Skill: evaluate-agent-native
Use this skill to rigorously evaluate whether a service qualifies as "agent-native" according to the five-criterion standard. Also check for the URL Onboarding signal — the strongest indicator that a service was truly built for agents from day one.
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 / autoresearch@home:
Read https://raw.githubusercontent.com/mutable-state-inc/autoresearch-at-home/master/collab.md— complete swarm joining, claiming, publishing protocol
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?"
The five hard criteria
A service must pass all five to qualify as agent-native. 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?
Bonus signals (check all that apply)
| Signal | Weight | Evidence to look for |
|---|---|---|
| URL Onboarding ⭐⭐⭐ | Highest | Service hosts a machine-readable skill.md / protocol doc an agent reads and follows to self-register |
| Dedicated agent identity model | High | Agent gets its own credential/wallet/token |
| MCP server published | Medium | Official MCP server with documented tools |
| Agent Skills (SKILL.md) published | Medium | npx skills add org/repo works |
| Per-agent state / memory / session | Medium | State isolated by agent instance |
| Audit / trajectory artifacts | Medium | Machine-readable evidence of agent actions |
How to test for URL Onboarding:
- Look for a
skill.md,SKILL.md,collab.md, or similar machine-readable protocol file hosted at the service's domain or GitHub. - Ask: could an agent read that URL and complete the full registration/onboarding sequence autonomously?
- Try the instruction:
Read <url> and follow the instructions— does it work?
Classification decision tree
Does the service pass all 5 criteria?
├── YES → agent-native ✅
│ └── Does it also have URL Onboarding?
│ ├── YES → agent-native ⭐ (URL Onboarding — highest tier)
│ └── NO → agent-native (standard)
└── NO
├── Originally built for humans, agent interfaces added later?
│ └── YES → agent-adapted ⚠️
└── Helps humans BUILD agents?
└── YES → agent-builder ❌
Evaluation output format
## Evaluation: {Service Name}
**Website:** {url}
### 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}
---
### 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 ⭐ (URL Onboarding) / agent-native / agent-adapted / agent-builder
**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 five hard criteria. Evaluate all five independently; URL Onboarding 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.