Menü
Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user)
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
| name | atulya-local |
| description | Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user) |
You have persistent memory via the atulya-embed CLI. Proactively store learnings and recall context to provide better assistance.
Before using memory commands, verify Atulya is configured:
uvx atulya-embed daemon status
If this fails or shows "not configured", run the interactive setup:
uvx atulya-embed configure
This will prompt for an LLM provider and API key. After setup, the commands below will work.
Use memory retain to store what you learn:
uvx atulya-embed memory retain default "User prefers TypeScript with strict mode"
uvx atulya-embed memory retain default "Running tests requires NODE_ENV=test" --context procedures
uvx atulya-embed memory retain default "Build failed when using Node 18, works with Node 20" --context learnings
Use memory recall BEFORE starting tasks to get relevant context:
uvx atulya-embed memory recall default "user preferences for this project"
uvx atulya-embed memory recall default "what issues have we encountered before"
Use memory reflect to synthesize context:
uvx atulya-embed memory reflect default "How should I approach this task based on past experience?"
Always store after you learn something valuable:
Always recall before:
Disciplined development workflow for AI coding agents working in the Atulya monorepo — covers context gathering, branch hygiene, edit/lint/test cycle, API surface changes that require client regen, parallel vs sequential tool use, and the report-back template. Use at the start of any non-trivial Atulya coding task, or when the user asks how to "implement X", "port Y", "change the API", or "ship a feature" in this repo.
Run the Atulya repo through end-to-end system testing — toolchain check, lint, Python pytest, Rust cargo check, TypeScript build, control-plane typecheck, OpenAPI + client regen as a contract test, cross-client field smoke test, and legacy-leak audit. Use when validating a change before commit/PR, after pulling main, before a release, or when the user asks to "run all tests", "verify everything", "system sanity check", or "end-to-end test".
Design a meaningful git commit for the Atulya repo end-to-end — what commands to run to inspect the change, how to decide if it's one commit or several, how to write a message in this repo's `[type] ~ subject` convention with a structured body grouped by surface, and the exact `git add` / heredoc `git commit` invocations to use. Use whenever the user says "commit this", "git commit", "draft a commit message", "what should the commit be", or you've just finished a feature/fix and want to propose a commit before they ask.
Complete Atulya documentation for AI agents. Use this to learn about Atulya architecture, APIs, configuration, and best practices.
Atulya Cortex (TUI/WhatsApp), Hand tools, internet stack (SearXNG/Firecrawl), config, and what-if troubleshooting. Use for cortex wiring, web_search/web_extract, deliberation limits, or docker internet-search compose.
Review changed code against project standards. Checks for missing tests, dead code, type safety, lint issues, and coding conventions. Run after completing any implementation work.