Agent Network: your local agent team
Astra and 12 specialists in a local Codex system. Assign work through a map or controller chat, collect result files and review the execution history.
Skills, prompts, templates and checklists for work. Choose a resource, share it with your agent or download the complete file.
Astra and 12 specialists in a local Codex system. Assign work through a map or controller chat, collect result files and review the execution history.
Give the archive to your agent and run a public CT demo. Includes a coordinating skill, the Slicer skill, the TotalSegmentator skill and CT-CHAT/Merlin templates.
Find request waterfalls, heavy imports and unnecessary renders. Vercel’s rules help prioritize changes worth measuring first.
Define a visual direction, typography and layout, then check them against the brief. Anthropic’s workflow connects design choices to the product.
Review slow SQL, schema design and row-level access. Supabase provides explanations and examples for PostgreSQL, including deployments outside Supabase.
Remotion’s official skill collection covers composition, preview and rendering, with dedicated guidance for timing, audio, captions and multiple scenes.
Start with the target table, work back to its sources and validate transformations against data. dbt Labs connects SQL, model dependencies and tests.
Connect entry points, valuable assets and trust boundaries to concrete abuse scenarios. OpenAI’s workflow separates observed controls from assumptions.
Review crawlability, canonical URLs, content and language variants. Connect each finding to observed evidence and a concrete fix.
Replace overloaded components with composable parts, explicit variants and shared state. Vercel illustrates component API design with concrete examples.
Inspect the rendered page before exercising a user journey. Anthropic includes Playwright examples and a helper for starting local servers.
Gather context, refine the document section by section and test it with a fresh reader. Anthropic’s method helps expose assumptions familiar to the authors.
Find answers in 3D Slicer source and documentation, prepare a script and, with a separately connected MCP server, inspect a real scene using a public sample.
The official skill guides an agent through MCP capability discovery, task, device and speed selection, then records the generated masks and run settings.
Compare files, uncover contradictions, and link conclusions to evidence.
Find conflicting rules and unnecessary requirements in SKILL.md and AGENTS.md.
Answer a work question using primary sources and make evidence gaps visible.
Extract jobs and recurring problems from interviews while keeping quotes and context.
Compare alternatives using explicit criteria and recommend a decision with revisit conditions.
Remove jargon and repetition while preserving the author’s voice, facts, and uncertainty.
Turn a source into a post, email, or script without inventing facts.
Extract decisions, owners, and deadlines from a transcript, separating commitments from ideas.
Define the user problem, first-version scope, and observable acceptance criteria.
Turn “it is broken” into clear reproduction steps, expected behavior, and evidence.
Find missing values, duplicates, mixed units, and ambiguous dates before analysis.
Review a specific diff for behavior bugs and regressions, with line references.
A task prompt with context, source material, scope, and a clear definition of done.
A concise starting point for project structure, real commands, and completion criteria.
Track which claim relies on which source, including dates and disagreements.
Define observable success, errors, empty states, and data preservation.
Define a hypothesis, metric, constraints, and decision rule before running a test.
Hand work to another person or agent with state, decisions, files, and a next step.
Check facts, calculations, and task fit before relying on an AI output.
Check a skill’s purpose, content, activation, and boundaries on realistic requests.
Separate original evidence from retellings and assess its relevance to the question.
Provide useful context and files while preserving structure, versions, and sensitive information.
Identify rules that cause unnecessary reading, repeated checks, or premature stops.
Walk through links, forms, mobile layouts, downloads, and failure states before publishing.
Check the environment, GPU, weights and chest CT encoding before running the model. An original planning template; CT-CHAT is a research model, not an agent skill.
Separate library setup, checkpoint downloads and model-generated text for one public sample. An original research template, not a Merlin agent skill.
Extract its folder into .agents/skills in your Codex project. Each skill folder contains SKILL.md. Select it in the interface; in CLI and IDE you can invoke it with $skill-name.
Each resource includes an example prompt. Supply your files, constraints, and expected result. Templates and checklists can be used directly in a chat.
Compare the output with your source material and adapt the instructions to your workflow. A skill guides the work; your agent provides file, browser, and other tool access.
Format and installation: OpenAI — Build skills ↗ · Checked September 6, 2026