Adds a per-run denylist via the existing-but-unused EXCLUDE_SOURCES
config key. Two coupled changes:
1. pipeline.available_sources() filters out any source listed in
config["EXCLUDE_SOURCES"] (comma-separated, case-insensitive,
whitespace-tolerant) before returning.
2. hooks/scripts/check-config.sh "Ready — N sources active" banner
subtracts excluded sources from the ScrapeCreators +3 (Reddit
comments + TikTok + Instagram) so the count matches what the
pipeline actually runs.
Use case: skip TikTok/Instagram on runs where you only want
text-substantive sources, without unsetting SCRAPECREATORS_API_KEY
(which would also kill Reddit comments). The existing INCLUDE_SOURCES
allowlist covers Perplexity opt-in but doesn't cover this denylist case
— tiktok and instagram are added unconditionally when
SCRAPECREATORS_API_KEY is set, with no opt-out short of removing the key.
Tests (tests/test_pipeline_v3.py::TestExcludeSources):
- excludes tiktok+instagram when listed
- no exclusion when env unset or empty string
- case-insensitive + whitespace-tolerant parsing
- works for any source (e.g. EXCLUDE_SOURCES=hackernews), not just SC-backed
* feat(digg): bump POSTS_PER_CLUSTER to 5 and render limit to 3
Match the per-item enrichment cap and inline-display cap used by the
other sources (Reddit, HN, YouTube, TikTok, GitHub all use 5 fetched /
3 displayed). At the previous 3/2 caps the engine routinely truncated
cluster context — a recent run on cli-printing-press lost the Jason
Calacanis quote tweet entirely because the display cut off after Garry
Tan's first two posts.
* feat(digg): rename 'Digg AI 1000' to 'Digg' in user-facing strings
Drop the 'AI 1000' suffix from the footer line, source label, inline
quote attribution ('via Digg'), why_relevant, container, mock title,
SKILL.md source list, and README sources table. Internal code comments
and docstrings still reference the upstream Digg AI 1000 product.
Bumps version to 3.2.1 and adds a CHANGELOG entry covering this rename
and the POSTS_PER_CLUSTER / render-limit bumps from the prior commit.
---------
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
* feat(digg): add Digg AI 1000 source module with cluster search and post enrichment
- search_digg shells out to digg-pp-cli with --since 30d --agent
- parse_digg_response normalizes clusters to last30days dict shape
- enrich_with_top_posts attaches top-ranked X posts to top-K clusters
- shutil.which gate plus subproc.run_with_timeout discipline matches
bird_x.py / youtube_yt.py patterns
25 unit tests cover parse, age window, relevance, binary-missing
fallback, timeout recovery, and partial enrichment failures.
* feat(digg): wire Digg source into pipeline, normalize, signals, and render
pipeline.py:
- Import digg, add to MOCK_AVAILABLE_SOURCES, gate via shutil.which
- Dispatch case calls search_digg + parse_digg_response, runs
enrich_with_top_posts at default/deep depth
- Mock fixture includes one enriched cluster + one bare cluster
normalize.py:
- _normalize_digg maps cluster dicts to SourceItem with
container='Digg AI 1000' and metadata.posts pass-through
signals.py:
- SOURCE_QUALITY['digg'] = 0.85 (top tier alongside YouTube,
reflecting Digg's curatorial layer)
- ENGAGEMENT_WEIGHTS['digg'] balances postCount, uniqueAuthors,
and the rank_score derived from Digg's curatorial position
render.py:
- SOURCE_LABELS['digg'] = 'Digg AI 1000'
- _FOOTER_SOURCES adds '⛏️ Digg AI 1000' line after GitHub
- ENGAGEMENT_DISPLAY mirrors footer keys
- New _digg_posts_for + _format_digg_quote helpers emit inline
'@handle via Digg AI 1000' quotes for clusters with attached X
posts; both compact and full-dump renderers call them
* feat(digg): polish per-item engagement display and progress label
- ENGAGEMENT_DISPLAY for digg uses 'posts' / 'auth' to match the
codebase abbreviation convention (HN: 'pts'/'cmt', X: 'rt'/'re')
- Footer item word changes from 'story' to 'cluster' to dodge the
pre-existing naive plural in _footer_line_for_source ('storys')
and to match Digg's actual data model
- ui.py SOURCE_COMPLETION_META adds digg with correct 'cluster'/
'clusters' plural so 'Research complete' shows 'Digg: N clusters'
* feat(digg): document Digg AI 1000 source in skill, README, and changelog
- planner.py SOURCE_CAPABILITIES adds digg with discussion/social/link
capabilities so the planner offers it through the standard fanout
- SKILL.md ACTIVE_SOURCES_LIST gate includes 'which digg-pp-cli' check
and the source list / available-sources line names digg as opt-in
- README.md Sources table adds the Digg AI 1000 row with the activation
gate so first-time readers see what they get
- CHANGELOG.md Unreleased section calls out the source addition
* fix(digg): enrich post-dedupe so brief survivors carry inline quotes
Pipeline dispatch was attaching X posts to the top-3 items returned by
search, but dedupe later picked different survivors when multiple
clusters compared similar (common for trending topics). The brief
ended up showing clusters with no posts attached even though
enrichment ran successfully on positions 0-2.
Move enrichment to _finalize_items_by_source. The new
digg.enrich_source_items helper reads metadata['clusterUrlId'] and
writes metadata['posts'] in place on the SourceItems that actually
survive dedupe.
Verified live on 'openclaw': 2 surviving clusters, both now carry
real X-post quotes from @sama and @jeremyphoward attributed
'via Digg AI 1000'.
Adds 3 unit tests covering survivor enrichment, non-digg skip, and
clusterUrlId fallback to item_id.
* test(digg): relax live off-topic test to check shape, not emptiness
Digg's live search uses fuzzy/popularity fallback, so an impossible
token can still return some loosely-related clusters. The contract
the pipeline depends on is shape (results is always a list);
token-overlap relevance handles the noise downstream.
---------
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Scoring hot path (_normalize_score_dedupe) re-tokenized the same
ranking_query ~240x per stream: once per item for local_relevance,
plus ~5x per item across snippet windows. Query tokens are immutable
within a stream, so compute them once as relevance.PreparedQuery and
thread through signals.annotate_stream and snippet.extract_best_snippet.
dedupe._PreparedText called normalize_text twice: once in __init__ and
again via get_ngrams. Factor out _ngrams_of_normalized so the prepared
path skips the redundant pass while get_ngrams keeps its public contract.
Behavior unchanged.
Trevin Chow