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>
The regression test from #290 walks the filesystem via Path.rglob, so
docs/plans/*.md files (gitignored, created by internal planning) trip
the assertion on any dev machine that has run ce:plan in this repo.
Fresh clones and CI never see them, but local runs fail.
Adding docs to skip_dirs keeps the guard narrow to first-class source
files while letting internal planning docs reference old paths
verbatim.
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>
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>
* 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>
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.
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
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.
The second assertion in test_resolve_google_judge_api_key_prefers_google_key
ran outside the mock.patch.dict context. When GOOGLE_API_KEY or GEMINI_API_KEY
is set in the real environment, os.environ takes precedence over the config
dict fallback and the test fails.
Wrap the assertion in its own mock.patch.dict scope that clears the three
relevant env vars so the test passes regardless of the developer's env.
This contribution was developed with AI assistance (Claude Code).
lib/exa_search.py was removed during the v3 refactor but
tests/test_exa_search.py still imports from it. This causes
an ImportError that blocks pytest -x from running any tests.
This contribution was developed with AI assistance (Claude Code).
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>
* 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>
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>
Matt Van Horn