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RobotFlow-Labs
GitHub 创作者资料

RobotFlow-Labs

按仓库查看 1 个 GitHub 仓库中的 47 个已收集 skills。

已收集 skills
47
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1
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2026-04-01
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Skills 分布在哪些仓库

按已收集 skill 数展示主要仓库,并显示它们在该创作者目录中的占比和职业覆盖。

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仓库与代表性 skills

3d-game-design
软件开发工程师

Master orchestrator for 3D/2D game development — engine selection, architecture, asset pipelines, physics, multiplayer, and deployment. Routes to 10 Three.js sub-skills and 7 reference guides.

2026-04-01
anima-manifest-designer
软件开发工程师

ANIMA Manifest Designer — Designs, validates, and evolves the three core YAML standards that define the ANIMA ecosystem: anima_module.yaml, anima_stack.yaml, anima_program.yaml. This is the "npm package.json of robotics" — whoever defines this standard owns the ecosystem. Modes: MODULE (design module manifest), STACK (design stack manifest), PROGRAM (design program manifest), VALIDATE (check a manifest against the schema), PROFILE (define data profiles).

2026-04-01
anima-marketing
市场调研分析师与营销专员

Mass marketing automation for RobotFlow Labs projects. 8 modes: SUBMIT (awesome lists, GitHub Discussions), SOCIAL (X/Twitter, LinkedIn, Reddit drafts), HN (Hacker News Show HN), REDDIT (browser-post to subreddits), BLOG (Dev.to/Medium), HUNT (ProductHunt), SCAN (find communities), BLITZ (all at once). Use via /anima-marketing [MODE] [project-url-or-path]. Supports browser automation for X.com, Reddit, HN via native Computer Use (embedded) and MCP browser tools as fallback. GitHub submissions via gh CLI.

2026-04-01
anima-strategy
首席执行官

ANIMA company strategy war room. Use when discussing AIFLOW LABS goals, ANIMA roadmap, competitive positioning, fundraising, Shenzhen trip planning, module prioritization, hardware integration strategy, or any company-level decision making. Loads full brainstorming vault + ANIMA ecosystem memory + competitive intelligence. Modes: BRAINSTORM (open discussion), STATUS (where are we), PLAN (sprint planning), COMPETE (competitive analysis), FUND (fundraising prep), DECIDE (make + record decisions).

2026-04-01
anima-supervisor-agent
软件开发工程师

ANIMA Supervisor Agent — The safety-first, quality-enforcing agent that oversees all ANIMA module development, pipeline compilation, and deployment. Acts as the SDK's brain during development. Modes: BUILD (module development), VALIDATE (pipeline checking), SAFETY (supervisor design), INTEGRATE (hardware integration), DATA (flywheel pipeline). Use when building ANIMA modules, designing safety systems, validating pipelines, or working on the Intelligence Compiler SDK.

2026-04-01
anima-validator
市场调研分析师与营销专员

ANIMA Strategy Validator — Goes online to find pitfalls, validate claims, and stress-test the ANIMA strategy against what competitors are ACTUALLY building. Searches for real announcements, GitHub repos, funding news, product launches, and technical claims that could invalidate or strengthen our positioning. Modes: VALIDATE (check our claims), HUNT (find new competitors), DESTROY (build destruction playbook for specific competitor), MONITOR (track changes).

2026-04-01
auramaxx
软件开发工程师

AuraMaxx — local secrets manager, credential agent, and crypto wallet. TRIGGER when: user mentions secrets, credentials, passwords, API keys, tokens, wallets, sending/swapping/funding crypto, login details, credit card info, sharing secrets, or "auramaxx"/"aura" by name. Also trigger for: "log into my <service>", "what is my <service> password", "run this with my <secret>", injecting env vars from stored credentials, diary/logging entries, or wallet balance/transaction requests. DO NOT TRIGGER when: general env var discussion unrelated to secret storage, generic auth/login code implementation, or non-AuraMaxx wallet code. Provides: secret CRUD (list/get/set/inject/share/delete), human-approval auth flows, crypto wallet ops (send/swap/fund/balance), daily diary logging, and MCP tool integration. Prefer MCP tools when available; fall back to CLI.

2026-04-01
autoresearch-analyze
数据科学家

Analyze autoresearch experiment results. Reads results.tsv, git log, and current train.py to summarize findings, identify patterns, suggest next experiments. Use when the user asks "analyze results", "what worked", "summarize experiments", or "what should we try next".

2026-04-01
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