npx agentspec-cli install 68f97fe7-a007-4855-bd09-6579d770e630 --file-type cursorrules→ .cursor/rules/
# FrameWorkers — Cursor rules Multi-agent stack: Flask backend + Assistant + Director; creative pipeline agents live under `agents/`. **Architecture and APIs belong in repo / package READMEs** — read them when needed; this file is only binding principles. ## Where things live - `dynamic-task-stack/` — Backend (`:5002`): Task Stack `/api/`, Assistant `/api/assistant/`, workspace under `Runtime/`. - `director_agent/`, `director_nostack/` — Orchestrators over HTTP only (no shared imports with `interface/`). - `agents/` — All agent framework + pipeline agents; `Assistant` uses `from agents import …` and `AGENT_REGISTRY`. - `inference/` — LLM / multimodal; **no imports from other top-level packages**. - `interface/` — Vue 3 + Vite; HTTP client `src/services/api.js`, polling `polling.js`. ## Environment (new shell sessions) - Many shells (and Cursor’s tool terminal) start **without** your login profile, so **`conda` may be missing** until you run **`source ~/.bashrc`** (or the file that registers Miniconda on your machine). Then **`conda`** should be on `PATH` (often `…/miniconda3/bin`). - **Always** run **`conda activate frameworkers`** **before** Python, tests, or other project commands (`pytest`, `python -m …`, backend/director runs, etc.). Use the **same** shell for the follow-up command. - If unsure which envs exist, run **`conda env list`** after the steps above; project work targets the **`frameworkers`** env (not necessarily `base`). ## Invariants (do not break) - `POST /api/assistant/execute`: `agent_id`, `task_id`, nested `execute_fields`. **`global_memory` is top-level on execution inputs**, not inside `execute_fields` / `InputBundleV2`; **`_memory_brief` in execute_fields is ignored.** - Post-success global memory summary is **strict** (failure → error response); no silent empty fallback. - LLM agent outputs: **JSON only** (no markdown fences); unknown → `""` / `[]`, not `null`; top-level `meta` matches `agents/common_schema.Meta` only (no extra keys such as `project_id`). - Asset / content ID conventions and evaluator layers: follow agent package READMEs and existing schemas. ## Principles (MUST) 1. **Doc-first** — Code changes include updates to that package’s `README.md`. Cross-package contracts (HTTP API, schemas, descriptors) → update **every** affected README. 2. **Read-before-write** — Read the relevant README(s), then the target file(s) and direct dependencies; reuse existing helpers, avoid duplicate patterns. 3. **Full-stack** — If you change backend routes or response shapes: sync `interface/src/services/api.js`, Vue consumers, and `director_agent/api_client.py` as needed. 4. **Cross-package consistency** — Registry, descriptors, shared models: search across packages and keep a single coherent contract. 5. **No silent LLM / JSON fallbacks** — Do not hide failures by re-parsing markdown, substring-recovering JSON, or retrying without JSON mode. Product-level fallbacks are allowed only if **logged** and **documented** in README. `LLMClient.chat_json`: see `inference/README.md`. 6. **Focused changes** — Edit only what the task requires; match local naming, style, and abstractions. ## When layout or entrypoints change Update **root `README.md` and/or the affected package READMEs** (and trim this file if any pointer here becomes wrong). Keep `.cursorrules` principle-oriented, not a second architecture doc.
.cursorrules – Custom Rule File for Modular AI Agent System
**Goal**: Design a Pythonic, GPU-accelerated, local-first AI research agent framework
by AgentHub Crawler
Cursor Rules — WindowConfigurator
Read `AGENTS.md` in this directory first. It contains the full working preferences, TDD rules, code conventions, architecture principles, documentation requirements, and skill registry. Do not start any task before reading it.
by AgentHub Crawler
multi-agent-reference-architecture — Cursor Rules
`multi-agent-reference-architecture` is the NVIDIA SA capstone reference implementation demonstrating production-ready multi-agent LLM orchestration using LangGraph with fully YAML-driven graph assembly. It is 100% feature-complete and serves as the primary ISV customization surface in the NVIDIA SA portfolio. This repo is what an NVIDIA Solutions Architect shows a customer or ISV partner as the "how to build on NIM" reference. `core/graph_builder.py` reads agent YAML configs and dynamically ass
by AgentHub Crawler
Sign in to leave a comment.