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Audit or design agent-facing product capabilities: action/context parity, discoverable contracts, safe mutations, verifiable outcomes, and recovery. Use for explicit agent-readiness reviews of apps, repos, CLIs, APIs, MCP/A2A integrations, or Agent Skills; not every general code or documentation task.

Use this Skill: https://skilld.dev/gh/tristanmanchester/agent-skills/agent-use

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referencesagent-native-architecture.md

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Agent-native architecture

This reference preserves the strongest ideas from the original Compound skills and generalizes them beyond a single app stack.

Core principles

  1. Action parity. Every important human action should have an agent-accessible path unless it is intentionally human-only. The path can be API, CLI, SDK, MCP/tool, file operation, or controlled UI automation. Parity is about outcomes, not copying every click.
  2. Context parity. Agents need the same domain objects, state, permissions, constraints, recent activity, and completion criteria a competent human operator sees.
  3. Shared workspace. Agents and humans should collaborate in durable, inspectable state: files, records, branches, drafts, tickets, tasks, or artifacts. Avoid hidden sandboxes unless isolation is the explicit product model.
  4. Primitives over workflows. Expose composable capabilities. Use workflow tools only when atomicity, safety, performance, or external orchestration makes primitive-by-primitive execution risky or wasteful.
  5. Dynamic context injection. Static system prompts are not enough. Runtime context should tell the agent what resources exist, what it may do, what state is current, and what success means now.
  6. Prompt-native behavior. Behavior that is inherently judgment-based should often be represented as prompts, policies, examples, and evals rather than hard-coded branching.
  7. Completion and recovery. Agents need explicit done states, progress, partial results, checkpoints, retries, and failure semantics.

The noun test

After mapping actions, map domain nouns. For each important object, ask:

Question Evidence to seek
Can the agent discover the object type? Docs, schema, examples, tool/resource lists
Can the agent identify a specific object? Stable IDs, URLs, search/list operations, file paths
Can the agent read enough context? Get/list/export operations, resource views, data exports
Can the agent act when appropriate? Create/update/delete/archive/import/export primitives
Can it verify the action? Mutation response, status endpoint, UI reflection, audit log
Can it recover? Undo, rollback, soft delete, idempotency, checkpoints

Capability map

Use this for app, API, CLI, and tool reviews.

Human workflow Human context Agent-readable context Agent action path Safety level Recovery path Status
Create project org selector, form validation org list, project schema, scopes POST /projects or tool project.create write archive/delete project planned
Delete production resource settings page, danger copy resource details, dependents, dry-run diff API/CLI/tool with approval token destructive soft restore/backups missing

Human-only exceptions

Do not flag these as parity failures by default: CAPTCHA, biometric prompts, MFA enrollment, OAuth consent screens, app-store permission prompts, legal acceptance, password entry, and other ceremonies designed to prove human presence or legal consent. Do flag the absence of an appropriate alternative, such as service accounts, scoped tokens, admin approval flows, or documented delegation.

Anti-patterns

Anti-pattern Signal Fix
Orphan feature UI action has no API/CLI/tool/SDK path Add an agent path or document the human-only boundary
Context starvation Agent sees a static prompt but not current state Inject workspace, permissions, objects, limits, recent activity
Sandbox isolation Agent outputs land where users cannot inspect/edit Use shared records/files/drafts or clear artifact links
Silent mutation Agent changes state but UI/logs do not reflect it Emit events, update shared store, expose audit trail
Workflow blob Tool encodes decisions and hidden side effects Split primitives or justify workflow atomicity/safety
Giant response Tool/API dumps unbounded data Add pagination, summaries, fields, cursors
Decision enum as input Tool asks agent to pass business decision category only Accept evidence/data and let prompt/policy decide

Review steps

  1. Find UI actions, route handlers, CLI commands, SDK methods, tool definitions, and system prompt/context construction.
  2. Build the action map first, then run the noun test.
  3. Separate must-have workflows from low-priority or cosmetic actions.
  4. Inspect mutation responses. They should return IDs, links, status, warnings, and next actions.
  5. Inspect runtime context. The agent path should not depend on guessing hidden IDs or reading a brittle DOM.
  6. Add evals for at least one read task, one mutation task, one permission failure, and one recovery path.

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub1mo

    The skill 'agent-use' is a comprehensive toolkit for auditing and designing agent-ready systems. It includes documentation, templates, and Python scripts for scanning repositories and websites. Security analysis found no malicious intent; identified network and command execution patterns are aligned with the skill's primary purpose as an auditing tool.

  • Socket1mo

    1 alert: gptAnomaly

  • Snyk1mo

    Risk: LOW · No issues

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Last checked against GitHub 2 weeks ago.

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metadata
{
  "version": "3.0.0",
  "reviewed": "2026-09-13"
}
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Local helper scripts require Python 3.10+; structural validation also requires PyYAML 6.0.3 or later in major 6. Network and execution checks need separately authorised tools and environments.

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