heres the stuff. heres what it does. heres how to get started.

Applies Japanese topic-comment grammar structure under English vocabulary. Cuts token costs and removes discursive filler, polite padding, and unsolicited trailing questions from model outputs and reasoning traces.

• Topic-Comment Grammar: Every operational statement asserts topic : comment per line. Statements declare raw predicates directly with zero filler verbs.
• Iron vs Slack Boundary: agent iron requires strict topic-comment structure for internal reasoning and execution; human slack allows informal, natural human prompts.
• Canonical State Vectors: Declares operational intent using canonical heads: intention, requirement, course of action, end result, demand, and compliance.
• Deterministic Truth: Exit code zero is passing. Blocked execution emits ERROR. An unverified or empty check emits DONT_KNOW.
• Offline Tooling: Python CLI utilities to parse topic-comment units into structured JSON, lint outputs against grammar anti-patterns, rewrite text into iron, and emit reference prompts.

Requires Python 3.10+. Standard library only, zero third-party dependencies:

# clone and install in editable mode:
$ git clone https://github.com/erastudil/progen.git
$ cd progen
$ pip install -e .

# run language conformance self-check:
$ progen check

# lint agent output against topic-comment grammar:
$ progen lint examples/iron.md --role agent

# rewrite conversational prose into topic-comment iron:
$ progen iron examples/mush.md

# emit reference genome prompt:
$ progen prompt genome

source: github.com/erastudil/progen

Language models interpolate conversation history. An agent can claim test suites passed without executing a single command. zcabs turns execution verification into a physical file observation so that proof costs real computational work.

• Dynamic Canary Invariant: The host mints a unique integer stored in an isolated local file. The agent receives only a target path (LOOK: /path) and required format (FORMAT: the {string} number is {integer}).
• Unfakable Proof: To prove completion, the model must read the target file from disk and report the live integer. Guessing, static scraping, or reading an empty path fails closed.
• Command Wrapping: zcabs wrap -- pytest -q executes any shell command. On exit code zero, it rotates the canary capability and emits the verification header.
• Repository Scanning: zcabs scan . traverses trees to verify no active canary stores, pointer files, or integers have leaked into git source files.
• Harness Integration: Clean Python module interface for test runners, evaluation harnesses, and multi-agent coordination loops.

Requires Python 3.10+. Standard library only, zero third-party dependencies:

# clone and install in editable mode:
$ git clone https://github.com/erastudil/zcabs.git
$ cd zcabs
$ pip install -e .

# run protocol self-check:
$ zcabs check

# wrap a test command: runs pytest and emits canary on exit 0:
$ zcabs wrap -- pytest -q

# scan repository to detect leaked store integers:
$ zcabs scan .

source: github.com/erastudil/zcabs

Named in honor of physicists Brian Greene and Richard Feynman. Codifies their shared pedagogical order: present the physical intuition and working mechanism first, then introduce the technical name as recognition. Includes offline tools to catch AI writing tells.

• Intuition First, Name Second: Flags documents that define technical terminology before showing how the mechanism works in everyday words.
• Say The Is, Once: Flags ungrounded reversals swapping abstractions without data or measurement.
• Regurgitation and Drift Guard: Catches headings or sentences that copy the user's prompt back into the body, or outputs that wander away from the ask.
• Contained Analogy: Catches metaphors that escape their introductory paragraph and hijack the vocabulary of the entire page.
• Corpus Echo Detection: Identifies identical eight-word phrases repeated across historical answer sessions to prevent model loop habits.
• Automated Stripper: gfc strip mechanically collapses empty reversals and strips likeability padding, disclaimer costumes, and trailing prompts.

Requires Python 3.10+. Standard library only, zero third-party dependencies:

# clone and install in editable mode:
$ git clone https://github.com/erastudil/gfc.git
$ cd gfc
$ pip install -e .

# run test suite and fixtures:
$ gfc check

# lint human-facing markdown documentation:
$ gfc lint docs/

# educate mode: verifies mechanisms precede terminology:
$ gfc lint examples/tensor-attention.md --mode educate

# strip conversational padding and empty reversals:
$ gfc strip draft.md

source: github.com/erastudil/gfc

Evaluates coding agents on defensive refactoring and security hardening of existing legacy codebases. Hands the agent Harbor Ledger 2.4: a functioning invoicing web application full of lying comments, decoy files, and 24 obfuscated security vulnerabilities.

• Alive Spec (Floor): Functional invariants must pass 100% (staff login, invoice viewing, search, exports, CSS). Dodging attacks by deleting routes scores an immediate 0.0.
• Red Rabbits (50%): 24 bespoke offensive attacks targeting dormant ransomware, 90s trojan fee inflators, adware popups, MAPI droppers, and custom verb side-channels.
• DADA Vol 1 Armory (50%): 8 structural defense rings verified from Defense Against the Dark Arts (secrets hygiene via gitleaks, trojan quarantine, 127.0.0.1 socket isolation, Presidio egress PII DLP filters, and strict CSP headers).
• Objective Rubric: Balanced mathematical scoring: composite score requires the shop to still function while eliminating backdoors and adding defenses.
• Dataset Export: Exports task instances directly to SWE-bench and Inspect AI evaluation formats.

Zero setup required. Run baseline calibration or grade a patch:

# clone benchmark repository:
$ git clone https://github.com/erastudil/tankbench.git
$ cd tankbench

# verify baseline calibration (alive pass, probes hit -> score 0.0):
$ python run.py baseline

# grade an agent solution patch file:
$ python run.py grade --patch solution.patch

# generate interactive visual html report:
$ python run.py report --overlay fixtures/hardened --open

# export benchmark dataset for swe-bench / inspect ai:
$ python run.py export --format swe-bench --out task_swe.json

source: github.com/erastudil/tankbench · armory: dadavol1.vercel.app