This is a knowledge/content repository, not a running software system: a curated collection of 11 'Agent Skills' packages compliant with the Agent Skills specification (agentskills.io), designed to be loaded on-demand by AI coding agents (Claude Code, Codex, Cursor, etc.) and other LLM runtimes. Each package is a self-contained directory with a SKILL.md entry point (YAML frontmatter with name/description plus routing instructions), plus references/, assets/, scripts/, and optional agents/ metadata. The skills cover research-analysis (deep-architecture-research with a confirmation-gate and evidence-ledger protocol; neural-mechanism-research; review-ai-conversations with reverse-engineered adapters for Claude Code, Codex, opencode, Grok Build, and Cursor conversation stores; understanding-first-report), software engineering (vibe-coding with scenario/domain/phases sub-references), team collaboration (meeting-coach-leader/worker), agent development (agent-builder with actual Python starter code; skill-judge with a 120-point rubric), and content publishing (media-writer for 7 platforms). There is no application entry point, server, or pipeline; the 'product' is the instructional content and routing logic itself. Although classified as application due to its standalone Python tools and operational guidance (rather than data pipeline), the repo is fundamentally a hybrid: most value comes from the rigor of its written protocols and evaluation rubrics, not executable code. The only real code lives in agent-builder (~4 self-contained Python files: minimal-agent.py, subagent-pattern.py, tool-templates.py, init_agent.py) which implement a clean, security-conscious agent loop. The most impressive craft is the data-engineering-style documentation in review-ai-conversations, which precisely specifies the storage schemas (SQLite tables, JSONL shapes, lineage/parent-child rules) of five proprietary agent tools, each fact tagged with provenance evidence ([CODE]/[DOC]/[ISSUE]) and version-pinned research dates. The skill-judge rubric and deep-architecture evidence protocol likewise demonstrate non-trivial expertise: hard gates, severity levels, evidence-vs-inference discipline, and explicit anti-patterns. The repo is actively maintained (last commit 2026-09-16, in-repo evidence research dated 2026-08-23, plus a documented skill-rename migration path in the READMEs, which exist in English, Chinese, and Japanese). Technical drift is low: the pinned model (claude-sonnet-4-20250514), SDK usage, and cited external tools (Codex 0.148.0, Cursor 2026.08) are current. No test suite exists, but that is the wrong bar for a documentation-oriented collection; the applicable standards are structural validity, evidence quality, and progressive-disclosure design, which are handled well. Weaknesses: media-writer's platform guides are somewhat generic social-media advice, some philosophical prose is repetitive, and the Python examples lack a requirements.txt/pinned dependency file.