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.
test_sync_cache_path_uses_skill_version asserts that sync.sh's plugin
cache path includes the version from SKILL.md frontmatter. The frontmatter
moved to 3.2.1 in #371 but sync.sh still pointed at 3.2.0, leaving CI red
on every PR.
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* 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>
* chore(release): v3.2.0
Bumps plugin/marketplace/codex/pyproject versions from 3.1.1 to 3.2.0.
Promotes the Unreleased CHANGELOG entries (--emit=html, Digg AI 1000
source) to the 3.2.0 release section.
* chore(release): bump SKILL.md header and sync.sh path to 3.2.0
---------
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>
Adds a one-command shareable HTML mode to /last30days. The skill detects
HTML intent (explicit --emit=html / --emit:html / --html flag in
$ARGUMENTS, or natural-language asks like "give me a shareable brief",
"for Slack", "export as HTML"), runs the normal research + chat synthesis
flow, then saves a self-contained HTML file to
~/Documents/Last30Days/{topic}-brief.html. The synthesis appears in chat
as usual; the HTML is an additional artifact for sharing.
User experience:
/last30days OpenClaw --emit=html
/last30days OpenClaw, give me an HTML brief for Slack
Synthesis prints to chat. Last line of the response: "📎 Shareable brief
saved to ~/Documents/Last30Days/openclaw-brief.html". Open it, drag it
into a message, browser-print to PDF, email it.
Architecture:
- SKILL.md gets a small detection block (triggers + early exit +
MUST/MUST NOT rules + rationale) that points to a reference file.
- references/save-html-brief.md owns the implementation: capture the
synthesis verbatim into a temp file via heredoc, invoke the engine
with --emit=html --synthesis-file, save to disk, append the
confirmation line to chat.
- lib/render.py exposes render_for_html(report, synthesis_md=None) and
render_for_html_comparison(...) -- clean markdown for HTML
conversion. Omits debug file header, model-facing safety note, and
data quality warnings (those stay in engine stderr; recipients can't
act on them in a shared artifact).
- lib/html_render.py is a new module: ~200-line CSS template (dark
mode default, prefers-color-scheme switch, print stylesheet, mobile
breakpoint), stdlib-regex markdown-to-HTML converter, marker-based
META + engine-footer wrapping, PROSE_LABELS registry promoting plain
-text labels to <h2>, colophon builder.
- last30days.py adds --emit=html argparse choice and --synthesis-file
PATH flag (engine still callable directly without the skill in the
loop).
Design:
- Voice-led research brief, not corporate report. Inter + JetBrains
Mono via Google Fonts with full system fallbacks (no FOIT, works
offline). Brand purple #a855f7 (#7c3aed in light mode). Type ramp:
body 17px/400/muted, bold lead-in 17px/600/fg, h2 + .prose-label
20px/600/fg, monospace badge/meta/footer/colophon at 13-13.5px.
- 720px max-width, generous whitespace, no card layouts or shadows.
- Print stylesheet: light theme, A4 margins, [href]::after URL
footnotes, page-break-inside:avoid on the engine footer.
Templated (locked) shell:
- HTML5 boilerplate, Google Fonts <link> with preconnect, all CSS
inline.
- .badge / .meta / .engine-footer / .colophon containers.
Flexible (role-based):
- <h2> rendering covers BOTH plain ## headers (comparison mode per
LAW 4 exception) AND promoted prose labels via PROSE_LABELS
registry. Adding a new SKILL.md prose label is a one-line tuple
addition; no CSS or template changes.
- Marker-based engine boundaries (<!-- META: ... -->,
<!-- PASS-THROUGH FOOTER -->) survive the markdown converter and
get promoted post-conversion. Robust to engine output format
changes.
- Generic markdown-to-HTML for body content; future SKILL.md additions
(new sections, tables, blockquotes) render correctly without code
changes.
Tests: 30 new tests in tests/test_html_render.py covering snapshots
(rich/thin/comparison), CLI parsing, --synthesis-file end-to-end, prose
label promotion, warning exclusion from artifact, parseability via
html.parser, no-script self-containment.
No SKILL.md voice contract changes, no LAWs 1-8 changes, no new pip
dependencies, no JavaScript anywhere.
is_available() only caught FileNotFoundError and TimeoutExpired. On WSL,
a /mnt/c/.../WindowsApps entry on $PATH returns EACCES during exec, and
Python raises PermissionError. That escaped is_available() and crashed
pipeline.diagnose() before any source ran.
Catch OSError instead. It covers FileNotFoundError, PermissionError, and
any other spawn-time OS error, so a non-executable xurl on PATH falls
through to the next backend instead of aborting the run.
bird_x.py and youtube_yt.py had four near-identical copies of the same
subprocess cleanup dance (Popen + os.setsid + communicate(timeout) +
SIGTERM via killpg + proc.kill() fallback + wait(5)). Extract to
lib.subproc.run_with_timeout(), which:
- runs the child in its own process group via os.setsid where available
- raises SubprocTimeout on timeout
- on timeout: SIGTERM the group, fall back to proc.kill(), wait up to 5s
- accepts an on_pid callback so bird_x can still register child PIDs
with last30days.register_child_pid for whole-process cleanup
- captures stdout/stderr as strings in a SubprocResult dataclass
Migrated call sites: _run_bird_search, search_handles inner worker,
search_youtube, fetch_transcript. With the helper in place, the signal
and subprocess imports became dead in both files (plus os in
youtube_yt) and went with them.
Tests: 9 new subproc tests cover success, non-zero exit, stderr capture,
timeout-raises, timeout-kills-group, missing-command, env passthrough,
PID callback, and callback-exception suppression. test_env_v3 and
test_youtube_yt patch subproc.run_with_timeout instead of the removed
bird_x.subprocess and yt-dlp subprocess.
1. N+1 queries in store.store_findings()
The old loop ran one SELECT per finding to check existence, then one
INSERT or UPDATE. 100 findings cost 200 serial SQLite roundtrips.
Now: one batch SELECT with WHERE source_url IN (...) builds a lookup
dict, then executemany() handles all inserts and updates. Query count
stays constant regardless of batch size. Benchmark on 500 findings:
~30ms to ~20ms; gap widens on slower storage.
2. O(n^2) source_items dedup in fusion.weighted_rrf()
Merging an item into an existing candidate ran any(existing.source ==
... for existing in candidate.source_items), linearly scanning a list
that grew with each merge. At 40 candidates with 20 source_items each,
fusion went quadratic. Now tracks (source, item_id) tuples in a
per-candidate set for O(1) lookup. The source_items list itself is
unchanged since other code iterates it.
3. Dead code removal
- providers.GeminiClient.ground_search() and .url_context_json(): zero
callers. Deleted.
- render._top_comment_excerpt(): zero callers. Deleted.
- env.is_reddit_available(): one-line wrapper around get_reddit_source.
Callers can check get_reddit_source(config) is not None directly.
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.
_normalize_reddit, _normalize_hackernews, and _normalize_github inlined
the same 5-line comprehension to stringify and space-join the first 3
top_comments' excerpt field. Extract one helper, call it from all three.
The comment field name varies per source (Reddit/GitHub use 'excerpt',
HN uses 'text'), so it's passed as a parameter. Behavior unchanged.
Trevin Chow