v2.4.0 · MIT
Multi-language repository map for agents and humans: MCP tools, dependency graphs, symbol indexes, impact review, and bounded source evidence.
v0.9.3 · Apache-2.0
A local detector for AI-writing patterns. Scores every prose file your agent saves. Python standard library only, no network, no model.
v0.2.5 · MIT
A portable memory protocol for AI agents — load it as standing rules; a curation discipline + reference spec + optional cap hook.
v3.34.6 · MIT
Framework-aware code intelligence MCP server — 88 framework integrations, 81 languages, 72.7% fewer input tokens to review a pull request, comprehension at parity
v3.34.6 · MIT
Framework-aware code intelligence MCP server — 88 framework integrations, 81 languages, 72.7% fewer input tokens to review a pull request, comprehension at parity
— · MIT
Graph-It-Live VS Code Extension
v2.40.0 · Apache-2.0
📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. | https://misakanet.org
v2.13.2 · MIT
"ULTRASHIP" Claude Code plugin — 39 skills, 33 tools, 11 agents for ship-ready workflows: planning, review, pentesting, safety guardrails, canary monitoring, SEO/AI-readiness check, penetration testing, code review, competitive analysis, incident response. 1 dependency. 180 tests. MIT.
v2.40.0 · Apache-2.0
📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. | https://misakanet.org
— · Apache-2.0
The ripgrep of AI context: a zero-dependency C++23 CLI + MCP server for coding agents. Find what you want without reading the repo, then check you built what you meant — blast radius, tests-to-run, quality deltas. Signatures at 74.7% fewer bytes than bodies; every guess labelled, every loss published. Paddle out with a map.
v1.0.21 · MIT
Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.