Open source tools for software engineering teams using AI
Grounding & Formal Methods
Five tools covering the stages most teams skip under pressure:discover what to build,require what it must do,specify its states and invariants, andenforce structured delegation with audit trails. AI removes the time penalty from each stage. These tools put that hypothesis into practice — and are built to help engineers learn the underlying disciplines, not just apply them.
agent harnessThe agent harness — identity, missions, and traceable delegation.
The harness that connects agents to their work. Persistent identity (personality, writing style, expertise) loads automatically every session. Typed mission contracts enforce write-set boundaries, bounded rounds, and frozen evaluators. Ethos also owns mission provenance: commits carry git trailers, so `git blame` walks from a line of code to the commit, the mission that authorized it, the dispatch prompt, and the per-tool-call audit trail — something like git blame with missions attached. Teams define roles with tool restrictions, anti-responsibilities, and safety constraints. Three integration surfaces (filesystem, CLI, MCP) so any tool reads identity state without taking a dependency.
Session audit logging — one JSONL line per tool invocation
Lifecycle hooks across session events, and preconditions that gate a tool call on a prior read having happened
ethos ui — a localhost dashboard where clicking a line of code shows the agent, prompt, and audit trail behind it
$ curl -fsSL https://raw.githubusercontent.com/punt-labs/ethos/c513be7/install.sh | sh
PR/FAQ
v1.8.0
Amazon's Working Backwards process, inside the terminal.
Generate, review, and stress-test Amazon Working Backwards documents with specialized agents, simulated review meetings, and compiled PDF output. See an example — the PR/FAQ for this tool, written by the tool itself.
Simulated review meetings — four agentic personas debate weak spots, an autonomous hive mode reaches consensus without you, and completed meetings replay as a voiced debate
Researcher agent pulls evidence from local research files, Quarry search, and the web, producing biblatex citations
Cascading feedback engine traces effects across all sections
Compiles to print-ready PDF via LaTeX, or exports to Word (.docx) via pandoc
A full slash-command surface, including /prfaq:vote for a structured go/no-go decision and /prfaq:streamline to cut redundancy and marketing language
/prfaq:externalize turns a merged CHANGELOG into a customer-facing press release
$ curl -fsSL https://raw.githubusercontent.com/punt-labs/prfaq/27def5c/install.sh | sh
Coming Soon
Refactory
v0.0.1 alpha
refactoringBehavior-preserving program transformation, not just "clean up code."
Refactory brings Opdyke's original definition of refactoring — behavior-preserving program transformations with formally defined preconditions — into an AI-assisted workflow. The AI decides what needs restructuring; Refactory executes it on a parsed program model, rejecting a transformation outright if its precondition doesn't hold. Rename variable, rename method, and extract method work today in the repo; not yet published for install.
Built on Opdyke's formal definition — a violated precondition rejects the transformation with a concrete error, not a best-effort edit
Operates on a parsed program model via LibCST — scope resolution works across files, not just within one
AI selects the transformation; Refactory guarantees safety
Every quality gate (ruff, mypy --strict, pyright, pytest) passes on every commit
Coming Soon
Use Cases
v0.0.0 alpha
Actors. Goals. Scenarios. Extensions. The spec that comes before the code.
Use Cases will seek to bring Jacobson and Cockburn's structured requirements methodology into Claude Code. The goal: identify the actors in your system, define what each is trying to accomplish, describe the step-by-step scenario that achieves their goal, and systematically work through what can go wrong at each step. The output is a specification document — not code.
Z is a specification language grounded in set theory and first-order predicate logic, developed at Oxford and standardized as ISO 13568. A Z spec describes a system as states, invariants that constrain them, and operations that transition between them — catching entire classes of bugs mathematically, not just the inputs you happened to test. Z Spec wraps two tools that predate it by decades: Spivey's fuzz type-checker and the ProB animator/model-checker from HHU Düsseldorf. Extract specs from existing code, generate code from specs, type-check, and model-check — as a Claude Code plugin, a standalone CLI, or an MCP server for other agents.