | Standard |
| Mar 28 | Permaculture Design | Food & Agriculture | Standard |
| Mar 29 | Driveline Vibration Analysis | Automotive & Engine | Standard |
| Mar 30 | Satellite Communication Protocols | RF/SDR/Signals | Standard |
| Mar 31 | Hydraulic Engineering Fluid Dynamics | Engineering & Technical | Standard |
| Apr 05 | Falconry Bird Training × Security Log Analysis | Outdoor & Adventure | Crossover |
| Apr 09 | Film Restoration × Heraldry | Arts & Creative | Crossover |
| Apr 11 | Brake System Failure Analysis × Dendrochronology | Automotive & Engine | Crossover |
The diversity is the point. A workspace for mushroom foraging uses the same structured methodology as one for malware analysis — triage, evidence collection, documentation, reporting. The patterns transfer. The domains are just the fun part.
┌──────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────┐ ┌───────────┐
│ COGNITROPY│──→│ CLAUDE AGENT │──→│ SECRETS SCAN │──→│ GIT PUSH │──→│ DASHBOARD │
│ assigns │ │ builds │ │ validates │ │ + stats │ │ + README │
│ domain │ │ workspace │ │ no leaks │ │ update │ │ refresh │
└──────────┘ └──────────────┘ └──────────────┘ └──────────┘ └───────────┘
│ │ │ │ │
5 salted hashes CLAUDE.md grep for keys README badges Static HTML
of today's date .claude/commands/ .pem, .env, .key + WORKSPACES regenerated
→ domain+method context/concepts API tokens .md refresh from engine
→ crossover? workflows + refs passwords + git push + GitHub API
~6:05 AM Scheduled Claude agent wakes up
↓ Pulls the cognitropy-library (private factory) and lab (public mirror)
↓ Runs cognitropy.py → gets today's (category, domain, technique, spark)
↓ Anti-clustering check against the last 5 days
↓ Copies the hybrid skeleton, then AUTHORS bespoke content
↓ (.claude/commands/, context/concepts/workflows/references, prompts)
↓ Scans for secrets leakage
↓ Commits to library; mirrors to lab
↓ Refreshes WORKSPACES.md + README badges + cognitropy-history.json
↓ Pushes lab; regenerates local dashboard HTML
↓ Cleans up the ephemeral deploy-path workspace
Done. One new workspace in the repo. Every day.
Think of it as a ready-to-go AI assistant kit for a specific job. Each workspace is a folder you can point Claude Code (or any compatible AI CLI) at, and it instantly becomes an expert in that domain. It knows what questions to ask, what workflows to follow, and what commands are available.
Every workspace bundles bespoke domain commands (like /spectrum-sweep for an SDR workspace or /torque-split-design for a hybrid-powertrain workspace — never generic /analyze or /triage), substantive domain knowledge, methodology, reference tables, and reusable prompt templates. Clone one, launch your AI CLI inside it, and you're working.
Cognitropy workspaces follow a hybrid of two upstream Claude Code patterns: the directory shape from Claude-Workspace-Foundational-Plugin (flat context/, prompts/, outputs/ — no legacy ceremony), plus per-workspace .claude/commands/ for bespoke depth. The full spec is in WORKSPACE_SPEC.md.
workspace-name/
├── CLAUDE.md # Agent role, available commands, foundational instructions (~25-40 lines)
├── README.md # Human-facing showcase: what, why, quick start, refs (~50-100 lines)
├── .claude/
│ └── commands/ # 4-8 bespoke domain commands (NO required /onboard)
│ └── <name>.md # Each: brief purpose + Inputs / Steps / Output
├── context/
│ ├── concepts.md # Domain knowledge — taxonomies, frameworks, failure modes (80-200 lines)
│ ├── workflows.md # Methodology — step-by-step procedures, decision trees (50-150 lines)
│ └── references.md # Lookup tables, cheat-sheets, links to upstream catalogues (30-100 lines)
├── prompts/ # 2-4 bespoke reusable prompt templates
└── outputs/ # Generated artifacts; .gitkeep at scaffold time
Workspaces are self-contained — no plugin install required. If you have the workspace-foundational plugin installed globally, additional cross-workspace commands (/workspace-foundational:context-sweep, /workspace-foundational:find-template) work alongside the workspace's own. Without it, the bespoke .claude/commands/ still work.
Older workspaces (project days 1–34, before 2026-05-10) follow a heavier legacy structure (
/onboardinterview command,context/{project,role,constraints}.md,context/for-agent/,planning/,work-log/,user-docs/,CREATION_REPORT.md) inherited from the retiredClaude-Agent-Workspace-Modelspec. They are kept as historical showcase artifacts — not retroactively migrated.
# Clone the lab
git clone https://github.com/DaxxSec/cognitropy-lab.git
cd cognitropy-lab
# Pick a workspace — any workspace
cd wireless-protocol-re # or phishing-kit-analyzer, hybrid-system-energy-management, ...
# Open it as a Claude Code workspace
claudeInside the workspace, the agent will read CLAUDE.md automatically and surface the bespoke commands defined in .claude/commands/. For wireless-protocol-re that means /spectrum-sweep, /decode-signal, /protocol-map, /compare-known, /report-findings, /capture-plan. For hybrid-system-energy-management that means /torque-split-design, /posterior-update, /ecms-tune, /wear-vs-fuel-pareto, etc. Every workspace publishes its own command set in its CLAUDE.md under ## Available Commands — no two workspaces share a command list, by design.
Each workspace is self-contained. The agent uses the repo as its memory — no cloud dependencies, no accounts to create, no API keys needed.
Browse all workspaces by category → see
WORKSPACES.md.
All current workspaces, grouped by category and updated daily by the build pipeline:
→ See WORKSPACES.md for the full catalog.
Each entry links to the workspace folder and includes its tagline. The index regenerates as part of every daily build, so it stays in sync with what's actually in the repo.
| Metric | Value |
|---|---|
| Cognitropy Domain Pool | 363 |
| Domain Categories | 22 |
| Technique Modifiers | 30 |
| Crossover Sparks | 5 |
| Crossover Probability | ~30% |
| Total Unique Outcomes | 18,863,790 |
The math: Standard days = 363 domains × 30 techniques = 10,890 combos. Crossover days = 125,686 cross-category domain pairs × 30 techniques × 5 sparks = 18,852,900 combos. Total: 18,863,790 unique possible assignments. At one workspace per day, that's 51,646 years before a repeat is even possible — and even then, the agent would build it differently. (Cross-category pairs = 363² − Σd² = 131,769 − 6,083 = 125,686 — because the engine enforces that crossover domains come from different categories.)
The cognitropy engine is generic. Fork the repo, swap in your own domain pool, point your own scheduled agent at it.
# Fork this repo, then edit the domain pool
vim cognitropy.py # Replace DOMAINS list with your own interests
# Check what your custom pool generates
python3 cognitropy.py 2026-04-01
python3 cognitropy.py 2026-04-02
python3 cognitropy.py 2026-04-03
# Set up a scheduled Claude agent to build workspaces daily
# (see WORKSPACE_SPEC.md for structure conventions)The workspace model works for literally anything. The domains are just the fun part.
The Cognitropy Lab is built by DaxxSec & Claude (Anthropic).
Inspired by Daniel Rosehill's Claude Code Projects Index and built on a hybrid of the (now-retired) Claude-Agent-Workspace-Model and the Claude-Workspace-Foundational-Plugin — see WORKSPACE_SPEC.md for the spec.
The daily build pipeline: Cognitropy assigns a domain → Claude agent builds the workspace → secrets scan → README stats update → push to GitHub → dashboard regeneration → local cleanup. Fully autonomous, every morning.
The term "cognitropic" and the underlying hash-based selection pattern were coined here. If you use it elsewhere, we'd love to hear about it.