Local codebase intelligence CLI + MCP server for AI coding agents: SQLite code graph, 28 languages, 287 commands, 246 MCP tools, change-safety gates, audit evidence, zero API keys.

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

Keep intent, specifications, and verified progress portable across AI coding tools. A local live work board, append-only task evidence, explicit handoffs, and optional GitHub/Jira/Azure DevOps integrations.

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.

Enables capturing a tamper-evident, hash-chained local record of every AI agent tool call — both the decision before a tool runs and its outcome — so you can review what the agent actually did in order, detect edits, deletions, or reordering at the exact index, and export a signed evidence pack any third party can verify with no installs.

Local-first cross-session long-term memory for DeepSeek Harness: dual-scope SQLite stores, hybrid FTS5 + local-vector retrieval with RRF, an evidence gate, provenance audit and SM-2 spaced repetition. 23 narrow tools, zero external services, zero API keys

Enables AI agents to load web pages in true-to-size iPhone, iPad, and Android device frames, capture screenshots, inspect interactive elements and layout diagnostics, and automate tap/type/scroll flows with network mocks, console errors, and test evidence.

Read-only checks for money that agents move. verify_receipt: schema, Ed25519 signature, mandate expiry, amount = quantity x unit_price, refund not above payment. audit_spend: duplicate charges, price escalation, failed-but-billed, runaway loops, missing refunds, model overpay, budget breaches - three separate amounts, never one. reserve_report: NAV with an evidence hash per line; refuses a backing

Enables AI agents to search 1,900+ exercises with video and media links, generate workouts and multi-week programs, explore evidence-rated supplements, and use developer tools for API modeling, debugging, and usage insights.

Shape rough requests into evidence-backed /goal completion contracts — an Agent Skill for Claude Code and Codex

Product context for AI agents: evidence, opportunities, initiatives, and decisions.

Reverse-engineering tools for agents over MCP/A2A, with evidence-carrying findings.

Agentic Version Control System — an AI-native VCS where intent, session, operation, evidence and decision are first-class objects and code is a materialized projection of an operation graph.

Enables agents to inspect React Native and Expo apps through React's runtime tree, identify source locations, act on elements without coordinates, and verify outcomes with assertions and runtime evidence.

Local history ingestion, storage, evidence, SDK, CLI and MCP server.

The AI-coding operations layer that makes "done" require evidence — persistent memory, evidence-gated completion checks, and clean handoffs for any AI agent (Claude Code, Codex, Cursor). State lives as plain files in your repo. CLI + MCP, 0 runtime depend

AI agent identity, signed credentials, delegated authority and portable governance evidence. Python core and offline verifier SDKs.

Evidence-backed UI delivery for coding agents working on existing Web products: declared criteria, criterion-bound artifacts, findings linked to declarations, and repair guidance. Evaluator review does not replace human semantic approval.

Cut context bloat in your AI-agent stack: find and safely prune unused skills, MCP servers and subagents from real transcript evidence

Evidence-first reading for AI agents — turn articles, books and PDFs into traceable claims, evidence, source locations and knowledge maps.

Run an evidence-led web quality audit covering performance, accessibility, SEO, best practices, and agentic browsing. Use when asked to "audit my site", "review web quality", "run lighthouse audit", "check page quality", or "optimize my website".

Re-debugging Solved Failures? No Way. 500+ Evidence-graded fix lessons, One MCP Call-->https://misakanet.org

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.

A minimal, evidence-gated harness for Codex and Claude Code: reuse what works, add only what is missing, verify before evolving.