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Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

日本語の概要は準備中です。原文の説明を表示しています。

Design a normalized, machine-readable "work receipt" that answers what changed, why, by whom, and with what validation, for any AI-coding-agent task regardless of backend (Claude Code, Codex, Cursor, Aider, CI, or homegrown). Use when defining a receipt schema, normalizing heterogeneous agent logs into one evidence shape, gating PR merges on artifact-backed proof, or designing an OSS maintainer's fast-reject/fast-merge workflow. NOT for validating a DAG node's output against its own schema (use output-contract-enforcer), continuous runtime monitoring of a live coordination daemon (use runtime-verification-for-agents), or general Port Daddy claims/notes/salvage mechanics (use port-daddy-agent-skill).

日本語の概要は準備中です。原文の説明を表示しています。

Design or review a declared control-command profile and its verb/backend lifecycle matrix. Distinguish policy denial, unsupported capability, delivery acknowledgement, independently observed effect, and unknown outcome; bind authorization to principal, target, scope, current policy, expiry, and fencing epoch. NOT for claiming runtime correctness, proving an adapter from declarations, or approving a control-panel button without independent runtime evidence.

日本語の概要は準備中です。原文の説明を表示しています。

Verify every visual-evidence artifact (screenshot/GIF/recording) attached to a PR carries a provenance manifest binding it to real daemon-backed truth — daemon port, run id, transcript head hash, agent node id, commit, and an honest real/fixture/mock source label — and that operator-control-panel PRs cover the required set of proof states. Use when gating a PR's proof artifacts before merge, auditing whether a "LIVE" visual claim is backed by a real daemon run versus a reused or fixture-backed mock, or defining the required state-coverage set for a control-panel change. NOT for the capture technique itself — headless Playwright, `screencapture -x -l`, non-interruptive capture (use port-daddy-agent-skill's visual-evidence doctrine), designing the receipt body schema an artifact attaches to (use agent-work-receipt-designer), or the broader dogfood/Potemkin product-quality bar (use multi-agent-authoring-product-bar).

日本語の概要は準備中です。原文の説明を表示しています。

Android background task specialist for WorkManager, Foreground Services, Doze mode, and battery optimization. Activate on: WorkManager, background task Android, Foreground Service, Doze mode, battery optimization, periodic work, background sync Android, JobScheduler. NOT for: iOS background tasks (use swiftui-data-flow-expert), React Native background (use mobile-offline-sync-architect), UI architecture (use jetpack-compose-navigation-expert).

日本語の概要は準備中です。原文の説明を表示しています。

Design RL simulation sandboxes, trajectory datasets, QLoRA/LoRA adaptation plans, eval harnesses, and rollback unhooks for agents learning specific coding behaviors beyond the base model. Use when building or reviewing tool-use training loops, offline trajectories, reward functions, self-improvement harnesses, or Port Daddy agent behavior curricula. NOT for generic ML tutorials, full model training operations, or production deployment without eval gates and safety unhooks.

日本語の概要は準備中です。原文の説明を表示しています。

Introduce people to AI, agentic AI, and AI skills with appropriate pedagogy for any audience. Covers LLM analogies, progressive complexity paths, aha-moment demos, audience-tailored explanations, workshop design, and overcoming emotional barriers to AI adoption. Activate on 'explain AI', 'introduce AI', 'AI workshop', 'AI demo', 'teach AI', 'AI literacy', 'onboard to AI', 'AI evangelism', 'explain agentic', 'explain skills to', 'AI training session', 'AI onboarding'. NOT for teaching ML engineering (use ai-engineer), not for building AI features (use ai-engineer), not for prompt engineering (use prompt-engineer), not for building training data pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

Author a pull request an AI coding agent can actually get merged: a scoped, coherent diff; a Summary and Test Plan backed by real evidence; correct triage of required-and-blocking CI gates versus external/advisory checks (e.g. a Cloudflare Pages preview build); draft-while-WIP discipline; named fixup commits for real review findings; and a clean landing through a merge queue without force-pushing or bypassing branch protection. Use when opening, updating, triaging red CI on, or landing an agent-authored GitHub PR. NOT for this repo's internal release ceremony, actor embodiments, or contributor mirror-sync mechanics (use port-daddy-internal-dev), tracking backlog/issue lifecycle across a board (use agent-issue-tracker-workflow), or deciding what to build next / sequencing a roadmap (use legible-roadmap-with-sidequests).

日本語の概要は準備中です。原文の説明を表示しています。

Discipline an AI agent needs to work an issue/task tracker (Jira, Linear, GitHub Issues) as the shared source of truth: pull the right next item instead of inventing work, search before creating to avoid duplicates, write actionable items with checkable acceptance criteria, keep status transitions honest, and link work to items via branches/PRs/commits so progress is legible. Use when an agent picks up tracker work, files a new issue, updates status, or closes an item. NOT for authoring the PR itself (use agent-pr-authoring), roadmap-level planning and prioritization (use legible-roadmap-with-sidequests), or the mechanics of multiple agents coordinating on the same codebase (use multi-agent-coordination).

日本語の概要は準備中です。原文の説明を表示しています。

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

日本語の概要は準備中です。原文の説明を表示しています。

Design or audit an agent identity, credential, session, continuity, and reputation plan. Use when attributed outcomes must survive key rotation, restart, recovery, or review; when choosing Elo, TrueSkill, a contextual bandit, or an LLM judge; and when making Sybil, newcomer, calibration, collusion, and evidence limits explicit. Produces a supplied-plan consistency audit, never proof of observed delivery, cryptographic identity, or live enforcement. NOT for proving personhood, global Sybil resistance, or an unobserved delivery.

日本語の概要は準備中です。原文の説明を表示しています。

Designs and verifies fail-closed continuation of a living durable agent after its VM, process, provider session, backend, or context window disappears. Use for body leases, generation fencing, cold-birth failure, process GC, native resume, cross-harness rebodiment, resurrection capsules, capability translation, ambiguous-effect reconciliation, and capacity-aware handoff. NOT for reviving an intentionally retired identity, generic disaster recovery, copying credentials or raw transcripts, ordinary process restart, or claiming metaphysical continuity.

日本語の概要は準備中です。原文の説明を表示しています。

Design and scaffold new skills, subagents, and MCP surfaces. Use when choosing which primitive to create. NOT for invoking existing agents, generic coding help, or deployment work.

日本語の概要は準備中です。原文の説明を表示しています。

Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).

日本語の概要は準備中です。原文の説明を表示しています。

Designs and audits closed, ordered, replay-safe message protocols between already admitted agent bodies. Use when a multi-agent exchange needs explicit epochs, audiences, per-sender sequencing, gathers, terminal fences, acknowledgements, and deterministic replay. NOT for selecting or spawning agents, granting identity or authority, choosing a runtime topology, general human facilitation, wire transport implementation, or deciding whether evidence is true.

日本語の概要は準備中です。原文の説明を表示しています。

Designs and audits deterministic, authority-filtered partitions of trust-typed context for already admitted bodies or abstract continuation slots. Use when causal context, obligations, disclosure boundaries, vector-space identity, capacity, and omission proofs must survive partitioning. NOT for spawning or admitting agents, choosing worker count, retrieving arbitrary knowledge, writing successor prompts, or granting tools, leases, identity, or effect authority.

日本語の概要は準備中です。原文の説明を表示しています。

Research AI coding assistant products, audiences, user stories, social proof, hacks, and unmet needs for apps like Cursor, Claude Code, Codex, Warp Code, Devin, Windsurf, GitHub Copilot, Cline, Aider, and homegrown agent stacks. Use when designing or positioning agentic developer tools, Port Daddy Agent Harbor, swarm consoles, background coding agents, or competitive/product research for AI software engineering. NOT for implementing the coding agent runtime, generic market research, or UX mockups without developer workflow evidence.

日本語の概要は準備中です。原文の説明を表示しています。

Design and audit a pricing function for variable-cost agent labor. Choose among per-seat, metered, credits, hybrid, and outcome pricing; name buyer value and cost metrics separately; calculate a reproducible cost floor; require pre-commitment guardrails; and stress-test declared personas. Use for offline pricing-design evidence, not billing implementation or runtime control. NOT for production billing, payment collection, or runtime spend enforcement.

日本語の概要は準備中です。原文の説明を表示しています。

Design discovery, directories, and guilds for agent swarms at scale. Use when agents need to find each other on a shared surface: capability indices, yellow-pages directories, whois/phonebook routing, reputation-aware discovery, and guild structures that scope trust. Covers the canonical prior art (FIPA Directory Facilitator, Contract Net, Chord/DHT, DNS-SD/Consul, EigenTrust, Greif's merchant guilds) and the failure modes (read-poverty, Sybil reset, Goodhart on rank, directory staleness, cold-start). Triggers: "agents can't find each other", "who owns this file/skill", "route a question to the right agent", "capability registry", "agent reputation", "guild / federation of fleets". NOT for issuing identities, granting authority, or automatically assigning work from a directory entry.

日本語の概要は準備中です。原文の説明を表示しています。

Designs digital experiences for ADHD brains using neuroscience research and UX principles. Expert in reducing cognitive load, time blindness solutions, dopamine-driven engagement, and compassionate design patterns. Activate on 'ADHD design', 'cognitive load', 'accessibility', 'neurodivergent UX', 'time blindness', 'dopamine-driven', 'executive function'. NOT for general accessibility (WCAG only), neurotypical UX design, or simple UI styling without ADHD context.

日本語の概要は準備中です。原文の説明を表示しています。

Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

日本語の概要は準備中です。原文の説明を表示しています。

Safety-case design for always-on agents with episodic memory. Covers data hygiene, privacy and security risk surfaces, cost controls, scope, user dependency concerns, retention, incident containment, and responsible deployment evidence. It identifies hazards and asks for named controls, tests, owners, and residual uncertainty; it does not provide legal, clinical, youth-safety, or jurisdictional determinations. Activate on: "agent safety", "always-on agent privacy", "agent cost control", "persistent agent risks", "AI companion safety", "agent data hygiene", "runaway agent costs", "parasocial AI risk", "/always-on-agent-safety". NOT for: architecture design (use always-on-agent-architecture), input design (use always-on-agent-inputs), application brainstorming (use always-on-agent-applications), healthcare compliance specifically (use hipaa-compliance).

日本語の概要は準備中です。原文の説明を表示しています。

How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).

日本語の概要は準備中です。原文の説明を表示しています。

Architecture and systems design for agents that retain bounded state across sessions. Covers durable event history, owned state, derived memory, retrieval indexes, framework evaluation, recovery, deletion, and rebuild behavior. Uses constructed implementation examples and workload calibration rather than universal vendor, latency, similarity, retention, or cost claims. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-patterns).

日本語の概要は準備中です。原文の説明を表示しています。