Context Hub for OpenClaw

Use Context Hub (chub) to fetch versioned API docs/skills before coding, then persist learnings with annotations.

Use this skill whenever implementation depends on third-party APIs/SDKs or fast-changing tools.

When to use

Trigger this skill when the user asks to:

• integrate with OpenAI/Anthropic/Stripe/etc.
• write SDK/API code where versions matter
• debug integration failures likely caused by doc drift
• create reusable internal implementation playbooks

Do not rely only on memorized API shapes. Fetch current docs first.

OpenClaw trigger heuristics (explicit)

Use this decision rule before coding:

• HIGH confidence trigger (use Context Hub immediately):

  • Request mentions an external API/SDK by name (openai, anthropic, stripe, supabase, etc.)
  • Task includes auth, webhooks, function calling, streaming, uploads, pagination, or retries
  • User asks for production-ready integration code/tests

• MEDIUM confidence trigger (use Context Hub unless local repo docs are clearly authoritative and current):

  • Refactor/migrate API client code
  • Fix runtime errors that look like contract drift (400 invalid param, schema mismatch, deprecated endpoint)
  • Add features across multiple languages/runtimes where SDK behavior may differ

• LOW confidence trigger (Context Hub optional):

  • Pure business logic with no third-party integration
  • Trivial formatting/renaming changes
  • Internal-only modules with stable local docs

Fast OpenClaw checklist

If 2+ of these are true, run chub first:

1. External API/SDK involved
2. Version-specific behavior likely
3. Endpoint/schema uncertainty exists
4. Failure cost is high (payments/auth/data integrity)
5. Existing code recently broke after dependency updates

Concrete trigger examples by provider

Stripe — trigger Context Hub first when:

• Implementing or fixing webhook signature verification
• Creating subscription/payment-intent flows with idempotency requirements
• Handling API version mismatch errors or changed field semantics

Example:

chub search "stripe webhooks" --json
chub get stripe/api --lang js --json

OpenAI — trigger Context Hub first when:

• Implementing chat/responses APIs with tool/function calling
• Streaming responses or migrating from older endpoints/SDK patterns
• Debugging model parameter mismatches, structured output schemas, or file/tool workflows

Example:

chub search "openai chat responses function calling" --json
chub get openai/chat --lang js --json

Core workflow (doc-first coding)

1. Search candidates

chub search "<vendor api or sdk>" --json

2. Fetch the best match (pin language/version when available)

chub get <id> --lang js --json
# or
chub get <id> --lang py --version <sdk-version> --json

3. Pull only needed references to reduce token noise

chub get <id> --file references/errors.md
# or
chub get <id> --full

4. Implement against fetched docs, not assumptions.

5. Persist new learnings (only non-obvious, high-value findings)

chub annotate <id> "<gotcha + fix + context>"

6. Optional quality feedback (ask user before sending)

chub feedback <id> up
chub feedback <id> down --label outdated --label wrong-examples

Annotation quality standard

Good annotation format:

• Symptom: what broke
• Cause: why docs/code failed in practice
• Fix: exact change that worked
• Scope: version/environment constraints

Example:

chub annotate stripe/api "Webhook signature failed in Next.js route handlers; use raw request body before JSON parse. Verified on stripe-node 17.x."

Avoid annotations that just restate obvious doc text.

OpenClaw integration pattern

• Treat Context Hub as first source for coding accuracy.
• Save references to project files when useful:

mkdir -p .context
chub get <id> --lang js -o .context/<id>.md

• For repeated workflows, maintain a compact project playbook (e.g., shared/<project>/context-notes.md) and keep chub annotations concise.

Useful commands

chub update
chub cache status
chub search "stripe webhooks"
chub get stripe/api --lang js
chub annotate --list --json
chub feedback --status

Safety + ops notes

• Prefer --json for machine parsing in agent flows.
• Keep annotation volume low and signal high.
• If docs conflict with runtime behavior, annotate locally and continue with verified behavior.
• Use version targeting (--version) when package major versions differ.