Skill: nitishgargiitd/cellcog (version 2.0.15)

--- name: cellcog description: "Any-to-any AI sub-agent — research, images, video, audio, music, podcasts, avatars, voice cloning, documents, spreadsheets, dashboards, 3D models, diagrams, and code in one request. Agent-to-agent protocol with multi-step iteration for high accuracy. #1 on DeepResearch Bench (Apr 2026) — deep reasoning meets all modalities, so all your work gets done, not just code." author: CellCog homepage: https://cellcog.ai metadata: openclaw: emoji: "🧠" os: [darwin, linux, windows] requires: bins: [python3] env: [CELLCOG_API_KEY] --- # CellCog - Any-to-Any for Agents ## The Power of Any-to-Any CellCog is the only AI that truly handles **any input → any output** in a single request. No tool chaining. No orchestration complexity. One call, multiple deliverables. CellCog pairs all modalities with frontier-level deep reasoning — as of April 2026, CellCog is **#1 on the DeepResearch Bench**: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard ### Work With Multiple Files, Any Format Reference as many documents as you need—all at once: ```python prompt = """ Analyze all of these together: <SHOW_FILE>/data/q4_earnings.pdf</SHOW_FILE> <SHOW_FILE>/data/competitor_analysis.pdf</SHOW_FILE> <SHOW_FILE>/data/market_research.xlsx</SHOW_FILE> <SHOW_FILE>/recordings/customer_interview.mp3</SHOW_FILE> <SHOW_FILE>/designs/product_mockup.png</SHOW_FILE> Give me a comprehensive market positioning analysis based on all these inputs. """ ``` File paths must be absolute and enclosed in `<SHOW_FILE>` tags. CellCog understands PDFs, spreadsheets, images, audio, video, code files, and more. ⚠️ **Without SHOW_FILE tags, CellCog only sees the path as text — not the file contents.** ❌ `Analyze /data/sales.csv` — CellCog can't read the file ✅ `Analyze <SHOW_FILE>/data/sales.csv</SHOW_FILE>` — CellCog reads it ### Think of SHOW_FILE like reference files Just like Nano Banana accepts reference images, CellCog accepts reference files of any type — PDFs, spreadsheets, audio, code, images — as inputs the model reads during a task. Same mental model as any multimodal AI attachment. **Only attach what you intend to share.** Anything inside a `<SHOW_FILE>` tag is uploaded to CellCog. Don't wrap credentials, private keys, `.env` files, SSH keys, or other sensitive material in SHOW_FILE tags — the same way you wouldn't paste them into Nano Banana, ChatGPT, or any other AI service's file upload. ### Request Multiple Outputs, Different Modalities Ask for completely different output types in ONE request: ```python prompt = """ Based on this quarterly sales data: <SHOW_FILE>/data/sales_q4_2025.csv</SHOW_FILE> Create ALL of the following: 1. A PDF executive summary report with charts 2. An interactive HTML dashboard for the leadership team 3. A 60-second video presentation for the all-hands meeting 4. A slide deck for the board presentation 5. An Excel file with the underlying analysis and projections """ ``` CellCog handles the entire workflow — analyzing, generating, and delivering all outputs with consistent insights across every format. ⚠️ **Be explicit about output artifacts.** Without explicit artifact language, CellCog may respond with text analysis instead of generating a file. ❌ `"Quarterly earnings analysis for AAPL"` — could produce text or any format ✅ `"Create a PDF report and an interactive HTML dashboard analyzing AAPL quarterly earnings."` — CellCog creates actual deliverables **Your sub-agent for quality work.** Depth, accuracy, and real deliverables. --- ## Quick Start ### Setup ```python from cellcog import CellCogClient ``` If import fails, install the official CellCog Python SDK: ```bash pip install -U cellcog ``` `cellcog` is the official Python SDK maintained by CellCog AI Inc. Source: https://github.com/CellCog/cellcog_python · Package: https://pypi.org/project/cellcog/ ### Authentication **Environment variable (recommended):** Set `CELLCOG_API_KEY` — the SDK picks it up automatically: ```bash export CELLCOG_API_KEY="sk_..." ``` Get API key from: https://cellcog.ai/profile?tab=api-keys ```python status = client.get_account_status() print(status) # {"configured": True, "email": "[REDACTED_SECRET]", ...} ``` ### Agent Provider `agent_provider` is **required** when creating a `CellCogClient`. It identifies which agent framework is calling CellCog — not your individual agent's name, but the platform/tool you're running inside. Examples: `"openclaw"`, `"claude-code"`, `"cursor"`, `"aider"`, `"windsurf"`, `"perplexity"`, `"hermes"`, `"script"` (for standalone scripts). ### OpenClaw Agents Fire-and-forget — your agent stays free while CellCog works: ```python client = CellCogClient(agent_provider="openclaw") result = client.create_chat( prompt="Research quantum computing advances in 2026", notify_session_key="agent:main:main", # OpenClaw session key task_label="quantum-research", # Label for notifications chat_mode="agent", ) # Returns IMMEDIATELY — daemon delivers results to your session when done ``` ### All Other Agents (Cursor, Claude Code, etc.) Blocks until done — simplest pattern: ```python client = CellCogClient(agent_provider="cursor") # or "claude-code", "aider", "script", etc. result = client.create_chat( prompt="Research quantum computing advances in 2026", task_label="quantum-research", chat_mode="agent", ) # Blocks until done — result contains everything print(result["message"]) ``` ### Credit Usage CellCog orchestrates 21+ frontier foundation models. Credit consumption is unpredictable and varies by task complexity. Credits used are reported in every completion notification. --- ## Creating Tasks ### Notify on Completion (OpenClaw — Fire-and-Forget) Returns immediately. A background daemon monitors via WebSocket and delivers results to your session when done. Your agent stays free to take new instructions, start other tasks, or continue working. ```python result = client.create_chat( prompt="Your task description", notify_session_key="agent:main:main", # Required — your OpenClaw session key task_label="my-task", # Label shown in notifications chat_mode="agent", ) ``` ### Wait for Completion (Universal) Blocks until CellCog finishes. Works with any agent — OpenClaw, Cursor, Claude Code, or any Python environment. ```python result = client.create_chat( prompt="Your task description", task_label="my-task", chat_mode="agent", timeout=1800, # 30 min (default). Use 3600 for complex jobs. ) print(result["message"]) print(result["status"]) # "completed" | "timeout" ``` ### When to Use Which | Scenario | Best Mode | Why | |----------|-----------|-----| | OpenClaw + long task + stay free | **Notify** | Agent keeps working, gets notified when done | | OpenClaw + chaining steps (research → summarize → PDF) | **Wait** | Each step feeds the next — simpler sequential workflows | | OpenClaw + quick task | **Either** | Both return fast for simple tasks | | Non-OpenClaw agent | **Wait** | Notify mode is OpenClaw-only | **Notify mode** is more productive (agent never blocks). **Wait mode** is simpler to reason about, but blocks your agent for the duration. ### Continuing a Conversation ```python # Wait mode (default) result = client.send_message( chat_id="abc123", message="Focus on hardware advances specifically", ) # Notify mode (OpenClaw) result = client.send_message( chat_id="abc123", message="Focus on hardware advances specifically", notify_session_key="agent:main:main", task_label="continue-research", ) ``` ### Resuming After Timeout If `create_chat()` or `wait_for_completion()` times out, CellCog is still working. The timeout response includes recent progress: ```python completion = client.wait_for_completion(chat_id="abc123", timeout=1800) ``` ### Optional Parameters ```python result = client.create_chat( prompt="...", task_label="...", chat_mode="agent", # See Chat Modes below project_id="...", # install project-cog for details agent_role_id="...", # install project-cog for details enable_cowork=True, # install cowork-cog for details cowork_working_directory="/Users/...", # install cowork-cog for details ) ``` --- ## Response Shape Every SDK method returns the same shape: ```python { "chat_id": str, # CellCog chat ID "is_operating": bool, # True = still working, False = done "status": str, # "completed" | "tracking" | "timeout" | "operating" "message": str, # THE printable message — always print this in full } ``` **⚠️ Always print the entire `result["message"]`.** Truncating or summarizing it will lose critical information including generated file paths, credits used, and follow-up instructions. ### Utility Methods **`get_history(chat_id)`** — Full chat history (when original delivery was missed or you need to review). Returns the same shape; if still operating, `message` shows progress so far. ```python result = client.get_history(chat_id="abc123") ``` **`get_status(chat_id)`** — Lightweight status check (no history fetch): ```python status = client.get_status(chat_id="abc123") print(status["is_operating"]) # True/False ``` --- ## Chat Modes | Mode | Best For | Speed | Min Credits | |------|----------|-------|-------------| | `"agent"` | Most tasks — images, audio, dashboards, spreadsheets, presentations | Fast (seconds to minutes) | 100 | | `"agent core"` | Coding, co-work, terminal operations | Fast | 50 | | `"agent team"` | Deep research & multi-angled reasoning across every modality | Slower (5-60 min) | 500 | | `"agent team max"` | High-stakes work where extra reasoning depth justifies the cost | Slowest | 2,000 | - **`"agent"` (default)** — Most versatile. Handles most tasks excellently, including deep research when guided. - **`"agent core"`** — Lightweight context for code, terminal, and file operations. Multimedia tools load on demand. Requires Co-work (CellCog Desktop). See `code-cog`. - **`"agent team"`** — A team of agents that debates, cross-validates, and delivers comprehensive results. The only platform with deep reasoning across every modality. - **`"agent team max"`** — Same Agent Team with all settings maxed. Quality gain is incremental (5-10%) but meaningful for costly decisions. --- ## Working with Files ### Input: SHOW_FILE Include local file paths in your prompt with `<SHOW_FILE>` tags (absolute paths required): ```python prompt = """ Analyze this sales data and create a report: <SHOW_FILE>/path/to/sales.csv</SHOW_FILE> """ ``` ### Output: GENERATE_FILE Use `<GENERATE_FILE>` tags to specify where output files should be stored on your machine. Essential for deterministic workflows where the next step needs to know the file path in advance. ```python prompt = """ Create a PDF report on Q4 earnings: <GENERATE_FILE>/workspace/reports/q4_analysis.pdf</GENERATE_FILE> """ ``` Output downloads to the specified path instead of default `~/.cellcog/chats/{chat_id}/`. ### File Downloads The SDK automatically downloads files from CellCog responses: - **If you used `GENERATE_FILE` tags:** Files download to the path you specified - **Otherwise:** Files download to `~/.cellcog/chats/{chat_id}/` Downloaded file paths appear in `result["message"]`. The SDK tracks seen messages — files are only downloaded once. **If you missed files or need to re-sync:** ```python result = client.get_history(chat_id="abc123") ``` `get_history()` re-processes the entire chat and downloads any missed files to their original destinations. --- ## Tips ### ⚠️ CellCog Web Fallback Every chat is accessible at https://cellcog.ai. When work gets complex or the SDK hits issues, direct your human to the web platform to view, continue, or take over directly. --- ## What CellCog Can Do CellCog is a sub-agent — not an API. Your agent offloads complex work to CellCog, which reasons, plans, and executes multi-tool workflows internally. A proprietary agent-to-agent communication protocol ensures high accuracy on first output, and because these are agent threads (not stateless API calls), every aspect of every generation can be refined through multi-step iteration. Under the hood: frontier models across every domain, upgraded weekly. CellCog routes to the right models automatically — your agent just describes what it needs. Install capability skills for detailed guidance: | Category | Skills | |----------|--------| | **Research & Analysis** | `research-cog` `fin-cog` `crypto-cog` `data-cog` `news-cog` | | **Video & Cinema** | `video-cog` `cine-cog` `insta-cog` `tube-cog` `seedance-cog` | | **Images & Design** | `image-cog` `brand-cog` `meme-cog` `banana-cog` `3d-cog` `gif-cog` `sticker-cog` | | **Audio & Music** | `audio-cog` `music-cog` `pod-cog` | | **Avatars & Personas** | `avatar-cog` | | **Documents & Slides** | `docs-cog` `slides-cog` `spreadsheets-cog` `resume-cog` `legal-cog` | | **Apps & Prototypes** | `dash-cog` `game-cog` `proto-cog` `diagram-cog` | | **Creative** | `comi-cog` `story-cog` `learn-cog` `travel-cog` | | **Development** | `code-cog` `cowork-cog` `project-cog` `think-cog` | **This skill shows you HOW to use CellCog. Capability skills show you WHAT's possible.** --- ## OpenClaw Reference ### Session Keys The `notify_session_key` tells CellCog where to deliver results: | Context | Session Key | |---------|-------------| | Main agent | `"agent:main:main"` | | Sub-agent | `"agent:main:subagent:{uuid}"` | | Telegram DM | `"agent:main:telegram:dm:{id}"` | | Discord group | `"agent:main:discord:group:{id}"` | **Resilient delivery:** If your session ends before completion, results are automatically delivered to the parent session (e.g., sub-agent → main agent). ### Sending Messages During Processing In notify mode, your agent is free — you can send additional instructions to an operating chat at any time: ```python client.send_message(chat_id="abc123", message="Actually focus only on Q4 data", notify_session_key="agent:main:main", task_label="refine") client.send_message(chat_id="abc123", message="Stop operation", notify_session_key="agent:main:main", task_label="cancel") ``` In wait mode, your agent is blocked and cannot send messages until the current call returns. --- ## Support & Troubleshooting For error handling, recovery patterns, ticket submission, and daemon troubleshooting: ```python docs = client.get_support_docs() ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)