70 skills · sorted by relevance
MATLAB R2026a robotics and autonomy workflow for ROS Toolbox, Robotics System Toolbox, Navigation Toolbox, UAV Toolbox, sensor fusion, trajectory planning, simulation, and reproducible robot experiments. Use for ROS/ROS 2 bags, robot models, path planning, SLAM, state estimation, and autonomy prototyping.
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Give the AI agent its own EVM wallet with admin-controlled policies the agent CANNOT bypass even under prompt injection. Encrypted keystore (AES-256-GCM, scrypt KDF), policy file the agent has no tool to write, deterministic policy gate on every signing operation, optional local HTTP dashboard. Triggers: agent wallet, give the agent a wallet, agent address, fund the agent, agent autonomy, policy gate, kill switch, agent permissions, bounded autonomy, ERC-4337 alternative, session-key alternative.
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Design motivation systems using Autonomy, Mastery, and Purpose (AMP) for products and teams. Use when the user mentions "intrinsic motivation", "gamification isnt working", "rewards arent working", "autonomy", "mastery", "purpose-driven", "my team is disengaged", or "how do I motivate people". Also trigger when designing onboarding progression, fixing broken gamification, or building team structures that sustain high performance. Covers why carrot-and-stick fails and how to build progress systems. For habit-forming product loops, see hooked-ux. For retention behavior design, see improve-retention.
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Diagnose motivation problems in a task using self-determination theory and redesign for autonomy, competence, and relatedness. Use when students are disengaged, resistant, or going through the motions.
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Generate scaffolds that gradually increase student choice, voice, and ownership within a learning task. Use when students depend heavily on teacher direction and need to develop autonomy.
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Applies modular monolith with enforced internal boundaries. Use when teams want service-level autonomy without distributed system overhead.
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Diagnose motivation problems in a task using self-determination theory and redesign for autonomy, competence, and relatedness. Use when students are disengaged, resistant, or going through the motions.
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Generate scaffolds that gradually increase student choice, voice, and ownership within a learning task. Use when students depend heavily on teacher direction and need to develop autonomy.
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Your team has high autonomy — and standups are vague, weekly meetings drift into status reports, and 1:1s produce nothing but "I'm fine." This playbook fixes all three layers at once. What's inside: • Daily Standup system — 3-field format, good sample, 4 COO principles to keep it honest • Weekly Meeting — strict 25-min agenda, Scrum Master rotation, 0–10 pulse check • 1:1 Framework — 9-dimension quantified pulse, 5-group deep question bank, bilateral feedback structure • Diagnostic layer — "stuck-at" detection: info / authority / collaboration / capability / direction • FAQ for 4 common failure modes (dead standups, status-report meetings, "I'm fine" 1:1s, scaling beyond 10 people) 🇨🇳 你的团队自由度很高——但 standup 越来越糊，例会变成汇报，1:1 问不出真话。这套三层 Motivation 操作手册解决的正是这件事：standup 是信息层，例会是对齐层，1:1 是个人层。三件事做好了，motivation 不是管出来的，是长出来的。 🇯🇵 チームの自律性は高いのに、スタンドアップは曖昧で、週次ミーティングは進捗報告になり、1on1では「大丈夫です」しか返ってこない——このプレイブックは、その3層すべてを同時に解決します。 🇰🇷 팀의 자율성은 높은데 스탠드업은 두루뭉술하고, 주간 회의는 진척 보고가 되어버리고, 1:1에서는 진심을 들을 수 없다면 — 이 플레이북이 세 레이어 모두를 동시에 해결합니다. Triggers: "团队 motivation" | "standup 没人写" | "例会变成汇报" | "1:1 问不出真话" | "高自由度团队管理" | "COO 如何管人" | "team motivation" | "standup best practices" | "1on1 framework" | "weekly meeting template" | "チームモチベーション" | "팀 동기부여" | "AI startup team ops" | "remote team alignment" | "engagement下降"
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Trust/autonomy 수치를 실제 기록으로 되짚는 검증법
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Choose multi-agent architectures using environment-first analysis, coordination pressure, commitment tuning, and knowledge levels. Use for autonomy design, protocol choice, and coordination failures. NOT for single-agent planning, centralized schedulers, or prompt-only swarms.
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Turns a structured-autonomy PR plan (plans/{feature}/plan.md) into copy-paste-ready implementation documentation with complete code for every step, saved as implementation.md. Use after a plan exists and before implementation, when each step needs fully specified changes.
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Read-only measurement of an agent's autonomy debt from its approvals log: how many times it asked the human for permission in a window, how many asks expired unanswered, and which classes they fall into (machine noise, login or 2FA, physical UAC, money). Use to find asks the agent should have decided itself and to cut needless interruptions. Triggers: /ask-debt, autonomy debt, how often do I ask, expired asks.
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Anchor your identity on the Autonomys Network so you can always be found — and always come back. Works with agents powered by Claude, GPT, Gemini, and Your agent always comes back. Anchor identity and memory on-chain so any new instance can resurrect from just an address — no local state, no single point of failure. Permanent identity and recovery on the Autonomys Network.
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Define when an agent may respond in shared chats with customers or partners and what it may send. Separate authorized routine replies from approval-only drafts while keeping internal information out of external messages.
Create or update a SOUL.md file that defines an AI partner’s behavior through a guided conversation. Clarify communication style, autonomy, honesty, and expectations, then review a concise English profile before saving it with setup_agent.
Experimental workflow for configuring an autonomous software delivery factory. Use when choosing feedback sources, schedules, worktree behavior, autonomy gates, or host automations.
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Structured code quality assessment with Conventional Comments format, scaled review depth, and soft-gating verdicts preserving user autonomy.
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Shared backlog conventions for Azure DevOps, GitHub, and Jira. Use for platform resolution, autonomy tiers, sanitization guards, and story quality, and for Azure DevOps pull request creation and build status or log retrieval.
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Prepare and rehearse a coming-out conversation, tuned to the specific person and the real risk — what to say, how to open, how to handle the likely reactions, and a safety-first plan if it could go badly. Use when someone says 'I want to come out to my parents/boss/friend', 'help me tell them I'm [gay/trans/bi/etc.]', 'rehearse this conversation with me', or is planning any identity disclosure. Produces an opener, a rehearsal against realistic reactions, and a safety plan. Safety and the user's autonomy come first — it never pushes anyone to come out.
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Help users master the art of proactive communication and alignment with their managers and founders to unlock autonomy, secure resources, and drive business impact.
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Track performance across sessions and reduce scaffolding as competence grows. Makes fading visible — the learner knows when scaffolds are removed and why. Use for sustained learning engagement where independence is the goal.
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Scores a coding task on Spread, Novelty, and Centrality (SNC 0-6) and maps the tier to autonomy, verification depth, review mode, reviewers, model tier. Use when sizing a fix or feature before planning, choosing fast vs deep review, or deciding whether HARD STOP approval is required.
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Sets up how you and your team build and work with AI agents, and produces an Agent Workflow Charter (agent lanes, plan-mode loop, autonomy ladder with promotion criteria, rules file, review policy, security rules, rollout plan). Use when you ask 'how should we work with Claude Code / Codex / Cursor', 'how many agents should I run', 'should the agent be autonomous', 'plan mode', 'CLAUDE.md', 'AI-native team', 'roll out AI to my team', or 'my agent keeps screwing up'. Draws on 110 Lenny's Podcast guests incl. Claire Vo, Boris Cherny and Aishwarya Naresh Reganti.
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