DO NOT MERGE until local validation passes on 3+ golden topics.
Problem: the magic footer (✅ All agents reported back!, emoji tree,
Top voices, Raw results saved) was composed by the synthesizer model
following a "Copy this EXACTLY" template buried 1150 lines into SKILL.md.
Under context pressure, Opus 4.7 dropped it. Three recent /last30days
runs (Opus 4.7, programming language for AI agents, Kanye West)
produced clean prose with no footer and used AI-slop section headers
(## The launch, ## Where it disappoints) instead of flowing paragraphs.
Fix:
1. render.py: new _render_emoji_footer() emits the deterministic footer
as the final block of every compact output. Zero-count sources are
omitted. Tree characters (├─ / └─) computed from populated-line
count. The model no longer assembles the tree from text instructions.
2. render.py: new _site_name_for_url() and _format_web_line_sources()
map URLs to clean publication names (Later, Buffer, CNN, etc.) so
the 🌐 Web line is pre-assembled by Python.
3. last30days.py: compute_save_path_display() turns the save path into
a ~/-relative string that the engine puts in the footer. Signature
change: emit_output() and render_compact() both accept save_path.
4. SKILL.md synthesis contract rewritten:
- Footer template DELETED. Replaced with instruction to include the
engine footer block verbatim.
- URL-to-site-name sub-block DELETED. Engine does this.
- "Calculate actual totals" paragraph DELETED. Engine does this.
- All em-dashes in the synthesis section replaced with ` - ` (single
hyphen with spaces). Em-dashes are the most reliable AI-slop tell.
- New rules: no ## markdown section headers in response body, no
invented title line like "{Topic}: last 30 days", no bold section
labels acting as headers. Bold-lead-in paragraph shape stays.
- SELF-CHECK updated to verify footer presence, no em-dashes, no
body-level headers.
Tests: 15 new tests covering footer emission, zero-source omission,
tree character placement, save-path threading, URL-to-name helper,
Web line formatting, Top voices combination, Polymarket line.
All 127 tests pass across render, rerank, cluster, briefing, CLI,
internals, fun-scoring.
Plan: docs/plans/2026-04-17-003-feat-deterministic-footer-plan.md
Local validation protocol (blocks merge):
- Run /last30days in a fresh Claude Code window on 5 golden topics
- Verify each output contains the footer block verbatim
- Verify zero ## body headers, zero em-dashes/en-dashes, zero invented
title lines
- Report 5x8 pass/fail matrix; all 40 cells must be green before merge
🤖 Generated with Claude Opus 4.7 (1M context) via [Claude Code](https://claude.com/claude-code) + Compound Engineering v2.63.1
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.
Add Xquik (xquik.com) as a new X/Twitter search source that uses a REST
API with full engagement metrics (likes, retweets, replies, quotes,
views, bookmarks). Uses stdlib urllib only -- no new dependencies.
- scripts/lib/xquik.py: source module with search, parse, query expansion
- tests/test_xquik.py: 32 unit tests covering all functions
- env.py: XQUIK_API_KEY config and availability check
- pipeline.py: source registration and retrieve dispatch
- normalize.py: reuses _normalize_x (same item format as Bird)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
When Bird's JSON response is a raw array instead of an object,
json.loads returns a list. All callers use .get('items') which raises
AttributeError on lists. Wrap list responses in {"items": parsed} so
callers always receive a dict.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* 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>
Matt Van Horn