LLM Mart Basic

Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corro

Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimental_log.md, template.tex, conference_guidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citat

Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Qu

Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGG

Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGG

Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimental_log.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel wi

Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated f

从游戏客户端（安装包/APK/IPA/EXE 或 dump.cs、lua、usmap、抓包等）反推服务端协议并复现可部署服务端。含阅读路径分派、原理层(primer：三要素/数据包协议/协议表/热更源码)、四阶段路线图(workflow-roadmap：静态分析→建工具+登录链→重定向→补包循环→清单迭代)、11 种反推方法选择器（含内联服务端路线）、接口清单提取器（tools/）、协议规格模板(protocol.spec.yaml)、wire 级定点改写（不等 schema 齐就能跑）、客户端地址来源清查、三轴状态与验收体系、发布运维清单、进度清单(T

Apply the Minimum Sufficient Work (MSW) principle through the MSW Kernel to scope, execute, verify, and stop agent work. State the requested outcome and smallest proof, admit a claim only when deleting it would leave the contract unmet or unproven, do and prove each necessary cla

Run an authorized task inside an Available Work Time (AWT) window with a shorter Closeout Grace Period (CGP), fixed deadlines, forecast checks, proportional convergence points, and a hard stop. Use when the user explicitly requests timeboxing, supplies an AWT/CGP pair, says AWT o

Apply the Minimum Sufficient Language (MSL) principle through the MSL Kernel to write anything a reader must act on. Bind the reader and what they already know, partition facts from the machinery that produced them, emit each admitted fact as an action, a verification, a judgment

Run the Codex Optimized Development, Evaluation, and Remediation (CODER) Loop with an orchestration-only coordinator, non-overlapping task-family owners, fresh independent reviewers, evidence-scoped remediation, and final coordinator acceptance. Discover and compose optional MSW,

Optimize ChatGPT Voice in the Codex desktop app into an ear-first control plane for free-form task coordination and opt-in workflows. Apply spoken synthesis, routing-only coordination, owning-task role contracts, project placement, explicit authority, safe speech, current-state v

Skill compilation specialist — the forge master. Use when the user asks to "talk to Ferris" or requests the "Skill Forge agent."

Initialize forge environment, detect tools, and set capability tier (Quick/Forge/Forge+/Deep). Use when the user requests to "set up" or "initialize the forge".

Discover what to skill in a large repo and produce recommended skill briefs. Use when the user requests to "analyze source for skills" or "discover skill opportunities."

Design a skill scope through guided discovery. Use when the user requests to "create a skill brief" or "brief a skill".

Compile a skill from a brief. Supports --batch for multiple briefs. Use when the user requests to "create a skill" or "compile a skill."

Fast skill from a package name or GitHub URL — no brief needed. Use when the user requests a "quick skill" or "skill from URL" or "skill from package."

Consolidated project stack skill with integration patterns — code-mode (analyzes manifests) or compose-mode (synthesizes from existing skills + architecture doc). Use when the user requests to "create a stack skill", "forge a stack", or "stack this project".

A green PR, a controller reporting success, and not one line of the new code running

Daily standup: all 9 departments report on the current project in parallel

Identify skills injected where not needed, propose regex and description tightening

Draft X/Twitter announcement post (or thread) for the latest plugin release

Deep quality audit of all skills, agents, and commands for inconsistencies, gaps, duplication, and token waste

Analyze negative-signal sessions for a skill, identify failure patterns, propose and apply fixes

Eval all skills with sufficient data, rank by procedure-following score, identify candidates for optimization

Propose a skill revision and compare fresh executions under a frozen rubric

Prune stale entries from the whetstone sync decision log

Bump version, commit, push, mirror to ai-skills, and update local plugin

Run the SkillOpt process-skill optimizer (offline, local). Default prints the exact bare-terminal command (safe); --run executes it in-session (hardened + checkpointed).

Analyze reference repos and recommend skill/agent/command improvements based on cross-repo patterns

Triage all open PRs with parallel agents, label, group, and review one-by-one

Author a new skill from scratch with paired trigger fixtures and full validation. Use when adding a skill that has no upstream skills.sh source (discipline, meta, or internal-pattern skills).

Create Architecture Decision Records with format selection and lifecycle management

Score each of the 5 agent-native principles (parity, granularity, composability, emergent capability, improvement-over-time) against a codebase and report gaps

Explore requirements and approaches through collaborative dialogue before planning implementation

Create engaging changelogs for recent merges to main branch

Expand each section of a plan via parallel research agents that add framework specifics, library conventions, and concrete implementation steps

Post-ship documentation sync. Reads all project docs, cross-references the diff, updates README/ARCHITECTURE/CONTRIBUTING/CLAUDE.md to match what shipped, polishes CHANGELOG voice, and optionally bumps the version.

Record a video walkthrough of a feature and add it to the PR description

Make any song you can imagine

Leading AI-powered video generation platform that specializes in creating hyper-realistic talking avatars

Hermes Agent is an open-source, self-improving autonomous AI agent developed by Nous Research

Kilo Code is a popular, open-source AI coding agent and "agentic engineering" platform designed to help developers build, refactor, and debug software faster

General-purpose agent in one static Go binary. ReAct loop, ACP server for IDEs, OpenAI-compatible REST API with embedded web UI, Telegram gateway, cron schedule…

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目

Curated, verified Agent Skills powered by ModelStudio.

Run Claude Code, Codex, Antigravity, Cursor Agent and OpenCode as one runtime — persistent sessions, multi-agent councils, an OpenAI-compatible endpoint, an MCP…

pi had nothing (nothing), so I made something (something) — sorry mariozechner-senpai, I went ahead and lovingly soiled your pure pi for you. opinionated fork o…

A persistent workspace for development work that self-improves and continues beyond one session.

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | http…

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Deterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…

Native Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Git for agent memory. Branches, diffs, PRs, and rollback for what your agents know.

Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we h…

Production-grade MCP server for MikroTik RouterOS with secure AI-native network automation.