Engineering decisions, change impact, documentation audits, and CI evidence for coding assistants.

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

Open source (AGPL-3.0) evidence-grounded repository audit CLI - deterministic scanner, MCP server, live dashboard, and a GitHub Action that posts PR diffs.

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.

Stop your AI from making things up — it proposes, deterministic tools decide, every claim checked against ground truth with evidence. Grounded facts and context survive resets. Reverse engineering is the proving ground. MCP server + CLI.

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.

Audit AI applications and agents for security risks before commit, PR, merge, or deployment. Deterministic findings with coverage and evidence receipts.

Enables AI agents to drive policy evaluation over MCP by starting executions, answering policy questions from case evidence, and reading deterministic outcomes with reasoning traces, while the runtime controls which questions are asked and how answers combine.

Enables AI clients to inspect installed software packages and saved browser traffic through read-only local parsers, producing structured evidence about application structure, dependencies, endpoints, and observed authentication schemes.

Enables coding agents to propose, review, promote, and verify architecture changes as recorded C4 model updates, with hypothetical diffs, evidence-based qualification, and component histories.

Enables keyword search across source-traceable evidence chunks, listing opportunities linked to supporting evidence, and deterministic ROI calculations with model-proposed inputs and TypeScript-computed dollar figures.

Checks a site for AI crawlers: which of 11 AI crawlers its robots.txt lets in, whether its llms.txt follows llmstxt.org, and what its home page says without scripts. Findings with evidence; no scores, no model calls, no telemetry.

Enables AI hosts to retain evidence, check explicit constraints, plan bounded work, and record outcomes through local-first MCP operations. Optional reconstruction tools also support experimental image-to-3D workflows with local model providers and Blender.

Enables evidence-first AWS architecture work through deterministic design calculations, live official AWS knowledge, integration catalog/configuration, and optional authenticated local read-only AWS tooling for requirements, comparisons, diagrams, ADRs, and operational planning.

Enables agents to probe URLs and classify bot-wall responses with vendor and signal evidence, perform offline classification, scan for exposed key-like material, and run owner-side access audits through stdio tools.

Enables AI agents to query Korean public data from OpenDART, data.go.kr and law.go.kr — corporate filings and financials, stock prices, apartment trades, business registration status, holidays, statutes in force on a given date, and court rulings. It corrects the live APIs' known traps (mislabeled periods, not-yet-in-force laws, cancelled trades, changed district codes) so answers come back right, with evidence.

Enables MCP clients to run provider-backed reverse-engineering and software-analysis workflows for source code, binaries, and architecture, with evidence, limits, and optional external tool integrations.

Enables AI clients to observe foreground browser viewports, read filtered page evidence, and run guarded reusable site-pack procedures through the same local runtime as the CLI.

Enables vendors to run a declared check battery against their own MCP server, emit sealed Assay-format scorecards with evidence hashes and environment fingerprints, and have a local verifier re-run a random sample to issue counter-signed verification receipts.

Enables LLM-powered developer environments to discover and evaluate potentially beginner-friendly GitHub issues using explainable heuristics, clear rationale, and direct evidence links over stdio.

A read-only server that enables AI agents to triage Kubernetes CronJobs in a single tool call: whether runs happened and stayed on schedule, whether durations are trending slower or overrunning, which jobs have silently stopped succeeding, and why a given run failed, with evidence and stated basis for each verdict. It also lists active runs and recent failures while enforcing namespace allowlists and no write access.

Enables agents to ingest local registry snapshots, detect removed or newly deprecated tools and schema-breaking changes, verify deprecation claims against hash-chained evidence, and generate migration diffs for agent configurations.

Enables local, evidence-led analysis of a master recording into inspectable stems, timing, beats, harmony, lyrics, MIDI and sonic evidence, then finds complete-bar loopable sections for Shorts and other short-form video. Results stay traceable through source hashes, manifests, JSON/TSV/NPZ artifacts and portable Sonic Visualiser sessions, and it can run as a CLI, Python library, web service, container or MCP plugin.