Convert all new tests from bare pytest style to unittest.TestCase
with sys.path.insert, matching the convention used by all existing
tests. Remove pyproject.toml and conftest.py.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add pytest infrastructure (pyproject.toml, conftest.py) and unit tests
for modules that previously had zero test coverage:
- test_schema_roundtrip.py: to_dict() serialization for all data classes
- test_reddit_enrich.py: URL parsing, thread data parsing, comment filtering
- test_reddit_sc.py: ScrapeCreators Reddit search (query expansion, subreddit discovery)
- test_instagram_sc.py: Instagram relevance scoring, tokenization, depth config
Includes fixtures/reddit_thread_sample.json for reddit_enrich tests.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
Cherry-picked from PR #24 (el-analista). Adds trending/viral/plugin/skill/tool
noise words to _extract_core_subject, and a last-chance retry that falls back
to the longest non-noise token when 2-word retry also returns 0 results.
cache.py and render.py env overrides were already on main.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The test was picking up the real OPENAI_API_KEY from the shell
environment, causing it to fail on any machine with that key set.
Added @patch.dict(os.environ, {}, clear=True) so the test runs in
a clean env and exercises the file_env path as intended.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* chore: fix YAML error in argument-hint
* add codex auth support to responses API
* Use gpt-5.1-codex-mini as default model for Codex auth
Add CODEX_FALLBACK_MODELS chain (gpt-5.1-codex-mini → gpt-5.2) for
Codex endpoint which doesn't support standard OpenAI models like
gpt-4o-mini. Adds model fallback retry on 400 errors in the Codex
search path. Also adds test_codex_auth.py with 22 unit tests covering
JWT decoding, auth resolution, SSE parsing, and payload building.
* Pass .env credentials to Bird Node subprocesses for X auth
On platforms without browser cookie access (e.g. WSL2), Bird's
vendored Node.js module cannot read AUTH_TOKEN/CT0 from Firefox
or Chrome cookie stores. The .env config file already supports
these values, but they were only loaded into the Python config
dict — never exported to the environment of Node subprocesses.
- Add AUTH_TOKEN/CT0 to env.py config key loading
- Add set_credentials()/\_subprocess_env() to bird_x.py to inject
credentials into the env dict passed to subprocess.run/Popen
- Call set_credentials() in main() before Bird auth detection
---------
Co-authored-by: Justin Williams <jblwilliams@gmail.com>
The Gamma API only searches event titles/slugs, missing markets where the
topic is an outcome (e.g., "Arizona" in NCAA Tournament Winner). This adds:
- All-word query expansion (not just first word): "Arizona Basketball" now
searches "Arizona", "Basketball" independently
- Tag-based domain expansion: extracts category tags (e.g., "NCAA") from
first-pass results and searches those as a second pass
- Neg-risk binary market synthesis: shows team names from market questions
instead of generic Yes/No outcomes
- Question shortening: extracts "Arizona" from "Will Arizona win the NCAA
Tournament?" for clean display
- Increased depth (3 pages) and result caps (15) for more coverage
Live results: "Arizona Basketball" now finds NCAA Tournament Winner (12%),
#1 Seed (88%), Big 12 Champion (69%). "Iran War" returns 15 markets (up
from 9) with no regression.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- _compute_text_similarity() now checks outcome names with bidirectional
substring matching (0.85) and token overlap (0.7), not just event titles
- Collect outcomes from ALL active markets per event, filter to >1% price
- Reorder outcome_prices to surface topic-matching outcome before top-3 truncation
- Add SKILL.md "Prediction Markets" synthesis section with structural/long-term
market preference, domain examples, citation format, and narrative weaving
- Add Polymarket to citation priority list between HN and Web
- Update stats box template to show up to 5 market odds
- Fix render.py "vol24h" label to "volume"
- Add NCAA seed fixture event for outcome-only matching tests
- 82 polymarket tests pass (14 new)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Polymarket results now rank by text similarity, volume, liquidity, price
movement, and competitive score instead of API return position. Also fixes
pagination (DEPTH_CONFIG now controls page count, not a no-op limit param)
and caps results after re-ranking.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Search Polymarket's free Gamma API for relevant prediction markets on any
topic. Uses smart multi-query expansion to cast a wider net (e.g., "Arizona
Basketball" also searches "Arizona"), merges and dedupes by event ID, and
shows price movement context ("up 22.5% this week"). No API key required.
Also hides sources with zero results from the stats output (all sources).
54 new tests, all passing. Full pipeline integration with scoring, dedupe,
cross-source linking, and rendering.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
YouTube videos now get real relevance scores based on token overlap
between the search query and video title (was hardcoded at 0.7).
Uses ratio overlap with stopword removal, floored at 0.1.
Cross-source linking annotates items that discuss the same story
across different platforms (e.g., Reddit + HN + X). Items get
bidirectional cross_refs displayed as [xref: R3, HN5] in compact
output so Claude can triangulate multi-platform coverage.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
HN was appearing before YouTube in the stats block, sort tiebreaker,
and source status. Now consistently: Reddit > X > YouTube > HN > Web.
Also restored emoji + box-drawing chars in test skill SKILL.md.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add HN search via free Algolia API (no key needed). Two-phase approach:
search for stories, then enrich top ones with comments. Integrated into
the full pipeline (normalize, score, dedupe, render) running in parallel
with Reddit/X/YouTube. Source priority: Reddit > X > HN > YouTube > Web.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fix invalid YAML in SKILL.md argument-hint (closes#8)
Wrapped value in single quotes to properly escape double quotes
- Add automatic model fallback for GPT-5 access errors (closes#9)
When OpenAI returns 400 for unverified orgs, retry with gpt-4o
- Add tests for model fallback logic
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Research topics across Reddit + X from the last 30 days using
OpenAI and xAI APIs. Features:
- Auto model selection (GPT-5.x, Grok-3)
- Popularity-aware scoring (relevance + recency + engagement)
- Reddit thread enrichment with real metrics
- Near-duplicate detection
- Multiple emit modes (compact, json, context, path)
- 24h caching with --refresh bypass
- NUX for API key setup
- 87 passing unit tests
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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