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Auditable code review skill for Claude Code and Codex: four independent probes, evidence-backed findings, and a JSON-derived merge gate.

🎬 Evidence-led Seedance 2.5 / 2.0 prompt library: 840+ cases traced to original posts, 490+ cross-model retest runs, 27 copy-ready templates, 79 installable AI-video Skills. Synced daily from goodcase.ai. EN / 中文 / 日本語.

Evidence-chain whiteboard + MCP server for AI research agents: every claim traces back to the run and code that produced it. 讓 AI agent 做研究時，每個結論都能追到產生它的程式碼。Claude Code · Codex · Cursor · Gemini CLI

Make Gemini code like Claude Models. Forces it to inspect files before editing, plan changes, test its work, and back up every “done” with actual evidence.

LeanCTX is the Context Gateway for AI Systems. Select task-relevant context, apply supported controls, connect local knowledge and inspect recorded evidence. Open-source Engine: CLI, MCP, hooks and supported proxy paths.

Harness self-evolution: update targets, theory, evaluation, and reported evidence. Bilingual reading list and project website.

The Sleuth Kit® (TSK) is a library and collection of command line digital forensics tools that allow you to investigate volume and file system data. The library can be incorporated into larger digital forensics tools and the command line tools can be directly used to find evidence.

Agent-native shopping for extreme value: verifiable same-product price evidence, checkout, orders, delivery, and after-sales.

Open-source AI agent firewall for MCP security and agent egress. Scans mediated HTTP, MCP, A2A, and WebSocket traffic for exfiltration, SSRF, and prompt injection, and emits mediator-signed action receipts: verifiable audit evidence from outside the agent.

Skills for human-led AI teams: delegate tasks, verify evidence, explain steps clearly, and learn from real cases. 一个人统筹 AI 团队：派活、核证、清楚说明、经验复用。

Multi-agent research execution system: paper reproduction, benchmark evaluation, AutoResearch and claim-evidence graphs

An inference chip design that runs a real LLM (Qwen2.5-0.5B) on FPGA — one transformer decoder layer in RTL, every silicon value bit-exact against a golden model. 0.56 tok/s measured, a 140× climb, full evidence trail.

AI/ML research agents from idea to paper-ready evidence. An EvoMap open-source project.

The ADE (agentic development environment) for running a team of coding agents: start a job with a chat, answer what needs you with the evidence, and build apps and dashboards over your data in a chat.

Project memory and workflows for Claude Code and Codex. Keep stories, plans, handovers, and review evidence in your repo. Resume across sessions and follow progress in the Mac app.

IPED Digital Forensic Tool. It is an open source software that can be used to process and analyze digital evidence, often seized at crime scenes by law enforcement or in a corporate investigation by private examiners.

Synapsa Commons: free, hands-on AI courses that run anywhere (Colab, Kaggle, Binder, Codespaces, Jupyter). EU AI Act conformity evidence, model validation, predictive maintenance, document extraction, retrieval and RAG, AI agents and MCP, a simulated humanoid. Every lesson autograded. From the team building Synapsa.

A model-driven harness for AI coding agents: semantic modeling, app generation, progressive API Assist and evidence-based verification.

androidqf (Android Quick Forensics) helps quickly gathering forensic evidence from Android devices, in order to identify potential traces of compromise.

An evidence-backed path from one useful home server to a resilient, AI-capable homelab.

AI-powered data analysis workbench built for transparency, reproducibility and user control. Inspect the code and evidence behind results, verify calculations through replay, and choose from a wide range of models, including local open-weight models.

Local MCP work notes with persistent sessions, decisions, evidence and resumable tasks.

The self-hosted AI SRE agent. You don't have to monitor your systems alone. Versus Incident will keep track of your services, applications, and systems, detect new or unexpected problems, and provide your team with the evidence needed to investigate.

Author, generate, validate and govern AI agent rules, skills, plugins and MCP configuration across 52 coding assistants. Review with verified model evidence, evaluate skills, lock and sign dependencies, and publish bundles. CLI, MCP server and Go API.

Disciplined AI Collaboration: a method for building software with AI. One loop at every size, rules held by checks rather than attention, state derived rather than written, evidence in place of claims. Includes the PAG grammar, an architecture and a queryable ontology. Every page is also served as Markdown and JSON at banes-lab.com.

First Principles Framework (FPF): AI-native pattern languages for systems engineering, research and management. Shared human-AI reasoning for architecture decisions, evidence, trade-offs and method engineering. FPF Core and domain frameworks (DPFs).

Open-source quantitative decision lab: test market assumptions, preserve negative results, and reproduce evidence.

Open-source GRC toolkit from the GRC Engineering Club. Claude Code plugins for evidence collection, SCF crosswalks, multi-framework gap reports, OSCAL workflows.

AI-assisted Perfetto analysis with Web UI, CLI, evidence workflows, reports, and portable runtimes.

Research pipelines as semantic execution units: each skill declares inputs/outputs, acceptance criteria, and guardrails. Evidence-first methodology prevents hollow writing through structured intermediate artifacts.