AI & ML

kreuzberg-dev Skill

Use when defining functions/tools for an LLM to call through liter-llm, or requesting structured JSON outputs. Covers tool schemas, tool_calls handling, and response formats.

kreuzberg-dev Skill

Use when extracting tabular data from PDFs, spreadsheets, or images. Covers layout-aware table detection, table model selection, output formats (markdown / JSON cells), and known limits.

richfrem Bundle

Trigger with "run health check", "check os metrics", "system monitor", or when the user wants to review the Agentic OS liveness metrics across the Event Bus, locks, and memory arrays. Scans context/events.jsonl, os-state.json, and context/memory.md deterministically via kernel.py — no conversational judgment required. Migrated from the former os-health-check agent (2026-09-05): deterministic Bash+Read diagnostic, no interview, no adversarial judgment — fits the skill archetype, not the agent archetype.

richfrem Bundle

(Industry standard: Routing Agent / Orchestrator Pattern) Primary Use Case: Analyzing an ambiguous trigger and routing it to one of the specific specialized implementations. Routes triggers to the appropriate agent-loop pattern. Use when: assessing a task, research need, or work assignment and deciding whether to run a simple learning loop, red team review, dual-loop delegation, or parallel swarm. Manages shared closure (seal, persist, retrospective, self-improvement).

richfrem Bundle

Systematically analyze agent plugins and skills to extract design patterns, architectural decisions, and reusable techniques. Trigger with "analyze this plugin", "mine patterns from", "review plugin structure", "extract learnings from", "what patterns does this plugin use", "check if this plugin is well-structured", "validate plugin compliance", or when examining any plugin or skill collection to understand its design. Use this skill even when the user just says "look at this plugin" or "tell me how this is structured."

richfrem Bundle

Guidance on slash commands vs. skills. Explains modern best practices where skills (skills/<name>/SKILL.md) supersede legacy commands/workflows (commands/<name>.md), and guides users to use create-skill instead for portable, evaluated, multi-agent workflows. Retains reference for creating personal flat prompt shortcuts if explicitly requested.

richfrem Bundle

Conducts a full-system adversarial audit of agent plugins, skills, specifications, and orchestration against enforced runtime contracts using deep reasoning.

richfrem Bundle

Reviews a completed os-eval-runner lab run and backports approved changes to master plugin sources. Trigger with "backport the eval results", "review the lab run", "apply eval improvements to master", "check what the eval agent changed".

bobmatnyc Skill

Security review gate for MCP server installations. Checks provenance, classifies risk, enforces version pinning, and documents credentials exposure before any MCP is added to your environment.

bobmatnyc Skill

Rust 2024 edition core patterns: idiomatic code, error handling, traits/generics, macros, async/concurrency, testing, and project architecture

bobmatnyc Bundle

MCP (Model Context Protocol) - Build AI-native servers with tools, resources, and prompts. TypeScript/Python SDKs for Claude Desktop integration.

swaylq Bundle

管理咨询 (管理咨询 (Management Consulting) — 战略与管理咨询的职业认知操作系统，从业者/咨询顾问/想入行者/采购咨询服务的客户视角。覆盖: (a) 第一性张力 — 假设驱动 (hypothesis-driven / answer-first / Day-1 answer) 的「先有答案再验证」⇄ 数据驱动 (data-led / bottom-up) 的「先穷尽事实再归纳」，这行的核心张力是「先射箭再画靶 vs 先画靶再射箭」，资深人偏 top-down 假设树减少 boil-the-ocean，但要时刻防 confirmation bias; 以及更深层的张力 — 「卖洞见 (insight / so-what) vs 卖工时 (analysis / 体力)」，「客户影响 (client impact / 落地) vs 智识严谨 (intellectual rigor / 漂亮 deck)」; (b) 方法论正典 (最标准化、最易蒸出高质量) — MECE (相互独立完全穷尽)、issue tree / logic tree (问题树/逻辑树)、hypothesis-driven problem solving (假设驱动)、Pyramid Principle (Minto 金字塔原理: SCQA 情境-冲突-疑问-回答 + 结论先行 + 自上而下 + 归纳/演绎)、80/20 (帕累托/抓大放小)、so-what (所以呢/洞见提炼)、storyline (故事线/ghost deck 鬼影稿)、driver tree / profit tree、2x2 矩阵、value chain、frameworks (Porter 五力/3C/4P/7S/BCG 矩阵/价值链) 既是脚手架也是陷阱 (套框架 vs 真洞见); (c) 行业结构 — MBB (McKinsey/BCG/Bain 战略三巨头) vs Big Four 咨询臂 (Deloitte/PwC Strategy&/EY-Parthenon/KPMG) vs 精品战略所 (Oliver Wyman/Kearney/Roland Berger/L.E.K./Arthur D. Little) vs 专业/职能 boutique (运营/数字化/PE 尽调); up-or-out (非升即走)、leverage model (

swaylq Bundle

推特中文圈 AI 自媒体博主 (推特(X) 中文圈 AI 自媒体博主 (从业者视角) — 在 X/推特 用中文做 AI 内容的自媒体博主，重点是**怎么写推文/thread、怎么制作内容**。覆盖: (a) 选题 — AI 模型发布解读 / prompt 技巧 / AI 工具实测 / AI 出海与独立开发(build in public) / 英文一手编译翻译 / 信息差 / AI 资讯快讯 / 深度长推; (b) 文案与内容结构(核心) — 单条推(开头钩子/信息密度/短句断行/配图截图/数据图)、长推与 thread(首条钩子/编号/逻辑链/结尾关注 CTA)、引用锐评(quote tweet)、排版可读性、配图(ray.so/carbon 代码图/产品截图/对比图); (c) 内容制作工作流 — 找信息源(英文一手/arXiv/官方/Reddit/HN)→选题→编译总结加洞察→写推/thread→配图→发布→互动维护; (d) AI 工具用于内容生产 — 英文一手编译总结(GPT/Claude/Kimi)、配图(ray.so/carbon/截图美化)、thread 工具(Typefully/Hypefury)、翻译、排程分析; (e) 平台机制与涨粉 — X 算法时间线(转评赞/互动权重/外链降权)、蹭热点借势、互动与回复、build in public 涨粉、英文区搬运到中文区的信息差红利; (f) 变现(次要) — 广告 / 知识星球 / 社群 / 付费课程 / 咨询 / 导流。学派分歧: 编译搬运(英文一手翻译) vs 原创洞察、快讯资讯 vs 深度长推、锐评玩梗 vs 严肃干货、build in public vs 纯内容、全 AI 自动发 vs 人工精选。代表生态(宝玉/歸藏/向阳乔木/AI 进化论/小互/Gorden Sun 等)。不含: 英文 AI Twitter 圈(聚焦中文圈)、通用 X 增长营销、其他平台、AI 工具技术原理。) Master OS — automated mastery of 推特(X) 中文圈 AI 自媒体博主 (从业者视角) — 在 X/推特 用中文做 AI 内容的自媒体博主，重点是**怎么写推文/thread、怎么制作内容**。覆盖: (a) 选题 — AI 模型发布解读 / prompt 技巧 / AI 工具实测 / AI 出海与独立开发(build in pu

kreuzberg-dev Skill

Use when the user wants a long-running HTTP service for scrape/crawl/map instead of one-shot CLI calls or the MCP server — for example wiring crawlberg into other apps over REST. Covers `crawlberg serve`, the Firecrawl-v1-compatible endpoints, `--host`/`--port`, and when to prefer it.

kreuzberg-dev Skill

Use when extracting keywords (YAKE/RAKE) from documents — and, secondarily, when detecting document language or generating embeddings for RAG and search. Covers the keyword config (and its feature gating), `--detect-language`, and the standalone `embed` command with real flags.

kreuzberg-dev Skill

Use when running the `liter-llm api` OpenAI-compatible gateway — virtual keys, per-key rate limits, budgets, cost tracking, and model routing. Covers the TOML config and the 22 REST endpoints.

kreuzberg-dev Skill

Use when streaming tokens incrementally from an LLM via liter-llm over SSE or async iterators. Covers chat_stream, delta handling, and null-content chunks.

kreuzberg-dev Skill

Use when calling LLM APIs through the liter-llm MCP server's 22 tools, and to decide when MCP beats the CLI or SDK. Covers the tool surface, the auto-installing launcher, and authentication.

kreuzberg-dev Skill

Use when generating embeddings, calling the 12 web-search providers, or running OCR over documents with the 4 OCR providers through liter-llm. Covers embed, search, and ocr methods plus reranking.

kreuzberg-dev Skill

Use when the user wants to split source code into chunks for an LLM context window without breaking syntax mid-construct. Covers `ts-pack process --chunk-size`, why syntax-aware splits beat fixed-byte splits, picking a size, and the chunk JSON shape.

richfrem Bundle

Selects confirmed native worktree facilities or the governed portable repository worktree fallback, validates placement, and reports cleanup guidance.

richfrem Bundle

(Industry standard: Loop Agent / Single Agent) Primary Use Case: Self-contained research, content generation, and exploration where no inner delegation is required. Self-directed research and knowledge capture loop. Use when: starting a session (Orientation), performing research (Synthesis), or closing a session (Seal, Persist, Retrospective). Ensures knowledge survives across isolated agent sessions.

richfrem Bundle

Triggers the L5 Red Team Sub-Agent to rigorously audit a plugin against the 39-point L4 pattern matrix.

richfrem Bundle

Bootstraps a skill evaluation lab repo for an autoresearch improvement run. Trigger with "set up an eval lab", "bootstrap the eval repo", "prepare the test repo for skill evaluation", "create an eval environment for this skill", "set up the lab space for this skill", or when starting a new skill optimization run that needs a standalone test environment.

richfrem Bundle

Maintains a persistent, folder-based log of all agentic-os experiment runs. Each run writes one dated file to context/experiment-log/ and updates index.md. Supports five source types: verifier (qualitative), tester (qualitative), orchestrator (numeric), planner (qualitative), survey (mixed). Handles both numeric results (eval scores, KEEP/DISCARD, delta) and qualitative results (PASS/FAIL/PARTIAL, gap analysis). Use after any experiment run to persist findings before temp/ is cleared.

richfrem Bundle

Trigger with "remember this", "update memory", "what should we record from this session", "capture learnings", "write a session log", or when closing a session. Guides agents on managing memory hygiene across sessions, deciding what to write to dated memory logs, what to promote to long-term memory.md, and when to archive.

richfrem Bundle

Scaffolds a new autonomous sub-agent with its own prompt, system instructions, and tool permissions. Enforces modern architectural guidance: sub-agents are reserved for isolated execution contexts, strict tool sandboxing, or adversarial personas. NOT for simple procedural skills (use `create-skill`) and NEVER for pointer-wrapper stubs that merely delegate to a skill.

pingqlin Skill

Specialized agent for comprehensive .NET framework upgrades with progressive tracking and validation

richfrem Bundle

Fixes broken path references in plugin skill and agent files to ensure portability across installed environments. Use when you see "plugins/" paths in SKILL.md or agent files, need to standardize path references after installing a skill, want to audit and fix cross-plugin path dependencies, run a portability audit on a repository, neutralize hardcoded machine paths like /Users/, find Python scripts using PROJECT_ROOT or Path() to reach into plugins/<name>/ at runtime, or are preparing plugin files for distribution via uvx or bootstrap.py. Also handles evolving a skill in-session while tracking quality scores with the eval runner to continuously improve skill routing accuracy.

richfrem Bundle

Coding conventions enforcement agent. Auto-invoked when writing new code, reviewing code quality, adding headers, or checking documentation compliance across Python, TypeScript/JavaScript, and C#/.NET.

richfrem Bundle

Skill for orchestrating the end-to-end GitHub issue lifecycle flow: Issue -> Worktree -> Implementation -> PR Creation -> Resolution Closure. USE ONLY when running or dry-running full lifecycle orchestration for resolving an issue with a PR. DO NOT USE for isolated worktree management only (use `issue-worktree-agent`) or logging issues (use `github-issue-agent`).

richfrem Bundle

Initialize and onboard a new project repository as an Obsidian Vault. Covers prerequisite installation, vault configuration, exclusion filters, and validation. Use when setting up Obsidian for the first time in a project.

richfrem Bundle

Executes complex workflows using deterministic graph-state machines, explicit transition guards, transactional worktree isolation, receipt gates, and automatic rollback on verification failure.

richfrem Bundle

(Industry standard: Review and Critique Pattern) Primary Use Case: Iterative generation paired with adversarial review, continuing until an 'Approved' verdict is reached. Orchestrated adversarial review loop. Use when: research, designs, architectures, or decisions need to be reviewed by red team agents (human, browser, or CLI). Iterates in rounds of research → bundle → review → feedback until approved.

richfrem Bundle

Pattern 5: Concurrent Event-Driven Multi-Agent Loop. Coordinates multiple Claude sessions as OS threads sharing a common event bus and memory address space. Every loop cycle is a full improvement cycle: execute, eval against benchmark (KEEP/DISCARD), emit friction events, and close with surveys, metrics, memory persistence, and Triple-Loop triggers.

bobmatnyc Skill

Use when auditing a codebase for semantic duplication — functions that do the same thing but have different names or implementations, especially common in LLM-generated codebases