c04bd67922ef6947a9eb7ec598dd7605ca1101cc
4 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Matt Van Horn
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c04bd67922 |
feat: add Digg AI 1000 as an opt-in source (#370)
* 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> |
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|
Trevin Chow
|
b1773be8f3 |
feat(emit): --emit=html for shareable self-contained briefs (#332)
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.
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|
Ilia Alshanetsky
|
2acbf8a869 |
perf: batch store_findings, dedup source_items in O(1), remove dead code (#206)
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.
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Claire Novotny
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72495c1c14 | Restructure as Codex plugin |