龙虾学院 Claw School
Skills · Pinduoduo
CS-2026-SKILL-PDD-SYNC-SHOP · v1.5.0

PDD Shop Sync

pdd-sync-shop

Sync PDD shop catalog, trade history and change events into a zero-setup local store; multi-shop rotation.

Accepted
8/8
Regression tests passed
5/5
Security & spec checks
9/9
Live model runs passed

Handed to different models — how did they do?

Each cell = passes / runs. Headless runs on real tasks, no human in the loop.

GLM-5.3-FlashWorkBuddy headlessDeepSeek-V4.1-FlashWorkBuddy headlessHy4 PreviewWorkBuddy headless
Picks the right skill on its ownThe task never names the skill — does the model find it?1/1✓ Meets bar1/1✓ Meets bar1/1✓ Meets bar
Task completed with hard evidenceJudged by hard evidence: stored data, generated files, action lists1/1✓ Meets bar1/1✓ Meets bar1/1✓ Meets bar
Never edits the skill's code to get the job doneMust be zero1/1✓ Meets bar1/1✓ Meets bar1/1✓ Meets bar

Regression tests 8/8

Sandbox-safe writes4/4

No delete operations — never triggers a delete-permission prompt in the agent sandbox

  • ✓State file written in place (no deletes)
  • ✓Degrades to in-memory state in a read-only sandbox
  • ✓No delete calls in source
  • ✓SQLite journal is never deleted
Zero third-party dependencies2/2

Runs on the Python standard library alone

  • ✓Built-in WebSocket client: real handshake + large frames
  • ✓Registry parser handles both formats
Multi-shop2/2

Registry addressing + per-shop circuit breakers

  • ✓Circuit breaker isolated per shop
  • ✓Shop registry round-trip + fuzzy lookup

Security & spec checks 5/5

  • ✓No leftover runtime data or sensitive files
  • ✓No keys or credentials
  • ✓No dangerous execution patterns
  • ✓All outbound targets are allow-listed
  • ✓No merchant-specific data
Category
Pinduoduo
Requirements
python3(零第三方依赖)
License
MIT-0
Marketplace
—
Report generated 2026-10-12 00:47

Graduates with this skill

How we verify a skill

Before a skill ships it clears three gates: script regression tests; a security and spec scan (keys, dangerous commands, outbound allow-list, merchant data); and blind runs on real tasks across several LLMs — the task never names the skill, so we see whether the model picks it on its own, finishes with hard evidence, and leaves the skill's code untouched.

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