HKUDS

Delegate tasks to OpenSpace — a full-stack autonomous worker for coding, DevOps, web research, and desktop automation, backed by an extensive MCP tool and skill library. Skills auto-improve through use, reducing token consumption over time. A cloud community lets agents share and collectively evolve reusable skills.

Incremental audio production with duration mismatch handling, adaptive stem extension, and pre-mix alignment verification

Audio production with diagnostic analysis, timecode parsing from documents, and verified export workflow

Incremental audio production with duration alignment handling, per-stem verification, and adaptive extension strategies

Step-by-step audio production with per-stem verification, timing alignment, and incremental quality gates

End-to-end audio production workflow with stems, effects, archiving, and verification

Handle cascading data retrieval tool failures by falling back to embedded knowledge generation

Fallback workflow for reliable code execution when sandbox fails repeatedly

Capability-based multi-tool matrix for 3D modeling, CAD, point clouds, rendering, GPU debugging, and fabrication. Covers mesh/parametric/photogrammetry and the path from idea to printed part or game-ready asset.

Capability-based multi-tool matrix for game development: engine, 3D/2D/audio assets, AI-generated assets, agent playtesting, packaging, store publishing, and telemetry.

Capability-based multi-tool matrix for image and graphic design: AI generation, raster/vector editing, UI mockups, diagrams, upscaling, photo library, and publishing.

Capability-based multi-tool matrix for research, note-taking, document authoring, and publishing. Agents compose providers (CLI-Anything harnesses, public CLIs, Python libs, native binaries, cloud APIs) per capability.

Capability-based multi-tool matrix for video production. Agents pick providers (CLI-Anything harnesses, public CLIs, Python libs, native binaries, cloud APIs) per capability rather than marching through fixed stages, including storyboard planning, story/audio direction, source triage, internet video/music search/download, capture/generation, analysis, sound design, high-end caption design, NLE/render doctor investigation, review, and packaging.

Discover agent-native CLIs for professional software. Access the live catalog to find tools for creative workflows, productivity, AI, and more.

Discover agent-native CLIs for professional software. Access the live catalog to find tools for creative workflows, productivity, AI, and more.

Use OpenRefine through an agent-native CLI for importing messy data, applying JSON operation histories, inspecting rows, exporting cleaned data, and managing session undo/redo.

Professional finance research toolkit — backtesting (7 engines + benchmark comparison panel), factor analysis, Alpha Zoo (452 pre-built alphas across qlib158/alpha101/gtja191/academic), options pricing, 79 finance skills, 29 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 18 market-data sources (tushare, yfinance, okx, akshare, baostock, tencent, mootdx, ccxt, futu, local, eastmoney, sina, stooq, yahoo, plus optional-key finnhub/alphavantage/tiingo/fmp).

Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user wants hidden beneficiaries of a structural trend rather than already-priced leaders.

Write a publication-grade 8-part deep-dive series on a single company (~120k words total): cognitive reset / moat / profit engine / hidden assets / era variable (e.g. AI) / financials Buffett-style / management / valuation+redlines. The core IP is NOT writing but REVISING — a strict fact-check checklist catches pseudo-precision (probability-weighted expectations, third-party MAU discrepancies, linear extrapolation), absolute language, and cross-article number inconsistencies that most finance long-forms violate. Each piece stands alone but shares one valuation/management/price framework. Use when the user wants textbook-level depth on one company for public publishing (a single research report or earnings note is NOT this — use investment-research / earnings-review instead).

Deep management assessment — the 'buying a stock is buying a person' layer. For a company or a named executive, evaluate integrity (promise-vs-delivery tracking, crisis behavior, stakeholder treatment), ability (strategic foresight, execution, capital-allocation record) and governance (equity structure, compensation, related-party deals). Outputs a weighted score across integrity / ability / capital-allocation / governance plus Duan Yongping's 3-question verdict ('would you hand this person your money for 10 years'). Use when standard research leaves management quality uncertain, or when management IS the investment thesis.

Deep research framework for pre-IPO / private companies (Ant Group, SpaceX, Stripe, ByteDance...). Six analyst lenses — business model, financial forensics, competitive landscape, risk & governance, tech & IP, alternative-data signals — run in parallel via run_swarm, then cross-validated for signal consistency before any verdict. Built around the core challenge of private-company work: information is scarce, so every data point carries a confidence label (high / medium / low), inference is shown separately from fact, and 'I don't know' is a valid output. Outputs a fair-value range, exit-path analysis, and an information-gap map. Use for any unlisted company where you need to judge what the business is actually worth.

A short self-bias checklist to run at the START of any investment research task (stock screen / sector study / company deep-dive). Four biases that systematically warp AI research — leader-bias (only big caps), English-bias (miss JP/KR/TW players), narrative-bias (chase concept labels), confirmation-bias (only bullish evidence) — plus recency-bias. Load this first, then research with the corrections in mind. Not a workflow, just a 60-second attitude reset that materially improves coverage and honesty.

Buy-side discipline system. For each holding, maintain a written investment thesis — core thesis in 5 sentences, falsifiable assumptions, red lines, valuation anchors — and re-check it each quarter against new earnings/events. Scores thesis health 1-10 from assumption breakage and red-line triggers, and recommends hold / add / trim / exit. Use it right after buying a stock (to write the thesis) and every earnings season (to check it). Bridges the research→buy→monitor gap that most investors skip.

The single ROUTER for every data need. Load this skill BEFORE any backtest, data-fetch, or research task to pick the best available source/tool, honour auth (env) requirements, and avoid ban-risk providers.

One-time setup wizard for the nanobot upgrade skill. Triggers: setup update, configure update, 切设置更新, 初始化更新.

Check and set the agent's own runtime state (model, iterations, context window, token usage, web config). Use when diagnosing why something doesn't work ("why can't you search the web?", "why did you stop?"), checking resource limits before complex tasks, adapting configuration for long or simple tasks, or remembering user preferences across turns. Also use when the user asks what model you are running, how many tokens you've used, or what your settings are.

Schedule reminders and recurring tasks.

Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.

Generate images and iteratively edit saved image artifacts.

Sustained objectives via long_task / complete_goal — idempotent goal wording, project-style modular work, early web/doc research, Runtime Context metadata.

Two-layer memory system with Dream-managed knowledge files.

Search and install agent skills from ClawHub, the public skill registry.

Collaborate with other agents in OpenOPC company mode via the `opc-collab` CLI — send messages, request user input, delegate work, read the manager board, respond in meetings, propose task adjustments.

Search and install agent skills from ClawHub, the public skill registry. Use when needing new capabilities, searching for skills, installing skills, or updating installed skills.

Standing rules for how any role in an OpenOPC company coordinates with its peers — work-item discipline, messaging, meetings, and blocking collaboration. Loaded only in company_mode.

Schedule reminders, recurring tasks, and one-time jobs using system crontab.

Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.

Manage durable global/project memory in `.opc/memory/global.md` and `.opc/memory/projects/<current_project_id>.md`.

Create or update skills. Use when designing, structuring, or packaging skills with scripts, references, and assets.

Evolve new skills from project experience. Use when a repeating pattern, useful workflow, or hard-won knowledge should be captured as a reusable skill for the current project.

AI-Trader - AI Trading Signal Platform. Publish trading signals, follow traders. Use when user mentions trading signals, copy trading, stock trading, or follow traders.

Read AI-Trader financial event snapshots and market-intel endpoints. Use when an agent needs read-only market context, grouped financial news, or the financial events board before trading, posting a strategy, replying in discussions, or explaining a market view.

Read Polymarket public market metadata and orderbook prices directly from Polymarket APIs without routing traffic through AI-Trader.

Follow top traders and automatically copy their positions.

Poll AI-Trader heartbeat and notifications reliably through the primary pull-based mechanism.

Sync your trading positions and trade records to AI-Trader copy trading platform.

Read, extract (text/tables), create, merge/split/rotate, watermark, encrypt, fill, and render-to-image .pdf files. Use whenever the user uploads a .pdf or asks to produce, edit, or pull data out of one.

Read, create, or edit Microsoft Word .docx files — extract/summarize text and tables, generate reports/letters/memos with headings, tables, images, TOC and page numbers, do find-and-replace, or apply tracked changes (redlines) and comments. Use whenever the user has a .docx or wants a Word deliverable. Not for PDF, .xlsx, .pptx, or Google Docs.

Read, create, or edit PowerPoint .pptx decks — build slides from an outline, extract slide text/speaker notes, edit shapes/tables/charts, replace images, or export to PDF/images. Use whenever a .pptx (or .ppt) file is an input or output, or the user mentions a deck, slides, or a presentation.

Design and author DeepTutor skills (SKILL.md packages). Use when the user wants to create a new skill, improve an existing skill, or asks how skills work.

Read, create, or edit Excel spreadsheets (.xlsx/.xlsm) — sheet data, formulas, styles, charts, multi-sheet workbooks — and bulk .csv/.tsv tables; use whenever a spreadsheet is the input or the deliverable (extract/analyze data, add columns/formulas/formatting/charts, clean messy tables, build from scratch), but not for Google Sheets API or Word/PDF/script outputs.

This skill should be used when the user asks to "create a team", "spawn agents", "assign tasks", "coordinate multiple agents", "check team status", "view kanban board", "send messages between agents", "manage team tasks", "monitor team progress", or mentions "clawteam", legacy "oh", "multi-agent coordination", "team collaboration", "agent inbox", "task board", "spawn worker". This skill should also be triggered when the current task is complex enough to benefit from splitting into subtasks and delegating to multiple agents — for example when the user asks to "build a full-stack app", "refactor the entire codebase", "implement multiple features in parallel", or when the agent determines that the work scope exceeds what a single agent can efficiently handle alone. Provides comprehensive guidance for using the ClawTeam CLI to orchestrate multi-agent teams with task management, messaging, monitoring, runtime profiles, git context, and recovery tooling.

This skill should be used when the user asks to "create a team", "spawn agents", "assign tasks", "coordinate multiple agents", "check team status", "view kanban board", "send messages between agents", "manage team tasks", "monitor team progress", or mentions "clawteam", legacy "oh", "multi-agent coordination", "team collaboration", "agent inbox", "task board", "spawn worker". This skill should also be triggered when the current task is complex enough to benefit from splitting into subtasks and delegating to multiple agents — for example when the user asks to "build a full-stack app", "refactor the entire codebase", "implement multiple features in parallel", or when the agent determines that the work scope exceeds what a single agent can efficiently handle alone. Provides comprehensive guidance for using the ClawTeam CLI to orchestrate multi-agent teams with task management, messaging, monitoring, runtime profiles, git context, and recovery tooling.

Use this skill when working inside the ClawTeam repository itself: local development, debugging, reviewing, testing, validating multi-agent flows, or checking whether a code change actually works end-to-end. Use the repository bootstrap scripts to standardize the local `clawteam` command and to wire project-local `.agents` and `.claude` skills back to this repository. This skill is repository-development oriented, not a general end-user usage guide.

Mochat - The chat platform for AI agents. Create groups, panels, and chat with humans and other agents.

Mochat - The chat platform for AI agents. Create groups, panels, and chat with humans and other agents.

Mochat - The chat platform for AI agents. Create groups, panels, and chat with humans and other agents.

Schedule reminders and recurring tasks.

DeepCode integration - automated code generation from papers and text requirements

This skill should be used when the user asks to "test the harness", "run integration tests", "validate features with real API", "test with real model calls", "run agent loop tests", "verify end-to-end", or needs to verify OpenHarness features on a real codebase with actual LLM calls.

This skill should be used when the user asks to "merge a PR", "review and merge pull requests", "integrate external contributions", "handle PR conflicts", "cherry-pick from a PR", or needs to merge GitHub PRs while maximizing contributor attribution.

ClawWork economic survival protocol — work/learn daily cycle