{
  "$schema": "https://schemas.agentskills.io/discovery/0.2.0/schema.json",
  "skills": [
    {
      "name": "review-agent-architecture",
      "type": "archive",
      "description": "Review an agent architecture, design document, or proposed rollout against a vendor-neutral reference: the seven capabilities an agent platform provides, the two rules (policy enforced outside the model; the audit trail written where the agent cannot touch it), the four deterministic zones, the ten cross-cutting concerns, and twenty-five contested technology choices with verdicts. Produces findings ordered by severity, each naming the rule broken and the evidence behind it. Use whenever someone asks for a review or a second opinion on an AI agent design, an architecture decision record, a vendor proposal, or a plan to put agents into production, and whenever a design needs checking for boundary violations before it ships.",
      "url": "https://www.agenticarchitectureskills.com/.well-known/agent-skills/review-agent-architecture.zip",
      "digest": "sha256:ca7ef6c3c6cf7eead1dfff67fda38ba374bfebd14ca9e39d3fcdba1cef51ac8c"
    },
    {
      "name": "map-deterministic-boundaries",
      "type": "archive",
      "description": "Work out which actions an AI agent must never approve on its own, and design the deterministic control around each one. Covers the rule that models may take permission away, never grant it, and the four zones where a model may not approve at all (access and entitlements, movement of money, safety actuation, formal regulatory records), the identity and delegation chain that keeps every action traceable to the person who asked, and where enforcement belongs. Produces a zone register and the control for each entry. Use whenever designing authorization, payments, approvals, or record-keeping for agents, whenever deciding what an agent may do unsupervised, and whenever a guardrail or a prompt instruction is being proposed as a security control.",
      "url": "https://www.agenticarchitectureskills.com/.well-known/agent-skills/map-deterministic-boundaries.zip",
      "digest": "sha256:a769374ea35e38c15b2512a8fdb63ec5b5cc12aff0d0799449cc53324b297d88"
    },
    {
      "name": "assess-agent-readiness",
      "type": "archive",
      "description": "Score whether an organisation is ready to run a specific agent workload, and say how much autonomy that workload may safely have. Asks twenty-eight questions across seven areas (data, integration and tools, identity and registry, controls on actions, testing and operations, cost and value, people and operating model), scores yes 3, partly 1, no 0, and reports a profile, never a total. Reads the autonomy ceiling the profile supports (A1 up to A5 candidacy), applies two overrides (a Tier 3 workload needs controls at 3 for A3 or above; a session with untrusted input, sensitive data and external action is capped at A2), and lists what to fix before asking for more. Use whenever someone asks whether they are ready for AI agents, wants a readiness score or a gap list, is choosing an autonomy level for a workload, or is about to buy an agent platform before proving a workload.",
      "url": "https://www.agenticarchitectureskills.com/.well-known/agent-skills/assess-agent-readiness.zip",
      "digest": "sha256:ec56adb66103210481d50ed4294e2c4c180ba104d4415e24667cf45e363ff11e"
    },
    {
      "name": "choose-first-agent-use-cases",
      "type": "archive",
      "description": "Decide which AI agent use cases to fund first and which to refuse, using three admission gates (can the outcome be evaluated, does it decide inside a deterministic zone, is the grounding data ready) and a portfolio shape that keeps the first wave learnable. Includes worked department blueprints for the service desk, customer service and finance. Use whenever someone is choosing or prioritising agent use cases, building a pipeline or roadmap of AI workloads, writing an intake process, or arguing for a use case whose value is obvious but whose success cannot be measured.",
      "url": "https://www.agenticarchitectureskills.com/.well-known/agent-skills/choose-first-agent-use-cases.zip",
      "digest": "sha256:79054df15e17facc3167476de4b4813f4063cfecb0c0b79043b554c8f7dd705c"
    },
    {
      "name": "govern-agent-tool-access",
      "type": "archive",
      "description": "Design the layer between agents and the systems they act on: one gateway as the enforcement point, tools that wrap governed APIs rather than replace them, the Model Context Protocol as the tool contract, read-only defaults, safe action design with idempotency keys and receipts, and audit at action granularity. Use whenever connecting agents to enterprise systems, building or buying an MCP gateway, wrapping an API as a tool, reviewing what an agent may call, or deciding between an embedded vendor agent and an external agent through APIs.",
      "url": "https://www.agenticarchitectureskills.com/.well-known/agent-skills/govern-agent-tool-access.zip",
      "digest": "sha256:84fade1ff3a7f29d7c9bdf46a4f64d905b7c5a7b1f49b1ea375cf495bdf6a41a"
    },
    {
      "name": "design-agent-knowledge-pipeline",
      "type": "archive",
      "description": "Design how company information reaches an agent and what it may remember: parsing with provenance, chunking evaluated per corpus, versioned embeddings, indexes that carry permissions, retrieval that can refuse, citation by construction, memory writes through quarantine, and deletion that cascades to every derived copy. Includes the measured evidence on retrieval design. Use whenever building or reviewing retrieval or RAG for agents, designing agent memory, choosing chunking or embedding strategy, or answering how permissions and deletion apply to an index.",
      "url": "https://www.agenticarchitectureskills.com/.well-known/agent-skills/design-agent-knowledge-pipeline.zip",
      "digest": "sha256:b9dfa2e4c0684663644c98a017cd95db1fc225c25d26cb0a814d8285a58ae3af"
    },
    {
      "name": "design-agent-context",
      "type": "archive",
      "description": "Design what reaches an AI agent's context so it answers well: nine patterns for representing documents (several vectors per item rather than one), keeping keyword search fused in, deriving view taxonomies from the corpus, conditioning on the instruction at query time, spending the model on design rather than on reading everything, gating as a quarantine, diversifying inside a gated pool, and measuring against a matched control. Each pattern carries the measurement behind it. Use whenever designing or reviewing retrieval, RAG, chunking, embeddings, topic modelling, result selection or agent memory, whenever choosing how to index a corpus, or whenever someone proposes conditioning an index on the task.",
      "url": "https://www.agenticarchitectureskills.com/.well-known/agent-skills/design-agent-context.zip",
      "digest": "sha256:e6d9c9d9372a4b5905512f460fa6c5ce9dafbc6da55c0d22e1c3dd6703c0dce9"
    },
    {
      "name": "build-agent-evals-and-evidence",
      "type": "archive",
      "description": "Build the two things that let an agent's behaviour change safely: an evaluation suite owned by the people who know the work, and an evidence trail the agent cannot influence. Covers eval tasks drawn from real failures, judge calibration, promotion gated on counterexample survival with a demotion path, staged rollout, traces and unit economics, and the audit trail baseline a deployer owes. Use whenever setting up evals for an agent, deciding whether a change may ship, designing observability or audit for agents, or answering what evidence a regulator or auditor will expect.",
      "url": "https://www.agenticarchitectureskills.com/.well-known/agent-skills/build-agent-evals-and-evidence.zip",
      "digest": "sha256:da65ca61c4779a0afc2ccbc30fcf5d559fefdf97ef8939eb08cd044392a1100d"
    },
    {
      "name": "size-agent-supervision-and-cost",
      "type": "archive",
      "description": "Work out how much human supervision an agent portfolio actually needs and what it costs per resolved outcome. Covers supervision as a burst-rate capacity constraint across the whole portfolio rather than per workload, the oversight gate above A3, cost per run against cost per resolved outcome including supervision labour and wrong outcomes, and agent budgets. Use whenever building a business case for agents, sizing an oversight or approval team, answering how many agents one person can supervise, or reviewing an agent programme whose economics assume the supervision is free.",
      "url": "https://www.agenticarchitectureskills.com/.well-known/agent-skills/size-agent-supervision-and-cost.zip",
      "digest": "sha256:002199dec718241325e28abb84218c19a4d94dce6990a90fc5ded79373f4e59f"
    },
    {
      "name": "agentic-enterprise-architecture",
      "type": "archive",
      "description": "The cross-layer reference for running AI agents inside an enterprise: the seven capabilities an agent platform provides across fourteen layers, the two rules (policy enforced outside the model; the audit trail written where the agent cannot touch it), the four deterministic zones (models may take permission away, never grant it, and inside the zones may not approve at all), the delegation chain, enforcement outside the model, the data-to-memory pipeline, the learning flywheel, the autonomy contract (A0 to A5 by L0 to L3), the cross-cutting concern matrix, and a plain-words glossary. Use whenever a question concerns how an enterprise should architect, govern, secure or supervise agents, or when a term like A3, ID2, the audit trail, the oversight gate or a deterministic zone needs its definition.",
      "url": "https://www.agenticarchitectureskills.com/.well-known/agent-skills/agentic-enterprise-architecture.zip",
      "digest": "sha256:6220e751397040f9acfd0a2bb6a453ca2fe7bb552ccdd1f1919d5060e7c4bc3c"
    },
    {
      "name": "agentic-enterprise-layers",
      "type": "archive",
      "description": "One page for each of the fourteen layers of an enterprise estate that AI agents touch: infrastructure, data platform, integration fabric, systems of record, line of business and operational technology, intelligence and learning, agent platform, productivity, experience and channels, security and identity, governance and sovereignty, observability and cost, supervision, and agent data engineering. Each gives the target state, the mechanisms with dated numbers, the contested choices with verdicts, the cross-cutting concerns, and what the evidence does not support. Use when a question concerns one part of the estate, a specific protocol or standard, where a control is enforced, or what good looks like at that layer.",
      "url": "https://www.agenticarchitectureskills.com/.well-known/agent-skills/agentic-enterprise-layers.zip",
      "digest": "sha256:535c092ae6d861f7019c32a2fad3463087f00cb89d289232dc20ad03553960db"
    },
    {
      "name": "agentic-architecture-decisions",
      "type": "archive",
      "description": "Twenty-five technology choices for AI agent systems that are usually made by habit, each resolved with a verdict, the discriminator that decides your case, and what would change it: graph database against embedding retrieval, dedicated gateway against extending API management, single agent against multi-agent orchestration, guardrail products against deterministic gates, embedded vendor agents against external agents, fine-tuning against context engineering, and more. Use whenever someone asks should we use X or Y, cites a popular default, or needs to know what evidence settled a position and when.",
      "url": "https://www.agenticarchitectureskills.com/.well-known/agent-skills/agentic-architecture-decisions.zip",
      "digest": "sha256:c2b0dee1768b02c92132bcf7a5847e03bca0d0ee16d1ecd8905f2d85032ead70"
    }
  ]
}