* feat: INCLUDE_SOURCES config + TikTok/Instagram opt-in in NUX
- INCLUDE_SOURCES=tiktok,instagram in .env forces sources on for all
query types, bypassing the tier system
- NUX shows opt-in modal after ScrapeCreators key is saved: "Also
search TikTok and Instagram?" with honest call-usage warning
- Tier system preserved as default — override only when INCLUDE_SOURCES set
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: neutral call-usage copy — works for free and paid tiers
---------
Co-authored-by: Matt Van Horn <mvanhorn@MacBook-Pro.local>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add extract_transcript_highlights() that scores sentences by specificity
(numbers, proper nouns, topic relevance) and filters YouTube filler
(subscribe, welcome back, etc). Top 5 highlights shown as structured
bullets in compact output. Full transcript moved to collapsible <details>
block so the LLM reads highlights first, full text on demand.
SKILL.md updated to instruct the judge agent to quote highlights
directly in synthesis, same as Reddit top comments.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
TRANSCRIPT_MAX_WORDS raised from 500 to 5000 so the LLM gets the full
content of most videos (up to ~25 minutes). Removed the second 200-char
truncation in render.py that was reducing transcripts to a single sentence
before the judge agent ever saw them.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When Cloudflare blocks requests to bsky.social or public.api.bsky.app
with a 403, the error was swallowed by a generic except clause and
reported as "Bluesky auth failed" - misleading users into thinking
their credentials were wrong.
Now _create_session() preserves the specific error in _session_error,
and search_bluesky() surfaces it. Cloudflare 403s get a clear message
about network-level blocks. Actual 401s say "Invalid credentials".
Closes#69
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Map prompt-oriented product searches and animation-oriented build searches away from the breaking-news default so source tiering and tiebreakers align with the benchmark topics.
Validation: uv run python -m unittest tests.test_query_type
Isolate eval subprocesses from local yt-dlp config and fix nDCG normalization against the judged pool.
Validation: uv run python -m unittest tests.test_evaluate_search_quality
This workspace uses GOOGLE_API_KEY as the canonical Google credential. Accept it ahead of the Gemini-specific aliases so the local evaluation harness can run without a separate GEMINI_API_KEY export.
Validation: uv run python -m unittest tests.test_env_project tests.test_evaluate_search_quality and a one-shot keychain-backed resolution check.
Add an optional local evaluator that compares a baseline revision against a candidate checkout, computes deterministic stability metrics, and can call Gemini for judged ranking metrics when configured.
The harness isolates child runs with a temporary HOME and a node-free PATH so historical revisions cannot trigger Bird browser-cookie auth during evaluation.
Validation: uv run python -m unittest and local smoke/full deterministic eval runs.
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
Phase-2 Bird handle searches were still spawning Node without the injected AUTH_TOKEN/CT0 env. That left the search pipeline vulnerable to Chrome keychain prompts whenever a query drilled into X handles.
Pass the popup-safe subprocess env through those handle searches and cover it with a regression test.
Classify prompting and animation queries as how_to so the stack does not treat them as generic breaking news. Also keep X available for how_to and preserve YouTube/HN coverage for breaking-news and prediction queries.
Validated with uv run python -m unittest tests.test_query_type and the five-query local comparison run used for PR #65 review.
Update README, launch copy, and UI guidance to prefer popup-free AUTH_TOKEN/CT0 configuration, and keep X backend selection on the verified Bird or xAI paths.
Validation: uv run python -m unittest tests.test_env_project
- Remove duplicate detect_query_type from query.py (divergent 5-type version);
canonical 7-type version lives in query_type.py
- Fix reddit.py import to use query_type.detect_query_type
- Clean unused STOPWORDS/SYNONYMS/tokenize imports from youtube_yt, instagram,
tiktok, scrapecreators_x, bird_x after relevance consolidation
- Fix _relevance_filter default from 0.7 to 0.0 (items without relevance
should not silently pass the filter)
- Remove --dateafter from yt-dlp (returns 0 results for evergreen topics)
- Remove restrictSearchableAttributes from HN search (misses Ask/Show HN)
- Lower HN points filter from >5 to >2 (avoids filtering niche posts)
- Add error logging to select_openai_model HTTP failures
- Remove mise.toml and internal planning doc from repo
- Update module docstrings to describe current purpose, not migration history
- Update tests to import from canonical relevance module
Filter items with relevance < 0.3 per source after dedup, but only
when list has >3 items. Extends the Reddit-only minimum-result
guarantee to all sources: keeps top 3 by relevance if all filtered.
This works with the computed relevance scores from the previous commit
to actually remove off-topic results from the final report.
- hackernews: use extract_core_subject instead of raw topic, add
points>5 filter and restrictSearchableAttributes=title to reduce
noise from URL-match and low-signal posts
- youtube: add --dateafter parameter to yt-dlp for server-side date
filtering (Python soft filter still handles fallback)
- reddit: skip opinion/review query variant for how_to/comparison
queries where it adds noise
- bird_x: add OR-group retry with compound terms before falling back
to word-dropping (uses X OR operator for multi-concept queries)
- query.py: add detect_query_type() and extract_compound_terms()
- bird_x: parse_bird_response now accepts query param and computes
token_overlap_relevance against tweet text
- reddit: _normalize_post computes relevance from query vs title+selftext
- hackernews: blends 60% Algolia rank + 40% token overlap + engagement
This makes the 45%-weight relevance factor in score.py actually
differentiate results instead of being a constant.
Replace duplicated STOPWORDS, SYNONYMS, _tokenize, and _compute_relevance
in four modules with imports from the shared relevance.py module.
Existing tests pass unchanged since modules re-export the functions
under the same names via import aliases.
Replace duplicated _extract_core_subject() in bird_x, reddit, youtube_yt,
tiktok, instagram, bluesky, and scrapecreators_x with thin wrappers that
delegate to query.extract_core_subject() with platform-specific noise sets.
Each module preserves its current behavior exactly:
- bird_x: max_words=5, strip_suffixes=True, full noise set
- youtube_yt: keeps tips/tricks/tutorial/guide/review (content types)
- reddit: preserves original smaller noise set
- tiktok/instagram: same small noise set
- bluesky/scrapecreators_x: minimal noise set
Existing tests pass without modification since _extract_core_subject()
still exists as a callable on each module.
Previously tiktok.py and instagram.py returned an error when the
requests library was not installed. Reddit already had an http.get()
fallback using stdlib urllib. Apply the same pattern so all three
ScrapeCreators modules work without requests installed.
Consolidate duplicated _extract_core_subject() (7 copies across bird_x,
reddit, youtube_yt, tiktok, instagram, bluesky, scrapecreators_x) into
query.extract_core_subject() with parameterized noise set, max_words,
and suffix stripping.
Consolidate duplicated _tokenize/_compute_relevance/STOPWORDS/SYNONYMS
(4 copies across youtube_yt, tiktok, instagram, scrapecreators_x) into
relevance.token_overlap_relevance() with hashtag-aware matching.
Integration into per-module imports follows in next commits.
The task profile is search tool invocation + JSON extraction — not
reasoning or creative work. Mini models handle this equally well at
3-5x lower cost per call.
OpenAI changes:
- Rename is_mainline_openai_model -> is_search_capable_model
- Include mini variants (gpt-5-mini, gpt-4.1-mini) in candidate pool
- Exclude gpt-4o-mini (no domain filtering) and nano (no web_search)
- select_openai_model() now prefers mini within newest generation
- OPENAI_FALLBACK_MODELS: gpt-5-mini first, mainline as last resort
- MODEL_FALLBACK_ORDER: same mini-first ordering
xAI changes:
- Switch alias from grok-4-1-fast (reasoning) to
grok-4-1-fast-non-reasoning — same token price, faster response,
no wasted reasoning tokens for structured extraction
Cost per Reddit search call: ~$0.015 (gpt-5-mini) vs ~$0.044 (gpt-4.1)
Brave's /res/v1/llm/context returns pre-extracted text chunks
optimized for LLM consumption instead of URLs + short snippets.
Enable with BRAVE_LLM_CONTEXT=1 env var; same API key and pricing.
- Add _search_llm_context() and _normalize_llm_context() to brave_search.py
- Wire opt-in flag through _search_web() in last30days.py
- Update module docstring (free tier eliminated Feb 2026)
- Add 23 tests covering normalization, filtering, date parsing
Add events_status=active and keep_closed_markets=0 to Gamma API
search params, filtering out resolved/closed markets that clutter results.
These params are confirmed in the Polymarket OpenAPI spec.
Detect query type (product/concept/opinion/how_to/comparison/breaking_news/
prediction) via lightweight regex patterns and use it for:
1. Source selection: each query type has tier-1 (always run) and tier-2
(run if available) sources. Unlisted sources are opt-in only.
Truth Social is always opt-in regardless of query type.
2. WebSearch penalty: varies by query type instead of flat -15pt.
Concept queries get 0 penalty (web docs are authoritative),
how_to gets 5pt, breaking_news gets 10pt, product/opinion get 15pt.
3. Tiebreaker ordering: source priority varies by query type.
YouTube ranks first for how_to, Polymarket for prediction,
HN for concept queries, X for breaking news.
All changes are backward-compatible: callers that don't pass query_type
get the original behavior (15pt penalty, Reddit > X > YouTube tiebreaker).
Model optimization (mini-first fallback, is_search_capable_model) belongs
in PR #67. This PR stays focused on endpoint/API fixes only.
Also fixes pre-existing test bug where test asserted gpt-4o was first in
MODEL_FALLBACK_ORDER when it was actually gpt-4.1.
- Instagram: migrate /v1/ to /v2/ ScrapeCreators endpoint (v1 deprecated Feb 2026)
- OpenAI: switch fallback chain to [gpt-5-mini, gpt-4.1-mini, gpt-4.1] (8x cheaper,
gpt-5-mini is the first mini model supporting web_search with filters.allowed_domains)
- xAI: use explicit grok-4-1-fast-non-reasoning (bare name aliases to reasoning variant)
- xAI: pass from_date/to_date natively to x_search tool config instead of prompt-only
- Polymarket: correct rate limit comment (15K/10s, not 350/10s)
Mastodon-compatible API at truthsocial.com/api/v2/search.
Opt-in via TRUTHSOCIAL_TOKEN env var (bearer token from browser).
Silent when unconfigured. Full pipeline: search, parse, normalize,
score, dedupe, render across all 10 pipeline files.
27 new tests, 440 total passing.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
searchPosts endpoint now returns 403 for unauthenticated requests.
Add session auth via createSession, gate on BSKY_HANDLE + BSKY_APP_PASSWORD
env vars. When unconfigured, Bluesky is completely invisible (no error).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Free, no-auth-required search via public.api.bsky.app.
Always-on like HN and Polymarket (no API key needed).
- New scripts/lib/bluesky.py: search + parse via AT Protocol
- BlueskyItem schema, normalization, scoring, deduplication
- Wired into orchestrator ThreadPoolExecutor with timeout config
- Rendering in compact, full, and JSON output modes
- 14 unit tests covering parsing, dates, relevance, edge cases
- --search=bluesky / --search=bsky for bluesky-only mode
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
One SCRAPECREATORS_API_KEY now covers Reddit, TikTok, Instagram, AND X.
Priority: Bird (free) > xAI API > ScrapeCreators (shared key).
New module scrapecreators_x.py follows the same pattern as tiktok.py.
Updated env.py source routing and last30days.py orchestrator dispatch.
Includes 20 unit tests.
Fixes#55.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add per-project configuration via .claude/last30days.env, discovered by
walking up from cwd. Uses the same .env format as the existing global
config — no new parsers or formats.
Priority (highest wins):
1. Environment variables
2. .claude/last30days.env (per-project)
3. ~/.config/last30days/.env (global)
Also adds file permission checking — warns to stderr if config files
are readable by other users (should be chmod 600).
Includes tests for discovery, precedence, source tracking, and
permission warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Xiaohongshu search via local MCP service (opt-in, zero impact if service not running)
- Reddit public JSON fallback (works with zero API keys)
- Reddit priority: ScrapeCreators -> OpenAI -> public fallback
- Updated env.py: Reddit always available via public fallback
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Resolve conflicts between ScrapeCreators Reddit (main) and
public Reddit fallback (PR #48). Priority: ScrapeCreators ->
OpenAI -> public Reddit fallback.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add --save-dir flag to last30days.py that saves raw research output
during the existing script run. Remove entire "Save Research to
Documents" section from SKILL.md (~45 lines). No more extra tool
calls, no (No output), no multi-minute cogitation after invitation.
Tested: --mock confirms file creation and duplicate date suffixing.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- New scripts/lib/reddit.py: multi-query expansion, global search,
subreddit discovery, targeted subreddit search, comment enrichment
- 68 results in 17s vs ~15 results in 60-90s (OpenAI)
- Cost: ~$0.02/search vs $0.03-0.10 (15-50x cheaper)
- Real engagement data (score, comments, dates) from API
- No more 429 rate limits on comment enrichment
- Falls back to OpenAI if SCRAPECREATORS_API_KEY missing
- Registered as last30daysbeta for parallel local testing
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- add xiaohongshu/xhs source path via xiaohongshu-mcp HTTP API\n- add Reddit public JSON fallback when OpenAI auth is unavailable\n- update diagnostics/UI rendering for new source availability states\n- harden Xiaohongshu availability probe to reduce false negatives\n- include source status reporting for Xiaohongshu
Pejman Pour-Moezzi