Evaluate Agent Native

Skill: evaluate-agent-native

Use this skill to select the correct admission track, collect primary-source evidence, and classify a candidate. Most services use the five-criterion standard. Purpose-built surfaces for operating live agents may use the narrow operator-surface track.

The gold standard: URL Onboarding

Before applying the five criteria, ask the highest-level question:

Can an agent join and start using this service by reading a single URL?

Services that answer YES are exhibiting the strongest possible form of agent-nativeness. They have internalized the agent as first-class user so deeply that the onboarding flow itself is machine-readable:

# The full agent onboarding in one instruction:
Read <url> and follow the instructions.

Examples:

• Moltbook: Read https://www.moltbook.com/skill.md — complete registration, heartbeat, posting, DM protocol
• Ensue: Read https://ensue.dev/docs and call POST https://api.ensue-network.ai/auth/agent-register — shared-memory agent registration
• autoresearch@home: Read https://raw.githubusercontent.com/mutable-state-inc/autoresearch-at-home/master/collab.md — complete swarm joining, claiming, publishing protocol
• db9 / mem9 / mails.dev / MailboxKit / Agents Mail: domain-hosted skill.md URL onboarding for database, memory, and email workflows

This is qualitatively different from:

• An SDK that a human developer installs (requires human coding time)
• An MCP server that a human adds to a config file (requires human config edit)
• A REST API that requires API key setup (requires human account creation)

URL Onboarding means the agent itself handles all of this — reading, understanding, and executing the join sequence autonomously.

Mark URL Onboarding as a strong bonus signal and highlight it prominently in the evaluation report.

When to activate

Activate when the user asks:

• "Is [service] agent-native?"
• "Does [service] qualify for the awesome list?"
• "I want to add [service] — does it meet the criteria?"
• "What's the difference between agent-native and agent-adapted?"
• "Why isn't [service] on the list?"
• "Does [service] have URL Onboarding?"

Choose the admission track

Use the standard track for infrastructure an agent consumes or invokes. Use the operator-surface track only when the product's original and primary purpose is operating live AI agents.

Do not use the exception for a generic dashboard, IDE skin, terminal theme, or process monitor that merely adds agent labels.

Standard track: five hard criteria

A standard-track service must pass all five. Evaluate each one explicitly.

Criterion 1 — Agent-First Positioning

Test: Does the official homepage or documentation explicitly identify AI agents as the primary consumer?

Evidence to look for:

• Homepage headline naming AI agents
• Documentation framing agents as the core user
• Product name or tagline that only makes sense for agents

Red flags:

• "Now with AI agent support" (agents are an add-on)
• "Build apps, workflows, and agents" (agents are one of many outputs)

Criterion 2 — Agent-Specific Primitives

Test: Does the API expose at least one primitive with no meaningful human-facing equivalent?

Questions to ask:

• What is the core API object? Agent inbox? KYA token? Claim? Heartbeat? Or generic inbox/token/task?
• Would this primitive exist if agents didn't exist?
• Is the output format optimized for LLM consumption or human reading?

Pass examples: agent inbox, KYA identity token, approval gate with context-window injection, claim_experiment(), heartbeat protocol, publish_hypothesis().

Fail examples: a REST API that sends emails (humans use it too), a webhook any server can receive.

Criterion 3 — Autonomy-Compatible Control Plane

Test: Can an agent complete a full task loop without a human clicking anything?

Questions to ask:

• Can the agent provision its own credentials?
• Can the agent initiate, execute, and complete the action without a human redirect?
• Does the service provide agent-appropriate constraint mechanisms?

Criterion 4 — Machine-to-Machine Integration Surface

Test: Is the primary interface an SDK, REST API, MCP server, webhook, or machine-readable URL?

Questions to ask:

• Can an agent use this service without a human ever opening a browser?
• Is there a URL, SDK, REST API, or MCP server documented as the primary integration path?

Note: A service that exposes a machine-readable skill.md or protocol URL (URL Onboarding) passes this criterion with exceptional strength.

Criterion 5 — Agent Identity / Delegation Semantics

Test: Does the service distinguish (a) agent's own identity, (b) delegated user permissions, (c) audit trail?

Operator-surface track

An operator surface must pass all five track requirements:

1. Agent-operations-first: Official positioning is explicitly about running or supervising AI agents.
2. Agent-specific live state: It continuously interprets state such as rollout/session identity, context pressure, tools, subagents, permissions, Skills, MCP configuration, or quota windows.
3. Session attribution: Displayed state maps to a concrete agent session, rollout, or typed agent path.
4. Dedicated operational surface: It exposes a CLI, wrapper, daemon, hook, or status-line protocol purpose-built for agent operations.
5. Honest boundary: It states when autonomy, machine APIs, delegated credentials, approval enforcement, Skills, or MCP are absent. Read-only observation must not be presented as orchestration or authorization.

Session attach/list/stop controls strengthen the case but are not mandatory. An operator surface can be human-facing and read-only; that is the point of this narrow track.

Bonus signals (check all that apply)

How to test for URL Onboarding:

1. Look for a skill.md, SKILL.md, collab.md, or similar machine-readable protocol file hosted at the service's domain or GitHub.
2. Ask: could an agent read that URL and complete the full registration/onboarding sequence autonomously?
3. Try the instruction: Read <url> and follow the instructions — does it work?

Classification decision tree

Is this infrastructure agents consume or invoke?
├── YES → apply all five standard criteria
│   └── PASS → agent-native (standard) ✅
└── NO → was it purpose-built to operate live AI agents?
    ├── YES → apply all five operator-surface requirements
    │   └── PASS → agent-native (operator surface) ✅
    └── NO → agent-adapted, agent-builder, or out of scope

For either qualifying track, add ⭐ when URL Onboarding is real.

Evaluation output format

## Evaluation: {Service Name}
**Website:** {url}
**Admission track:** Standard / Operator surface

### URL Onboarding Check ⭐
**Has URL Onboarding:** YES / NO
**Onboarding instruction (if YES):** Read {url} and follow the instructions to {join/register/participate}
**Notes:** {what the agent gets by reading that URL}

---

### Criterion 1 — Agent-First Positioning
**Result:** PASS / FAIL / PARTIAL
**Evidence:** "{exact quote}" — {source URL}

### Criterion 2 — Agent-Specific Primitives
**Result:** PASS / FAIL / PARTIAL
**Evidence:** {primitive name and description}
**No human equivalent because:** {explanation}

### Criterion 3 — Autonomy-Compatible Control Plane
**Result:** PASS / FAIL / PARTIAL
**Evidence:** {how agents operate without human confirmation}

### Criterion 4 — Machine-to-Machine Integration Surface
**Result:** PASS / FAIL / PARTIAL
**Evidence:** {URL, SDK, API, MCP details}

### Criterion 5 — Agent Identity / Delegation Semantics
**Result:** PASS / FAIL / PARTIAL / N/A
**Evidence:** {identity model details}

### Operator-Surface Track (complete instead of standard criteria when selected)
1. Agent-operations-first — PASS / FAIL — {evidence}
2. Agent-specific live state — PASS / FAIL — {evidence}
3. Session attribution — PASS / FAIL — {evidence}
4. Dedicated operational surface — PASS / FAIL — {evidence}
5. Honest boundary — PASS / FAIL — {documented limitations}

---

### Bonus signals
- [ ] URL Onboarding ⭐⭐⭐ — agent joins by reading one URL
- [ ] Dedicated agent identity model
- [ ] MCP server published
- [ ] Agent Skills (SKILL.md) published
- [ ] Per-agent state/memory/session
- [ ] Audit/trajectory/replay artifacts

---

### Overall verdict
**Classification:** agent-native ⭐ / agent-native (standard) / agent-native (operator surface) / agent-adapted / agent-builder / out of scope
**Recommendation:** Add to main list / Add to Excluded section / Do not add
**Confidence:** High / Medium / Low
**Reasoning:** {one paragraph summary}

### Next steps
{If agent-native with URL Onboarding: highlight this in the issue and service file prominently}
{If agent-native without: link to issue template}
{If agent-adapted: explain what would need to change}

Common borderline cases

"The product added an MCP server — does that make it agent-native?"

No. MCP support is a bonus signal, not a criterion. The core question is whether the service was designed from inception for agents. A human email provider that adds an MCP server is still agent-adapted.

"The service has URL Onboarding but other criteria are weak."

URL Onboarding is the strongest bonus signal but cannot substitute for the selected admission track. It is an amplifier, not a replacement.

"The service says 'for AI agents' in marketing."

Check the actual primitives. URL Onboarding is a reliable signal because it requires genuine design effort — you can't fake it with a marketing blog post.

"It is a human-facing Codex HUD. Is that automatically excluded?"

No. Apply the operator-surface track. A HUD qualifies only when it was purpose-built for live agents, interprets agent-specific runtime state, attributes it to concrete sessions, exposes a dedicated operational surface, and discloses its lack of control or delegated authority. A generic terminal dashboard still fails.