* feat(resolve): category-peer subreddit map for Step 0.55
Introduces scripts/lib/categories.py with a curated category->peer-subs
map and wires scripts/lib/resolve.py auto_resolve() to merge peers into
the WebSearch-extracted subreddit list. Named 2026-04-22 failure mode:
a "Prompting GPT Image 2" run resolved only r/OpenAI + r/ChatGPT and
missed r/StableDiffusion, r/midjourney, r/dalle2, r/aiArt where
prompting techniques actually live.
Map is static, curated, ~11 categories (ai_image_generation,
ai_video_generation, ai_music_generation, ai_coding_agent,
ai_agent_framework, ai_chat_model, saas_screen_recording,
saas_productivity, prediction_markets, crypto_defi, dev_tool_cli).
First-match-wins ordering from most-specific to least-specific.
Compound-term patterns only (no bare common nouns like "image", "ai").
auto_resolve now:
- calls detect_category(topic) after _extract_subreddits
- merges peer_subs case-insensitively, caps at MAX_SUBS (10)
- preserves every WebSearch-returned sub (freshest signal)
- emits [Resolve] Matched category=<id>, adding peers: <list> on stderr
only when peers were actually added
- returns new "category" key in the result dict for observability
- wraps classifier in try/except so failures degrade to unwidened list
Includes drive-by: test_full_resolve / test_partial_failure
searches_run expectations bumped from 3->4 / 2->3 to match the current
queries dict (subreddit + news + x_handle + github).
* feat(skill): Step 0.55 category-peer expansion and self-check
Adds Section 2a (category-peer expansion, MANDATORY for product topics)
and the Step 0.55 self-check checkpoint that fires immediately before
the Resolved block displays. Structural mirror of the engine-side
categories.py map: same categories, same peer subs, same priority
order.
The model-side path now:
- Applies category-peer expansion to the WebSearch-resolved subs on
every product-in-a-known-category run.
- Emits the (+ <category_id> peers) annotation on the Reddit line of
the Resolved block as the observable contract. Absence on a
product-in-a-known-category topic is a Step 0.55 regression.
- Runs a self-check before emitting Resolved: "does the resolved list
include at least 2 peer subs for the matched category? if not,
widen NOW and do not run the engine yet."
Mirror of the Python map lives inside Step 0.55 as a table for the
model to pattern-match against; extrapolation to unlisted categories
is explicitly allowed. Worked example (the exact failing query)
appears below the table so reviewers can see before/after at a glance.
Both changes land inside the existing Step 0.55 block. No new
top-level section, no new LAW. LAWs 1-6 wording unchanged.
* test: end-to-end regression for GPT Image 2 failure mode
Stubs grounding.web_search to return the OpenAI-only subs that caused
the 2026-04-22 failure, then asserts that auto_resolve widens to
include the image-gen peers and emits the [Resolve] Matched
category=ai_image_generation stderr line. Covers the cap boundary
and the uncategorized-topic no-op path.
Fixture tests/fixtures/prompting-gpt-image-2-resolved-block.md is
documentation-grade (not parsed by tests) and shows the pre-fix vs
post-fix Resolved block shape so reviewers can evaluate future
categories.py edits against the original bug.
---------
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Reddit, TikTok, YouTube, Instagram, Bluesky, X and Threads top comments
now render as u/author or @handle in the evidence block, instead of the
generic "Comment (...)" label. The enrichment adapters already captured
author; only the render layer was dropping it.
Also fixes the TikTok adapter to prefer user.unique_id (the @handle) over
user.nickname (display name) so attribution round-trips to a profile URL.
Legacy "Comment (...)" shape is preserved when author is empty, [deleted],
or [removed].
Bumps to 3.0.10.
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Adds xurl (https://github.com/openclaw/xurl) as a third X search
backend, sitting after xAI API and Bird/GraphQL in the priority chain.
xurl uses the official X API v2 with OAuth2+PKCE authentication,
requiring only a free X Developer App. It auto-refreshes tokens and
works reliably as a stable fallback when xAI API key or browser
cookies are not available.
Limitations:
- X API search/recent returns last 7 days only (vs Bird's full archive)
- No AI-powered relevance scoring (uses token_overlap_relevance instead)
- Free tier: 180 requests per 15-minute window
New files:
- scripts/lib/xurl_x.py: xurl CLI wrapper with search + parse
- tests/test_xurl_x.py: 30 unit tests (all passing)
Modified files:
- scripts/lib/env.py: detect xurl in get_x_source_with_method(),
get_missing_keys(), and get_x_source_status()
- scripts/last30days.py: add xurl_x import and xurl branch in
_search_x() priority chain
- SKILL.md: document xurl setup option
PR #285 introduced the stderr warning "No --plan and no LLM provider
configured. Using deterministic fallback..." The 2026-04-19 Run 1
agent self-debug said it read that as "I don't have a key, I can't do
LLM stuff, I have to accept fallback" - which is the exact wrong
mental model. The word "provider" referred to the engine's INTERNAL
planner credentials, but the agent parsed it as "I need credentials
to plan at all."
Rewritten to say plainly: YOU are the reasoning model hosting this
skill (Claude Code, Codex, Hermes, Gemini, or any agent runtime);
YOU ARE the planner; you do not need an API key or credentials - you
ARE the LLM. The --plan flag exists precisely so a reasoning model
generates its own plan upstream and passes it to the engine. The
deterministic fallback is the headless/cron path only.
Runtime enumeration is explicit so agents on every supported runtime
recognize themselves - this skill ships to Claude Code, Codex, Hermes,
and ~/.agents via sync.sh.
Tests: updated test_fallback_logs_warning_when_no_provider to assert
the new language (YOU ARE the planner, runtime names present) and
assert the old misleading phrasing is absent. Renamed the companion
test for clarity.
PR #285's entity grounding checked only title + snippet. That missed:
- YouTube videos where the entity is mentioned in transcript but not
in title (false demotion of on-topic content)
- Reddit posts where the entity is in top comments but not in title
(false demotion of on-topic discussion)
And it also wasn't strong enough to reliably demote items like the
2026-04-19 Nate Herk "Managed Agents" video - which had no Hermes
anywhere - because the -25 penalty on rerank_score composed to only
-15 on final_score via the 0.60 weight, and engagement bonus partially
offset that.
Two fixes:
1. _candidate_haystack() now joins title + snippet +
metadata[transcript_snippet] + metadata[transcript_highlights] +
metadata[top_comments][*].excerpt/text + metadata[comment_insights].
Catches entity mentions wherever they actually live. Guarded with
isinstance checks so malformed metadata doesn't raise.
2. ENTITY_MISS_FINAL_PENALTY (20.0) applied directly in _final_score
when candidate.explanation contains "entity-miss". This lands the
full penalty weight on the composite signal that cluster-scoring
consumes, instead of being diluted by the rerank_score weight.
Combined effect: entity-miss gap grows from ~15 to ~35 points.
Tests: 8 new scenarios covering transcript match, transcript highlight
match, top-comment match, comment-insight match, empty-text skip,
no-primary-entity no-op, and the dual-penalty composition check.
The stderr [Planner] warning from PR #285 doesn't reach the user because
Claude and other reasoning agents hide stderr from their synthesis. The
2026-04-19 Hermes Agent Use Cases Run 1 produced source=deterministic
and the user never saw it.
Adds a user-visible stdout block that the model's LAW 5 pass-through
contract forces into the response. Fires only when plan_source is
deterministic AND no pre-research flags were passed AND the topic is
pre-research-eligible (named entity). Cron jobs on abstract topics
don't trigger it.
Position: BEFORE the EVIDENCE FOR SYNTHESIS envelope so the model sees
it as the first non-badge content. Wrapped in a new USER-VISIBLE BANNER
envelope matching the EVIDENCE/PASS-THROUGH envelope pattern from Unit 1
of PR #285.
Runtime-agnostic language: explicitly enumerates Claude Code, Codex,
Hermes, Gemini so the hosting reasoning model recognizes itself
regardless of runtime.
pipeline.py now persists plan_source to report.artifacts so the
renderer can consume it. Adds 7 tests covering fire conditions,
suppression conditions (external/llm plan source, flags present,
abstract topic), and correct position relative to the evidence envelope.
The prior pipeline.py only logged the planner outcome when an external
--plan was passed ("[Planner] Using external plan (N subqueries)").
The internal LLM planner and the deterministic fallback ran silently,
so retrieval-breadth failures were invisible without --debug.
After plan finalization, emit a unified trace:
[Planner] Plan: intent=X, freshness=Y, cluster_mode=Z, subqueries=N, source=external|llm|deterministic
[Planner] sq1 label=... search="..." sources=[...]
[Planner] sq2 ...
Stderr only; does not touch the user-facing stdout synthesis. The
source= annotation distinguishes --plan (external), provider-backed
(llm), and deterministic paths — so when the 2026-04-19 Hermes Agent
Use Cases failure mode recurs, the trace tells the user which path ran
and what subqueries it produced.
Tests: added test_planner_trace_always_fires_on_mock_run which captures
stderr on a mock pipeline run and asserts the summary + per-subquery
lines appear.
The 2026-04-19 Hermes Agent Use Cases run had a Nate Herk YouTube video
titled "I Tested Claude's New Managed Agents" score 51 and rank #2
with zero Hermes content. The reranker had intent-specific scoring hints
but no entity-grounding check, so topic-vicinity matches (one offhand
OpenClaw mention) drifted to the top.
Add _primary_entity(topic) that strips intent-modifier suffixes ("use
cases", "workflows", etc.) so "Hermes Agent use cases" yields
primary_entity="Hermes Agent". Pass the entity through to both the LLM
and fallback scoring paths.
Fallback path: if primary_entity is not found (case-insensitive) in
title + snippet, subtract ENTITY_MISS_PENALTY (25 pts). Skip the
demotion for candidates with no text at all (image-only TikToks etc.)
to avoid false negatives on thin-text sources.
LLM path: add a "Primary entity grounding" hint to _build_prompt when
primary_entity is non-empty. Instructs the LLM to score candidates
without the entity at <=30.
Tests: 24 rerank tests pass, including 8 new entity-grounding tests.
Topics with suffixes like "use cases", "workflows", "review",
"examples" were previously echoed near-verbatim into search_query,
returning near-zero matches because nobody posts the literal phrase
(2026-04-19 Hermes Agent Use Cases failure).
Unit 2 — planner breadth:
1. Planner prompt rule: STRIP intent-modifier phrases from search_query
(keep them in ranking_query). Paraphrase across 4-5 subqueries that
each express the intent differently.
2. Planner prompt rule: quote only multi-word proper nouns like
"Hermes Agent", not the user's full topic.
3. Raise _max_subqueries cap from 3 to 5 for how_to / opinion / product /
breaking_news / prediction. Comparison stays at 4; factual / concept
stay at 2 unless the topic carries an intent modifier.
4. Deterministic fallback: when intent is non-{comparison,prediction}
and topic contains an intent modifier, append 3 paraphrased
subqueries (workflows, production, experience).
Unit 3 — deterministic fallback defaults:
5. _infer_intent default changed from "breaking_news" to "concept".
Prior default forced strict_recent freshness on unclassified topics,
biasing against older relevant material. Recency-signal regexes
("trending", "this week", etc.) added above the default so genuinely
time-sensitive topics still classify correctly.
6. _keyword_query now quotes only title-cased multi-word proper nouns
("Hermes Agent", "Claude Code"), not the user's full typed topic.
Hyphenated compounds and lowercase terms are left as bare keywords
so platform tokenizers broaden rather than narrow retrieval.
7. New stderr warning when plan_query runs with no --plan and no LLM
provider: surfaces that the deterministic fallback path is weaker
than the --plan-from-Claude-Code path, so callers know to generate
and pass a plan.
Tests: 37 planner tests pass, including 11 intent-modifier and 7
fallback-defaults tests.
The engine's ## Ranked Evidence Clusters block is a scratchpad for the
model to read, not user-facing output. Two consecutive /last30days runs
on 2026-04-19 (Hermes Agent Use Cases) dumped it verbatim as user output
because the prior canonical-boundary text (Pass through the lines ABOVE
this boundary verbatim) was ambiguous about scope.
Split render_compact stdout into two bounded blocks:
- <!-- EVIDENCE FOR SYNTHESIS: ... --> wraps Ranked Evidence Clusters,
Stats, and Source Coverage. Transform into prose per LAW 2.
- <!-- PASS-THROUGH FOOTER: ... --> wraps the emoji-tree footer only.
Emit verbatim per LAW 5.
Rewrite _render_canonical_boundary to scope pass-through to the footer
block explicitly and give the model a concrete self-check string
(### 1. followed by a score tuple) as the named LAW 6 failure signal.
Add LAW 6 to SKILL.md OUTPUT CONTRACT with the observed violation
(2026-04-19 Hermes Agent Use Cases) and a worked transformation example.
Five Opus 4.7 self-debugs on v3.0.8 (3 passing, 2 failing runs) converged
on four fixes:
1. Engine refuses Class 1 demographic-shopping queries at main() front-door.
Birthday-gift failure mode becomes structurally impossible - the pipeline
never runs on a doomed query. Exit code 2 with a REFUSE message on stderr
pointing the model to ask for hobbies/relationship/budget. Escape hatch:
LAST30DAYS_SKIP_PREFLIGHT=1 for "just run it" overrides.
2. Delete stale `.agents/skills/last30days/SKILL.md` (1382 lines, April 13
snapshot) and `.hermes-plugin/SKILL.md` (269 lines, April 13 snapshot).
Peter Steinberger's self-debug named the first file as the one it read
instead of the real SKILL.md. One SKILL.md per plugin, at the plugin root.
Sync script simplified: Hermes now always uses main SKILL.md.
3. render_compact() appends an explicit END-OF-CANONICAL-OUTPUT boundary
with pass-through instruction. The model had the canonical body in its
buffer on the Peter run and discarded it; the boundary makes pass-through
the path of least resistance.
4. LAW 1 gains a verbatim-pattern override clause naming the exact WebSearch
tool-result reminder ("CRITICAL REQUIREMENT: MUST include Sources:
section") that caused Peter's trailing Sources leak. No more ambiguity
at synthesis time.
Tests: tests/test_preflight.py, 29 scenarios covering Class 1 matches
(birthday gift, best-for-demographic, what-to-buy-relationship), qualifier
skips (budget, hobbies, activity after year-old), and the REFUSE message
shape.
Validation gate before merging to main: re-run the 5 debug topics
(Peter Steinberger, birthday gift for 40 year old, Kanye West, Garry Tan,
OpenClaw vs Paperclip vs Hermes) on v3.0.9 and confirm 5/5 canonical
compliance. Rollback to v3.0.8 if any previously-passing topic regresses.
Plan: docs/plans/2026-04-18-015-fix-engine-refuse-keyword-traps-delete-stale-skillmd-files-plan.md
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three independent Opus 4.7 self-debugs on 2026-04-18 converged on the same
root cause of the v3.0.6/v3.0.7 canonical-compliance regression: SKILL.md is
42,860 tokens / 1,478 lines, LAWs lived at line 1094+, every realistic reading
strategy failed to reach them before synthesis.
Unit 1 - Moved the BADGE MANDATORY block and VOICE CONTRACT LAW 1-5 (plus
the formatting-authority preface) from line ~1090 to line ~75 (right after
the SKILL CONTRACT preface, before HOW TO INVOKE THIS SKILL). Every reading
strategy now lands the LAWs in active context before synthesis.
Unit 2 - Engine now emits the badge as the first line of --emit=compact
stdout. Passing through the script output becomes the default-correct
behavior; emitting the badge no longer depends on model compliance. Reads
version from .claude-plugin/plugin.json at runtime with graceful fallback.
Unit 3 - Deleted skills/last30days/SKILL.md stub (231-line v3-spec file).
This was the wrong-file-capture hazard Ron Conway's self-debug identified:
model grabbed the first SKILL.md find surfaced and treated it as
authoritative. Only ONE SKILL.md in the plugin package now.
Diagnoses verbatim:
- Kanye thread: "I read lines 1-600 in chunks, jumped to 300-899, then
stopped. File is 1478 lines. I never saw past ~900."
- Peter thread: "I tried Read once, hit the 25K token cap on a 42,860-token
file, and bailed instead of chunked-reading with offset/limit. I never
opened SKILL.md at all."
- Ron Conway thread: "I read one SKILL.md (231 lines)... the v3 spec stub.
I never opened the operational SKILL.md sitting next to the script."
Validation: direct engine invocation confirms badge at line 1 of compact
output. Module imports clean.
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Consolidates seven beta-validated plans into the public release. Validated
on nine+ topics across GENERAL, COMPARISON, RECOMMENDATIONS, and
demographic-shopping classes before ship.
Plans bundled in this release:
- 003 Engine-emitted Pre-Research Status warning + Polymarket summarization
+ VOICE CONTRACT LAW 1-5 + Step 0.55 MANDATORY
- 004 WebSearch deferred-tool loading (ToolSearch STEP 0) + LAW 5 universal
+ top-of-file imperative
- 005 Supplement floor (2-3 minimum) separate from Step 0.55 pre-research
- 006 Step 2.5 MANDATORY raw-file append with canonical format example +
count-equality self-check
- 007 Restored April 9 canonical comparison template with Quick Verdict,
per-entity Strengths/Weaknesses, 9-axis Head-to-Head, Bottom Line,
emerging stack + LAW 2/4 COMPARISON exceptions
- 008 Person-topic GitHub handle resolution MANDATORY + LAW 1 reinforcement
at Step 2 tail and Step 2.5 entry + RECOMMENDATIONS signal-weighted
ranking rewrite + Polymarket post-merge topic filter (engine change,
filter_items_against_topic helper + vs/versus in _NOISE_WORDS)
- 009 Unified pre-flight CHECKLIST + VOICE CONTRACT formatting-authority
preface + Step 0.45 Query Quality Pre-Flight (4 keyword-trap classes) +
post-synthesis Sources-block self-check
Beta validation topics (2026-04-18): Kanye West, Matt Van Horn, CLI vs MCP,
OpenClaw vs Paperclip vs Hermes, Paperclip vs Hermes vs Open Claw, Garry
Tan, Israel vs Lebanon, Best programming language for AI agents, Peter
Steinberger post plan 009, Birthday gift for 42 year old man (Class 1
pre-flight fired correctly), Vincent Koc (passed).
No breaking changes. No new CLI flags. No new public API. Plugin name
(last30days) and marketplace name (last30days-skill) unchanged.
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- scripts/compare.sh now runs /last30days vs /last30days-beta (was
/last30days vs /last30days-3:last30days-skill-private which was a stale
private install name that no longer works)
- CLAUDE.md adds a Beta channel section pointing at mvanhorn/last30days-skill-private
so future agent sessions discover the two-skill layout on project load
No runtime impact on /last30days. Engine code unchanged.
Plan: docs/plans/2026-04-17-005-feat-beta-skill-from-private-repo-plan.md
(plan file is gitignored per PR #259, not included in this diff)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Most users never touch FUN_LEVEL. Default medium was shipping a stats
block but rarely a Best Takes block, and when it did it was below the
cluster fold where a synthesizing model had already stopped reading.
A 2,304-upvote Reddit comment ("WHAT?! I reached my monthly limit
just reading this post") on the 2026-04-17 Opus 4.7 run sat inside
cluster 11 and never made it into synthesis. Four coordinated changes:
1. render: promote Best Takes above the cluster list so the synthesizer
sees comedy before it anchors on cluster 1.
2. render: lower medium threshold from 70 to 55 (heuristic maxes at 80),
drop the two-gem floor to one-gem. Default now reliably emits the
block on typical runs.
3. rerank: score individual top_comments by upvote ratio to their parent
thread. A 2,304-upvote comment on a 300-upvote thread now outranks a
400-upvote comment on a 3,400-upvote thread, which is the viral-wit
signal. Handles both the LLM scoring path and the heuristic fallback.
4. render: merge scored comment gems into Best Takes alongside candidate
gems, sorted together. Comment lines show body + parent title +
r/subreddit or @handle + absolute upvotes.
5. SKILL: tell the synthesizer to quote at least two Best Takes entries
verbatim, with an example of the new comment format.
Plan: docs/plans/2026-04-17-001-feat-default-fun-surfacing-plan.md
🤖 Generated with Claude Opus 4.7 (1M context) via [Claude Code](https://claude.com/claude-code) + Compound Engineering v2.56.1
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Support comma-separated API keys in SCRAPECREATORS_API_KEY with random
selection per run, distributing load across multiple free-tier accounts.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PR #260 wired YouTube comment enrichment against
`/v1/youtube/video/comments` with `id=<video_id>`, but the endpoint
requires `url=https://www.youtube.com/watch?v=<video_id>`. Every enrich
call was returning 400 "missing_parameter: you must provide a url", so
no YouTube items ever carried `top_comments`.
The SC transcript fallback (`_sc_fetch_transcript`) had the identical
contract mistake. It was latent because `_fetch_transcript` prefers
yt-dlp and the SC path only fires when yt-dlp is missing, but it would
have failed the same way on hosts without yt-dlp installed.
Switching both callers to `url=` surfaces a second issue in the
response parser: SC returns `author` as `{"name": "@handle", ...}` and
nests like counts under `engagement.likes`, not top-level. The parser
was reading `author` as a string and missing the nested likes, so even
after the param fix every comment would land with an object-shaped
author and 0 likes.
- `_fetch_video_comments`: send `url=` on both urllib and requests branches
- `_sc_fetch_transcript`: same
- Response parser: extract `author.name` when author is a dict, read
`engagement.likes` when top-level `likes` is absent, prefer
`publishedTime` / `publishedTimeText` for date. Legacy string-author
and top-level-likes shapes still work, so existing mocks are unchanged.
Verified live against api.scrapecreators.com: `_fetch_video_comments`
now returns fully-populated comments with real @handles and like
counts (e.g. "@JennyNicholson: ... (49000 likes, 2025-04-15)"). All
tests in youtube_yt/normalize/signals/render pass.
Plan: docs/plans/2026-04-15-002-fix-youtube-comments-scrapecreators-param-plan.md
🤖 Generated with Claude Opus 4.6 (1M context) via [Claude Code](https://claude.com/claude-code) + Compound Engineering v2.56.1
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
v3.0.3's fix (#262) restored skills/ and .claude-plugin/ to the git
archive, which Claude Code needs for /plugin install. But
scripts/build-skill.sh uses the same archive to produce the claude.ai
.skill bundle, which must contain exactly one root SKILL.md and stay
under the 200-file cap.
Fix: after git archive, 'zip -d' strips both directories from the
.skill bundle. git archive output is unchanged (Claude Code still
gets the full tarball on /plugin install).
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
* feat(normalize): pass YouTube top_comments through with Reddit-compatible shape
_normalize_youtube silently dropped top_comments after enrich_with_comments
populated them, so the downstream signals/render/entity layers never saw
YouTube comments. Map likes->score and text->excerpt so the existing
Reddit-compatible readers Just Work.
Shared _remap_comments helper will be reused for TikTok in a later commit.
* feat(tiktok): fetch top comments via ScrapeCreators when opted in
Mirrors the youtube_comments pattern: new env.is_tiktok_comments_available
gate (requires SCRAPECREATORS_API_KEY + tiktok_comments in INCLUDE_SOURCES),
tiktok.enrich_with_comments ranks posts and fetches via
GET /v1/tiktok/video/comments. Vote field is digg_count; text and user.nickname
come across verbatim. Pipeline calls the enricher right after TikTok search
when the gate is open.
Comment-fetch errors never crash the pipeline — the enricher returns an
empty list on 4xx/5xx.
* feat(normalize): pass TikTok top_comments through with digg_count->score mapping
Instagram uses the same shortform normalizer and has no comment fetcher
today, so the key is harmlessly absent there — no Instagram regression.
* feat(signals): add YouTube + TikTok top-comment score to engagement formula
Mirrors Reddit's 10% top-comment slot. Without top_comments present, the
formula reduces to views-dominant weighting; with a high-signal comment,
the item gets a meaningful bump (log1p(10k) ~ 9.2, weighted 0.10 = ~0.92
on the engagement score).
Updated the existing dominant-weight and missing-fields tests to the new
weights (0.45/0.32/0.13 for YT, 0.45/0.27/0.18 for TT). Views still dominate.
* feat(render): source-aware thresholds and vote labels for top comments
10 upvotes on Reddit signals community interest; 10 likes on a viral
TikTok is noise. Introduce per-source minimums (reddit 10, youtube 50,
tiktok 500) and native vote labels ('upvotes' for Reddit, 'likes' for
YT/TT). First-pass numbers — tune after live observation.
* docs: generalize top-comment quoting to YouTube + TikTok, add tiktok_comments opt-in
Synthesis instructions previously called out Reddit top comments only.
Now cover Reddit/YouTube/TikTok uniformly with source-appropriate vote
labels (upvotes vs likes), and explicitly frame YT transcript highlights
and comments as complementary signals. README and setup-wizard copy
document the new tiktok_comments INCLUDE_SOURCES token.
---------
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Wraps git archive with --prefix=last30days/ so the zip contains a single
top-level skill folder matching SKILL.md's name: frontmatter. Enforces:
- refuses to build with a dirty working tree (prevents shipping untracked changes)
- fails if zip exceeds 200 files (claude.ai's empirical upload cap)
- fails if zip contains more than one SKILL.md (avoids name: confusion)
Output at dist/last30days.skill (gitignored).
Six source modules each defined an identical 8-line _sc_headers(token)
function returning {"x-api-key": token, "Content-Type": "application/json"}.
Moved it to http.scrapecreators_headers() and migrated all 33 call sites.
Affected files: reddit.py, threads.py, tiktok.py, instagram.py, pinterest.py,
youtube_yt.py. Zero per-source variation, zero behavior change.
Net: -40 lines. 1022 tests pass (15 pre-existing failures unchanged).
Live smoke test: reddit search returns 12 threads with full engagement.
When a tweet has no engagement metrics, _first_of() returns None for
every key, producing {"likes": None, "reposts": None, ...}. This
all-None dict propagates to signals.py where it is treated as "data
exists but is zero" rather than "no data available." Return None
instead when every engagement field is missing.
Makes the `variants/open/` sync steps in `scripts/sync.sh` conditional on
the directory actually existing in the source tree. The script is shared
between the public and private repos of last30days-skill, but the OpenClaw
variant only lives in the private repo (it's sanitized via
`strip_for_openclaw.py` and published separately to ClawhHub). When the
script runs from a checkout of the public repo, the variants/open paths
don't exist and the unconditional `rsync` and `sync_target` calls error
out immediately.
Changes:
- `sync_target()` now only creates `variants/open/references` and rsyncs
`variants/open/` when `$SRC/variants/open` exists.
- The trailing `sync_target "$OPENCLAW_TARGET" ...` call is now gated by
the same check, with an explanatory skip message when the directory is
absent.
No behavior change when running from the private repo (which has
`variants/open/`). When running from the public repo, the script now
completes its COMMON_TARGETS loop without erroring.
This also closes out the confusion from PR #211, where a contributor saw
the broken `variants/open/` reference and tried to add the variant back
to the public repo. The real fix was making the script tolerate the
absence, not recreating the directory.
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
github.py _parse_date used naive string slicing (return iso_str[:10])
which accepted any 10+ character string as a "date." For input
"hello world" it returned "hello worl". Now delegates to
dates.parse_date() which validates the format and returns None for
non-dates.
Also migrated reddit.py and threads.py _parse_date to the shared
dates.parse_date(). Both previously reimplemented ISO-with-trailing-
offset handling (the .replace("Z", "+00:00") dance) and reddit.py
also had its own Unix timestamp branch. dates.parse_date() already
handles all of this, including the +0000 no-colon variant Reddit emits.
Preserved reddit.py's original falsy-check so 0 still returns None
(epoch 0 would otherwise parse as "1970-01-01", breaking an existing
test and changing long-standing behavior).
Added 4 new github tests for garbage rejection and offset variants.
All 1026 existing tests pass (15 pre-existing failures unchanged).
Added params kwarg to http.request()/http.get() that urlencodes a dict
into the query string. None values are dropped, ints and bools are
stringified, and params append correctly if the URL already has a
query string.
Migrated reddit.py to use this helper for all three ScrapeCreators
call sites (global search, subreddit search, post comments). Deleted
the try/import requests/except ImportError fallback and the paired
if not _requests: / else: branches. Six new http tests cover the
params-encoding behavior.
Net: reddit.py -70 lines. Behavior is identical - the existing http.py
urllib implementation already had retry logic, 429 handling, and
HTTPError types that are strictly better than the ad-hoc requests
branches we deleted.
99 reddit tests pass. Live smoke test on a real ScrapeCreators run
returned 12 threads with the same engagement data as before.
The module-level _cached_token was set once and never refreshed. AT
Protocol tokens expire after ~2 hours, causing silent 401 errors in
long-running watchlist cron sessions. Adds a 90-minute expiry check
using time.monotonic() and logs re-authentication.
Fixes#92
Add column whitelists to prevent SQL injection via kwargs keys in
dynamic UPDATE queries. Values were already parameterized but column
names were string-interpolated directly from kwargs.
Fixes#90
The dedup hot path recomputed normalize_text() 4 times per comparison
and recomputed item_text() on every inner-loop iteration. Pre-computing
n-gram sets and token sets into a _PreparedText cache cuts dedup time
by 6x (2.16s to 0.39s on 300 unique items).
Bird handle searches spawned one Node process per handle sequentially.
Now uses ThreadPoolExecutor so N handles run concurrently. Same pattern
applied to YouTube comment enrichment (was serial, Reddit was already
parallel) and the retry-thin-sources phase in the pipeline.
Clustering now pre-computes candidate text and uses prepared_similarity
for the O(n^2) grouping and MMR representative selection loops.
Minor: _is_wsl() cached with lru_cache, Bundle.add_items() uses
extend() instead of list concatenation.
End-to-end: 5.2s -> 3.7s (29% faster) on a typical 4-source query.
On WSL2, native Linux Firefox typically has no x.com cookies since users
browse in Windows. Chromium browsers (Edge, Chrome, Brave) encrypt cookies
with DPAPI/app-bound encryption, making them inaccessible without admin
privileges. Windows Firefox stores cookies unencrypted in SQLite, readable
directly through the /mnt/c mount.
The cookie extractor now detects WSL2 via /proc/version, locates Windows
Firefox profiles under /mnt/c/Users/*/AppData/Roaming/Mozilla/Firefox,
and falls back to them when Linux Firefox yields no results. Reports
source as "firefox-wsl" to distinguish from native.
Also fixes profile resolution priority: Install* sections (Firefox >= 67)
now take precedence over the legacy Default=1 flag, which could select a
stale profile on multi-profile installations.
Matt Van Horn