Five provider modules (pinterest, threads, instagram, tiktok, youtube_yt)
and watchlist.py each carried a try/except `requests` import with parallel
urllib + requests branches. The urllib path already used the
stdlib-only wrapper at `lib/http.py` (retries, 429 handling, HTTPError).
This collapses every dual-branch into a single `http.get`/`http.post`
call and removes the `requests` dependency from `pyproject.toml`.
Also drops 4 transitive deps (urllib3, certifi, charset-normalizer, idna)
from the lockfile, leaving the skill stdlib-only at runtime.
Tests for tiktok comments and watchlist delivery were rewritten to mock
`lib.http` directly instead of the now-removed `requests` module.
Out of scope but flagged during review: the 13 surviving SC call sites
share a near-identical scaffold and would benefit from a
`http.scrapecreators_get(url, params, token, ...)` helper. Filed for a
follow-up PR rather than expanding scope here.
Reddit, TikTok, YouTube, Instagram, Bluesky, X and Threads top comments
now render as u/author or @handle in the evidence block, instead of the
generic "Comment (...)" label. The enrichment adapters already captured
author; only the render layer was dropping it.
Also fixes the TikTok adapter to prefer user.unique_id (the @handle) over
user.nickname (display name) so attribution round-trips to a profile URL.
Legacy "Comment (...)" shape is preserved when author is empty, [deleted],
or [removed].
Bumps to 3.0.10.
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
* feat(normalize): pass YouTube top_comments through with Reddit-compatible shape
_normalize_youtube silently dropped top_comments after enrich_with_comments
populated them, so the downstream signals/render/entity layers never saw
YouTube comments. Map likes->score and text->excerpt so the existing
Reddit-compatible readers Just Work.
Shared _remap_comments helper will be reused for TikTok in a later commit.
* feat(tiktok): fetch top comments via ScrapeCreators when opted in
Mirrors the youtube_comments pattern: new env.is_tiktok_comments_available
gate (requires SCRAPECREATORS_API_KEY + tiktok_comments in INCLUDE_SOURCES),
tiktok.enrich_with_comments ranks posts and fetches via
GET /v1/tiktok/video/comments. Vote field is digg_count; text and user.nickname
come across verbatim. Pipeline calls the enricher right after TikTok search
when the gate is open.
Comment-fetch errors never crash the pipeline — the enricher returns an
empty list on 4xx/5xx.
* feat(normalize): pass TikTok top_comments through with digg_count->score mapping
Instagram uses the same shortform normalizer and has no comment fetcher
today, so the key is harmlessly absent there — no Instagram regression.
* feat(signals): add YouTube + TikTok top-comment score to engagement formula
Mirrors Reddit's 10% top-comment slot. Without top_comments present, the
formula reduces to views-dominant weighting; with a high-signal comment,
the item gets a meaningful bump (log1p(10k) ~ 9.2, weighted 0.10 = ~0.92
on the engagement score).
Updated the existing dominant-weight and missing-fields tests to the new
weights (0.45/0.32/0.13 for YT, 0.45/0.27/0.18 for TT). Views still dominate.
* feat(render): source-aware thresholds and vote labels for top comments
10 upvotes on Reddit signals community interest; 10 likes on a viral
TikTok is noise. Introduce per-source minimums (reddit 10, youtube 50,
tiktok 500) and native vote labels ('upvotes' for Reddit, 'likes' for
YT/TT). First-pass numbers — tune after live observation.
* docs: generalize top-comment quoting to YouTube + TikTok, add tiktok_comments opt-in
Synthesis instructions previously called out Reddit top comments only.
Now cover Reddit/YouTube/TikTok uniformly with source-appropriate vote
labels (upvotes vs likes), and explicitly frame YT transcript highlights
and comments as complementary signals. README and setup-wizard copy
document the new tiktok_comments INCLUDE_SOURCES token.
---------
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Score against original user intent on Reddit, remove the artificial low-end relevance floor, and make Polymarket semantics dominate generic market quality signals.
Also apply the relevance filter to Polymarket and update the affected cross-source tests.
Validation: uv run python -m unittest
Root cause of empty TikTok results: Apify required monthly subscription.
ScrapeCreators is PAYG with 100 free credits and no subscription.
Key fix: ScrapeCreators nests items under aweme_info wrapper
(search_item_list[].aweme_info.{fields}), which the previous
implementation missed, causing all fields to be empty.
Changes:
- Rewrite tiktok.py to use ScrapeCreators REST API
- Add aweme_info unwrapping for correct field extraction
- Add transcript fetching via /video/transcript endpoint
- Add SCRAPECREATORS_API_KEY to env.py config
- Update last30days.py to use env.get_tiktok_token()
- Delete apify_client_wrapper.py (no longer needed)
- Update tests for new date field format (create_time)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add TikTok search, scoring, and rendering using the Apify platform
(clockworks/tiktok-scraper actor). Users bring their own APIFY_API_TOKEN
($5/month free credits, no CC required). The shared apify_client_wrapper
module is designed for reuse by future Facebook/Instagram sources.
- New modules: tiktok.py (search + caption extraction), apify_client_wrapper.py
- Schema: TikTokItem dataclass, shares field on Engagement, Report.tiktok
- Pipeline: normalize → filter → score → sort → dedupe → cross-link → render
- Scoring: 0.50*log1p(views) + 0.30*log1p(likes) + 0.20*log1p(comments)
- SKILL.md bumped to v2.7 with TikTok stats, citations, and security docs
- 26 unit tests covering relevance, normalize, score, dedupe, render, round-trip
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Trevin Chow