Reference skill for CDF Data Modeling API best practices. Covers concurrency limits (avoiding 429s), pagination patterns for instances.list and instances.query, batching write operations, search vs filter guidance, and the QueuedTaskRunner (Semaphore) utility for controlling concurrent requests. Triggers: DMS limits, 429 error, rate limit, pagination, cursor, nextCursor, batching, semaphore, QueuedTaskRunner, cdfTaskRunner, instances.search, instances.list, instances.query, instances.upsert, concurrency, deadlock.
89
88%
Does it follow best practices?
Impact
96%
1.47xAverage score across 3 eval scenarios
Passed
No known issues
Semaphore utility and batch upsert/delete
Semaphore file path
100%
100%
QueuedTaskRunner class
50%
100%
cdfTaskRunner export
0%
100%
Default concurrency 15
0%
100%
Upsert uses cdfTaskRunner
0%
100%
Upsert batch size 1000
100%
100%
Upsert uses Promise.all
100%
100%
Separate delete runner
20%
100%
Delete batch size 1000
100%
100%
Delete sequential loop
0%
100%
Pitfalls documented
100%
100%
Pagination and search/filter operator selection
Typeahead uses OR operator
70%
100%
Precise lookup uses AND operator
0%
100%
Search not filter for text
37%
100%
list pagination loop
100%
100%
query pagination cursors
100%
100%
query isEmpty check
100%
100%
cdfTaskRunner wraps calls
20%
100%
Pagination + semaphore combined
100%
0%
list uses filter not search
62%
75%
Pagination strategy documented
100%
100%
Report written to disk
100%
100%
Deadlock fix, in-filter batching, key deduplication
No nested schedule calls
100%
100%
Promise.all with semaphore
100%
100%
in-filter batch size ≤ 1000
100%
100%
Multiple batches merged
100%
100%
Key-based deduplication
0%
100%
Unique key per work order
0%
100%
Deadlock fix explained
100%
100%
in-filter limit explained
100%
100%
Key deduplication explained
40%
100%
Raw Promise.all absent
100%
100%
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Table of Contents
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