Record, build, tailor, and review resumes in a single-column ATS-safe house style: keeps an honest experience record the user owns, generates a formatted Google Doc from that record, an interview, or an existing resume file, aims a copy at a specific job description, and audits any resume against a pre-submission checklist without ever inventing achievements.
File-driven project management for AI agents — every repo change becomes one tracked unit in flat YAML, driven by a CLI + MCP. Setup bootstraps the full stack (npm or straight from GitHub).
Typed, calibrated judgments from TypeSafe Jev: classify, score, check, a batched multi-question ask, a many-item triage with server-side file reads, and model discovery.
SNARC — salience-gated memory for Claude Code. Scores every tool use on 5 dimensions (Surprise, Novelty, Arousal, Reward, Conflict) at capture time. Only stores what's salient. Automatic context injection, confidence decay, dream-cycle pattern extraction.
Self-amendment MCP server framework with layered skill architecture (L0/L1/L2) and blockchain-backed auditability. Skills can evolve, promote, and audit themselves through 29+ MCP tools. Prerequisites: Ruby 3.0+ and 'gem install kairos-chain' required for MCP server functionality.
Process information for real understanding with the explain-back skill: explain from memory, grade against the real source, struggle-then-teach per gap, output Mochi cards and a resumable session record. An antidote to the illusion of clarity.
Extract and analyze large PDFs (3MB-10MB+) with minimal token usage. Preserves 100% of content while achieving 12-103x token reduction through local extraction, semantic chunking, and intelligent caching. Perfect for technical documentation (NIST, ISO, AWS guides), compliance frameworks, and research papers. One-time extraction, instant querying.
Extract and analyze large Excel workbooks (1MB-50MB+) with minimal token usage. Lossless extraction of all formulas, cell formatting, and complex table structures while achieving 20-100x token reduction through local extraction, semantic chunking, and intelligent caching. Perfect for compliance matrices, financial models, security audit logs, and data tables. Tested with 287K cells and 15K formulas.
Extract and analyze Word documents (1MB-50MB+) with minimal token usage. Lossless extraction of all text, tables, formatting, and document structure while achieving 10-50x token reduction through local extraction, semantic chunking by headings, and intelligent caching. Perfect for policy documents, contracts, technical reports, and any large Word document.
Enforces the gws-multi-account layout: blocks bare `gws` CLI commands that do not set GOOGLE_WORKSPACE_CLI_CONFIG_DIR, and ships the matching skill so Claude picks the right account automatically.