Our philosophy on extending 𝚏𝚡: open protocols. ∙ MCP (modelcontextprotocol⁠.io) ∙ Skills (agentskills⁠.io) ∙ Plugins (agent-plugins⁠.org) And the best one, Unix: ① Small programs that do one thing well and compose by calling other programs. ② 𝚕𝚒𝚋𝚏𝚡 that enables embeddability into more complex programs. You should be able to build your own CLI, background agent, software factory. Local or cloud.

Ask an AI to write a paper and it will happily report favorable numbers for experiments it never ran — this "plausible fabrication" has been the real wall for research automation. Earlier automated-research systems poured effort into standing up dedicated agent infrastructure and forcing a single clean story to completion. But somewhere in the long generation, factual integrity would fray, and unsupported claims slipped straight into the paper. Behind the flashy results, the foundation of reliability was quietly missing. Spark-to-Paper flips the framing. Instead of a new agent platform, it implements paper generation as 13 composable skills running inside Claude Code. The key is separation: context-dependent judgment goes to the model, while citation checks and LaTeX compilation go to deterministic code. Experiments fix their metrics and table structure before results are observed, blocking post-hoc adaptation. And a "Self-Refutation Loop" — endlessly revising a hypothesis the evidence keeps rejecting — is capped at seven cycles, recording failure as failure and moving on to a different direction. The results speak clearly. Citation validity hits 99.5%, above the human-preprint average, and fabrication detection climbs from 14% single-pass to 92% with the full adversarial-review stack. At roughly $8.1 and 3.2 hours per manuscript, you get a paper complete with editable figures. Rather than dressing every attempt up as success, it centers evidence integrity and the acceptance that "not every research direction works." A thought-provoking blueprint for reliability in long-horizon AI. Spark-to-Paper: End-to-End Research Paper Generation as a Composable Skill #AIResearch# #AgentSkills#

# Codex Features and Practical Usage 🧩 Tired of re-explaining the same workflow to Codex every time? Agent Skills let you package a reusable playbook once, and Codex loads it only when it is actually needed. 🏷️ Title: Agent Skills (SKILL.md) 🔗 URL: 📘 Overview Agent Skills package routine workflows into a single `SKILL.md` file you can reuse. Only a skill's name and description sit in Codex's context at all times; the full body loads when Codex decides to invoke it. You scope skills by where you place them: repository-wide, personal, or machine-level. ⚙️ How It Works ・A skill is a folder with `SKILL.md` (required) plus optional `scripts/` (executable code), `references/` (docs), `assets/` (templates), and `agents/openai.yaml` (UI config). ・`SKILL.md` opens with frontmatter containing `name` and `description`. A good description states clearly when the skill triggers and what its boundaries are. ・Codex scans several locations in priority order: the repo's `.agents/skills`, `$REPO_ROOT/.agents/skills`, your personal `$HOME/.agents/skills`, the admin path `/etc/codex/skills`, and OpenAI's bundled built-in skills. ・Progressive disclosure keeps only names, descriptions, and paths in the initial context (capped around 8,000 characters). The full body loads on invocation, so many installed skills won't bloat the prompt. 🛠️ Practical Usage ・The easiest way to author one is the built-in `$skill-creator`. It walks you through what the skill does, when it triggers, and whether to bundle scripts (instruction-only is the default). ・Invoke explicitly with `/skills` in the CLI/IDE, or mention a skill by name like `$skill-name`. Codex also selects skills implicitly when your task matches the description. ・To forbid implicit selection for a skill, set `policy.allow_implicit_invocation` to `false` in `agents/openai.yaml`. ・To disable a skill without deleting it, add a `[[skills.config]]` entry with its `path` and `enabled = false` in `~/.codex/config.toml`. 💡 Use Cases Encode error-prone, brittle routines such as release procedures, how to run the E2E suite, or the correct way to use an internal library. Anyone on the team then gets consistent results from Codex. Put shared workflows under the repo and personal habits under `$HOME/.agents/skills` for a clean split. ⚠️ Caveats ・Duplicate skill names across locations are not merged; both appear in the selector, so keep names unique. ・A vague description breaks implicit matching. Front-load key use cases and be concrete. ・With many skills installed, descriptions may be shortened to save context. If changes don't show up, restart Codex. #OpenAICodex# #AgentSkills#

Write agent skills in Notion, then install them into your coding agent with the skills CLI. No Git repository required. $ 𝚗𝚙𝚡 𝚜𝚔𝚒𝚕𝚕𝚜 𝚊𝚍𝚍 <𝚗𝚘𝚝𝚒𝚘𝚗-𝚞𝚛𝚕>

Introducing Agent Plugins, an open standard for extending agents. Supports Agent Skills and MCP, with more to come. Built in collaboration with: @awsdevelopers, @code, @cursor_ai, @github, and @openaidevs.

Building agent skills from scratch is a grind. We've been there. So we built the fix — and open-sourced all of it under MIT. PDF, Excel, PPTX, DOCX, and more. Use it, fork it, PR it. Intelligence with everyone. Github Repo → Try online →

Cool Hermes agent use case to unlock productivity: Nous Research has a complete catalog of Hermes agentic skills. Instead of scrolling through them all manually, just tell your Hermes agent to do it for you - based on what it thinks would be helpful to you. Prompt: "Based on my existing workflows, scan [ and identify 5-10 agent skills that could genuinely change how I work. Summarise your top picks, and deliver it in a report so I can then manually approve what you can install." Once installed, you can then have your agent modify the skills to your exact needs. Low-hanging fruit, but it works so well.

MCP meets agent skills MCP already gave agents a standard way to connect to tools, resources, and external systems. Now it also defines a standard way to discover and load Agent Skills directly from MCP servers. The flow is simple: → connect to MCP server → discover available skills → inspect skill metadata → load the relevant 𝗦𝗞𝗜𝗟𝗟.𝗺𝗱 only when needed Under the hood, Skills are served through MCP’s existing Resources primitive. That means 𝗦𝗞𝗜𝗟𝗟.𝗺𝗱, references, scripts, examples, and other supporting files are exposed as resources that the client can read on demand. This is especially useful for context window management. Instead of loading every workflow instruction upfront, the agent can first discover what skills are available and pull in only the one required for the current task. A useful mental model is: tools = what the agent can do resources = what the agent can access skills = how the agent should perform a reusable workflow Previously, that workflow knowledge often lived separately in docs, repos, prompt files, or custom integrations. Now the MCP server can expose the capability and the playbook for using it together. So you get: → standardized skill discovery → on-demand context loading → cleaner distribution and versioning → reusable workflows that travel with the server MCP was already the connection layer. Skills now add a standardized way to ship reusable agent know-how on top of it. The illustration below visually summarizes everything that we discussed so far. Read more: Cheers! :)