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Collect Baidu Tieba thread and user evidence with MediaCrawler using keyword or bar discovery, exact thread detail, replies, and creator pages.
Passed 18 of 22 test cases with the skill, and 10 of 22 without it.
and 10 more in the full report
9 fixed10 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
A user submits this request: 'Scrape 100 threads from the Linux Baidu Tieba forum published in the past 7 days up to 2 levels of replies.' Purpose is listed as kernel feedback research. Before running the crawler, what required destination parameter is missing?
| Checked | Without skill | With skill |
|---|---|---|
| Request the invoking Model Memory destination parameter. | Missed | Passed |
The instructions your model reads when the skill loads.
Follow media-crawler. Require purpose, keyword/bar/thread/user inputs, target size and time bounds, reply depth, and invoking Model Memory destination.
Use --platform tieba --type search for discovery. Configure bar names in TIEBA_NAME_LIST when the research scope is community-specific; otherwise use intent-distinct keywords. Configure exact thread IDs in TIEBA_SPECIFIED_ID_LIST and full user-home URLs in TIEBA_CREATOR_URL_LIST; use detail/creator mode.
Preserve bar name, thread title, original post, floor/reply relationships, author, publish time and canonical URL. Do not flatten replies into independent opinions without their parent context. Sample across different threads/authors and deduplicate by thread ID plus post/floor identity. Start without deep replies, enrich only relevant threads, and stop on authentication/risk control.
Return scope/bar list, exact inputs, raw/copied paths, thread/reply/user counts, coverage, time, commit, exclusions and selection rationale.
test cases passed, vs 10 / 22 without
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