「eas」の検索結果

Multi-dimensional lead qualification scoring. Evaluates leads against BANT criteria, firmographic fit, behavioral signals, and intent indicators. Outputs qualified/disqualified verdict with detailed reasoning.

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Weekly churn risk detection across active client accounts. Scans for signals like decreased engagement, missed meetings, delayed payments, and competitor mentions. Scores risk 1-10 and outputs prioritized alert list.

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, reconciles, and reviews an end-to-end Drydock program for safely staging agent runtimes: immutable worktree provenance, disposable VM containment, typed effect brokerage, global body accounting, resurrection, subscription-capacity economics, context compaction, operator control, evidence, and promotion. Use when several Drydock contracts must compose into one architecture, implementation hypertree, diagram set, or release plan. NOT for launching Port Daddy, running an untrusted subject, replacing focused containment/resurrection/capacity audits, or treating a design packet as runtime 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。

Use for sensemaking, frame revision, anomaly handling, questioning strategy, and ambiguity diagnosis with the Data/Frame model before action is chosen. NOT for deterministic classification, settled execution planning, or situations where the frame is already stable.

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。

Chooses and designs developer surfaces for an agentic product: SDK, CLI, MCP, GUI, API, webhooks, and pd tube-style listener/sender workflows. Use when deciding why a user belongs in an SDK versus CLI/MCP/GUI, planning Python SDK parity, or making agent invocation easy across languages. NOT for implementing the full SDK, generic API docs, or UI visual design.

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。

### 1. The Fault/Error/Failure Hierarchy NOT for unrelated tasks outside this domain.

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。

Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".

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。

Implement AI chatbot analytics and conversation monitoring. Use when adding conversation metrics, tracking AI usage, measuring user engagement with chat, or building conversation dashboards. Activates for AI analytics, token tracking, conversation categorization, and chat performance.

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。

Conduct Critical Decision Method (CDM) interviews to elicit expert reasoning strategies (L3 knowledge) from practitioners about specific challenging past incidents. Use when capturing expertise for skill development, preserving knowledge before practitioner departure, building post-incident learning artifacts, or investigating decisions in complex sociotechnical systems. Outputs structured incident reconstructions with cues, decisions, alternatives considered, and counterfactual analysis suitable for distillation into skills or training material.

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。

Blue-green and canary deployment orchestrator with traffic shifting and automated rollback. Activate on: blue-green deployment, canary release, rolling deployment, traffic shifting, rollback automation, progressive delivery, Argo Rollouts, Flagger. NOT for: K8s manifest generation (use kubernetes-manifest-generator), CI/CD pipeline setup (use github-actions-pipeline-builder), monitoring (use monitoring-stack-deployer).

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Track the full Toulmin epistemic ancestry of every claim produced by a multi-agent swarm. Each agent output is tagged with an argument chain (claim / data / warrant / relationship) derived from SwarmTracer spans and LineageEdge records, so operators can zoom into any sub-claim and see precisely which agents asserted it, what triggered each assertion, and how contradictions propagated or were synthesised away. The SwarmTracer pattern — wrapping the SwarmAgentExecutor to capture spans, record messages, and build a lineage graph — is the concrete runtime substrate. Argumentative lineage lifts that graph into the Toulmin vocabulary so reasoning provenance is both machine-queryable and human-interpretable.

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Audits an Articles of Agreement contract — the daemon-witnessed agreement every official Port Daddy agent signs covering registration, transcript reporting, tool-use gating, file claims, parley conduct, budget limits, and operator control — against the enforcement-beats-hope bar: a clause counts as safe only when it resolves to a concrete, daemon-observable mechanism with a defined denial shape, never a promise the agent might honor. Use when drafting a new agent's Articles, reviewing a compliance-level (C0-C6) claim, deciding whether a clause is genuinely enforced or merely documented, or auditing whether an agent's claimed identity can be trusted. NOT for securing the inbound event-to-spawn path (fleet-event-spawn-trust), designing the relay's transport PKI and signing keys (pd-relay-zero-trust), or tracking an agent's identity and reputation across its whole lifetime after it signs (agent-identity-continuity-reputation).

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Design an agent's handling of obligations, prohibitions, permissions, and conflicts among norms and goals. Use when an autonomous agent must decide which norms apply and what to do when they conflict. NOT for simple fixed policy checks, ordinary BDI commitment design, or organizational metaphor.

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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).

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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).

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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。

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。

Set up @changesets/cli for semantic versioning, CHANGELOG generation, and npm publishing in a monorepo or single package. Covers init, config, contributor workflow, GitHub Actions release automation, and npm provenance.

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。