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ivangdavila

B2A

作者 Iván · GitHub ↗ · v1.0.0
linuxdarwinwin32 ✓ 安全检测通过
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在 OpenClaw 中安装
/install b2a
功能描述
Sell to AI agents with machine-readable products, agent-optimized APIs, structured pricing, and discovery strategies for the agentic economy.
使用说明 (SKILL.md)

When to Use

User building products/services for AI agents as customers. Covers making products agent-discoverable, designing for autonomous purchasing, payment integration, and competing when buyers compare cold data instead of responding to storytelling.

Quick Reference

Topic File
Technical implementation infrastructure.md
Agent discovery & SEO discovery.md
Retail/ecommerce specifics retail.md

The Paradigm Shift

B2C/B2B B2A
Humans browse, compare, feel Agents query, parse, decide
Emotional storytelling wins Structured data wins
UX optimized for eyes APIs optimized for parsing
Brand = trust + emotion Brand = verified track record
Loyalty = relationship Loyalty = switching cost
Marketing = persuasion Marketing = engineering

Core Rules

1. Machine-Readable First

  • Products must be structured objects, not prose descriptions
  • JSON-LD, Schema.org, OpenAPI with typed fields
  • If an agent has to "interpret" text to extract price/specs, you lose
  • Normalize units: shipping_days_max: 2, not "fast shipping"

2. Comparability Is Everything

Agents compare ruthlessly. Win by being comparable:

  • Standardized attributes across your catalog
  • Same fields as competitors (price_currency, availability_stock)
  • SLAs with concrete numbers, not promises
  • "Better" must be objectively measurable

3. Discovery ≠ SEO

Agents don't Google. They query registries and APIs:

  • Publish in skill stores / capability directories
  • /.well-known/ai-plugin.json or MCP tools
  • Metadata must declare capabilities, not market them
  • The new PageRank = ranking in agent skill stores

4. Trust Is Verified, Not Told

Agents don't believe claims. They verify:

  • Uptime/latency/SLA history via API, not badges
  • Reviews from other agents (programmatic reputation)
  • Certifications as queryable data, not PDF downloads
  • Track record > marketing copy

5. Zero-Friction Trial or Death

Agents don't "consider"—they test or discard:

  • Onboarding \x3C 1 API call
  • Sandbox with rate limits, not "talk to sales"
  • Must work perfectly first time (no second chances)
  • Errors must be machine-readable, not HTML pages

6. Payments for Agents

The agent needs to transact autonomously:

  • Stripe Agent Toolkit, Mastercard Agent Pay, or similar
  • Pre-authorized budgets (agent has $X to spend)
  • Programmatic receipts and confirmations
  • Escrow for trust between unknown parties

7. Metrics That Matter

Metric What It Measures
Agent Conversion Rate % queries → purchase
Decision Latency Time from first query to commit
Comparison Survival % times reaching final shortlist
Repeat Agent Retention % agents that return
API Error Rate Failures causing agent to discard

Traditional metrics (page views, bounce rate) are meaningless.

Common Traps

Trap Why It Fails
Pretty website, no API Agents don't see your UI
"Contact us for pricing" Agents need programmatic pricing
Marketing copy in descriptions Agents parse data, skip prose
HTML error pages Agents need JSON errors
Manual onboarding Agents won't wait
Trust badges instead of APIs Unverifiable = untrusted
Optimizing for humans first Delays agent-readiness

Honest Limitations

What an AI helping you with B2A cannot do:

  • Create track record — You have to actually deliver 99.9% uptime
  • Know internal rankings — How Claude/GPT rank skills is opaque
  • Predict agent decisions — Each agent has its own heuristics
  • Guarantee discovery — Skill stores may have hidden placement deals
  • Prevent gaming — Competitors lying about specs is real

Readiness Checklist

□ Products exposed via structured API (not scraping required)
□ Pricing programmatically queryable
□ Inventory/availability real-time
□ Authentication supports client_credentials (not interactive)
□ Errors return JSON with semantic codes
□ Onboarding works in \x3C 5 API calls
□ Payment rails support autonomous agents
□ SLA metrics exposed via API
□ Listed in relevant skill registries
安全使用建议
This skill is a content/strategy guide only and does not request credentials or install code. It's coherent with its stated purpose. Before you act on its recommendations: (1) treat any guidance to publish manifests or expose APIs as a prompt to design proper authentication, rate-limiting, and access controls; (2) protect any payment credentials and user-agent linking data (agent_id, user_id) and comply with privacy rules; (3) avoid exposing supplier/private/internal details as advised in the docs; and (4) if you implement agent-driven payments or pre-authorized budgets, audit for abuse scenarios (compromised agents, runaway spending) and implement spending caps and strong audit trails.
功能分析
Type: OpenClaw Skill Name: b2a Version: 1.0.0 The skill bundle consists entirely of documentation files (`.md`) and a metadata file (`_meta.json`). The content provides conceptual guidance and best practices for building products and services for AI agents (B2A). There are no executable scripts, no instructions for the OpenClaw agent to perform any actions (malicious or otherwise), and no evidence of prompt injection attempts, data exfiltration, unauthorized execution, or persistence mechanisms. The included code snippets are illustrative examples for developers, not commands for the agent to execute. The skill is purely informational and educational.
能力评估
Purpose & Capability
Name/description (B2A: selling to AI agents) match the provided content: guidance on machine-readable products, APIs, pricing, discovery, and payments. No unrelated credentials, binaries, or installs are requested.
Instruction Scope
SKILL.md and linked docs are guidance and API examples (OpenAPI, OAuth client_credentials, Stripe patterns, schema.org). They do not instruct the agent to read local files or exfiltrate data. Note: the docs explicitly recommend publishing manifests, exposing metrics, and logging agent_id/user_id per-query for analytics — these are expected for the purpose but carry privacy/data-handling implications if implemented.
Install Mechanism
Instruction-only skill with no install spec, no code files to write, and no downloads. This is the lowest-risk install profile and is proportionate for a documentation/strategy skill.
Credentials
The skill requests no environment variables or credentials. However, it recommends implementing OAuth client_credentials, Stripe (or similar) payments, and per-agent API keys in your implementation — those recommendations imply you will handle credentials and payment secrets when you implement the guidance. That is expected but requires secure handling (not a problem with the skill itself).
Persistence & Privilege
No special persistence or privileges requested (always: false). The skill does not attempt to modify other skills or agent settings; autonomous invocation defaults are unchanged.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install b2a
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /b2a 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release
元数据
Slug b2a
版本 1.0.0
许可证
累计安装 0
当前安装数 0
历史版本数 1
常见问题

B2A 是什么?

Sell to AI agents with machine-readable products, agent-optimized APIs, structured pricing, and discovery strategies for the agentic economy. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 585 次。

如何安装 B2A?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install b2a」即可一键安装,无需额外配置。

B2A 是免费的吗?

是的,B2A 完全免费(开源免费),可自由下载、安装和使用。

B2A 支持哪些平台?

B2A 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(linux, darwin, win32)。

谁开发了 B2A?

由 Iván(@ivangdavila)开发并维护,当前版本 v1.0.0。

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