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>
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>
* 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>
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.
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
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>
TRANSCRIPT_MAX_WORDS raised from 500 to 5000 so the LLM gets the full
content of most videos (up to ~25 minutes). Removed the second 200-char
truncation in render.py that was reducing transcripts to a single sentence
before the judge agent ever saw them.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When Cloudflare blocks requests to bsky.social or public.api.bsky.app
with a 403, the error was swallowed by a generic except clause and
reported as "Bluesky auth failed" - misleading users into thinking
their credentials were wrong.
Now _create_session() preserves the specific error in _session_error,
and search_bluesky() surfaces it. Cloudflare 403s get a clear message
about network-level blocks. Actual 401s say "Invalid credentials".
Closes#69
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Map prompt-oriented product searches and animation-oriented build searches away from the breaking-news default so source tiering and tiebreakers align with the benchmark topics.
Validation: uv run python -m unittest tests.test_query_type
This workspace uses GOOGLE_API_KEY as the canonical Google credential. Accept it ahead of the Gemini-specific aliases so the local evaluation harness can run without a separate GEMINI_API_KEY export.
Validation: uv run python -m unittest tests.test_env_project tests.test_evaluate_search_quality and a one-shot keychain-backed resolution check.
Add an optional local evaluator that compares a baseline revision against a candidate checkout, computes deterministic stability metrics, and can call Gemini for judged ranking metrics when configured.
The harness isolates child runs with a temporary HOME and a node-free PATH so historical revisions cannot trigger Bird browser-cookie auth during evaluation.
Validation: uv run python -m unittest and local smoke/full deterministic eval runs.
Score against original user intent on Reddit, remove the artificial low-end relevance floor, and make Polymarket semantics dominate generic market quality signals.
Also apply the relevance filter to Polymarket and update the affected cross-source tests.
Validation: uv run python -m unittest
Phase-2 Bird handle searches were still spawning Node without the injected AUTH_TOKEN/CT0 env. That left the search pipeline vulnerable to Chrome keychain prompts whenever a query drilled into X handles.
Pass the popup-safe subprocess env through those handle searches and cover it with a regression test.
Classify prompting and animation queries as how_to so the stack does not treat them as generic breaking news. Also keep X available for how_to and preserve YouTube/HN coverage for breaking-news and prediction queries.
Validated with uv run python -m unittest tests.test_query_type and the five-query local comparison run used for PR #65 review.
Update README, launch copy, and UI guidance to prefer popup-free AUTH_TOKEN/CT0 configuration, and keep X backend selection on the verified Bird or xAI paths.
Validation: uv run python -m unittest tests.test_env_project
Matt Van Horn