Model optimization (mini-first fallback, is_search_capable_model) belongs
in PR #67. This PR stays focused on endpoint/API fixes only.
Also fixes pre-existing test bug where test asserted gpt-4o was first in
MODEL_FALLBACK_ORDER when it was actually gpt-4.1.
- Instagram: migrate /v1/ to /v2/ ScrapeCreators endpoint (v1 deprecated Feb 2026)
- OpenAI: switch fallback chain to [gpt-5-mini, gpt-4.1-mini, gpt-4.1] (8x cheaper,
gpt-5-mini is the first mini model supporting web_search with filters.allowed_domains)
- xAI: use explicit grok-4-1-fast-non-reasoning (bare name aliases to reasoning variant)
- xAI: pass from_date/to_date natively to x_search tool config instead of prompt-only
- Polymarket: correct rate limit comment (15K/10s, not 350/10s)
- add xiaohongshu/xhs source path via xiaohongshu-mcp HTTP API\n- add Reddit public JSON fallback when OpenAI auth is unavailable\n- update diagnostics/UI rendering for new source availability states\n- harden Xiaohongshu availability probe to reduce false negatives\n- include source status reporting for Xiaohongshu
* chore: fix YAML error in argument-hint
* add codex auth support to responses API
* Use gpt-5.1-codex-mini as default model for Codex auth
Add CODEX_FALLBACK_MODELS chain (gpt-5.1-codex-mini → gpt-5.2) for
Codex endpoint which doesn't support standard OpenAI models like
gpt-4o-mini. Adds model fallback retry on 400 errors in the Codex
search path. Also adds test_codex_auth.py with 22 unit tests covering
JWT decoding, auth resolution, SSE parsing, and payload building.
* Pass .env credentials to Bird Node subprocesses for X auth
On platforms without browser cookie access (e.g. WSL2), Bird's
vendored Node.js module cannot read AUTH_TOKEN/CT0 from Firefox
or Chrome cookie stores. The .env config file already supports
these values, but they were only loaded into the Python config
dict — never exported to the environment of Node subprocesses.
- Add AUTH_TOKEN/CT0 to env.py config key loading
- Add set_credentials()/\_subprocess_env() to bird_x.py to inject
credentials into the env dict passed to subprocess.run/Popen
- Call set_credentials() in main() before Bird auth detection
---------
Co-authored-by: Justin Williams <jblwilliams@gmail.com>
The Reddit search uses OpenAI's Responses API with web_search, which
frequently returns 429 rate limit errors. The previous retry logic used
linear backoff (1s, 2s, 3s) which is too aggressive for OpenAI's rate
limiter (often needs 10-60s waits).
Changes:
- Increase max retries from 3 to 5
- Switch from linear to exponential backoff (2s, 5s, 9s, 17s, 33s)
- Parse and respect Retry-After header from OpenAI 429 responses
- Fall back to cheaper models (gpt-4.1 → gpt-4o) on 429s, not just
on 400/403 access errors
- Remove gpt-4o-mini from fallback chain — it doesn't support
web_search with the filters parameter
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
YouTube: Add youtube_future timeout key (60/90/120s for quick/default/deep)
separate from the shared future timeout. YouTube needs more time because
it does search + parallel transcript fetching. Previously, 20 videos +
5 transcripts exceeded the 60s budget and all results were discarded.
Reddit 429: Propagate rate-limit errors instead of swallowing them.
Enrichment now uses 10s timeout / 1 retry (was 30s / 3 retries).
On first 429, cancel remaining enrichment and skip Phase 2 Reddit.
Total time wasted on 429 drops from ~75s to ~12s.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
After the initial broad search (Phase 1), extract key entities from results
and run targeted secondary searches to surface content the broad pass missed:
- New entity_extract.py: parses @handles, #hashtags, subreddits from results
- bird_x.py: search_handles() does targeted from:handle searches via Bird CLI
- openai_reddit.py: search_subreddits() uses Reddit's free .json search endpoint
- last30days.py: Phase 2 orchestration runs after enrichment, merges + dedupes
Tested with "kanye west" (+9 Reddit, +1 X) and "claude code skills" (+6 Reddit, +1 X).
Phase 2 is skipped on --quick mode. Default caps at 3 handles/subs, deep at 5.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Stats: replace BAD/GOOD examples with strict fill-in-the-blank template
- Reddit: add subreddit-targeted fallback search, soften scoring penalties
(engagement -10→-3, date confidence -10→-5), add minimum result guarantee
- Citations: limit to 1 per insight, short format, no engagement metrics
- Summary: add bold topic headers template for structured paragraphs
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fix invalid YAML in SKILL.md argument-hint (closes#8)
Wrapped value in single quotes to properly escape double quotes
- Add automatic model fallback for GPT-5 access errors (closes#9)
When OpenAI returns 400 for unverified orgs, retry with gpt-4o
- Add tests for model fallback logic
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Refactor run_research() to use ThreadPoolExecutor for parallel execution
- Reddit timeout/crash no longer blocks X search from running
- Add catch for ConnectionResetError/OSError in http.py
- Per-item error handling in Reddit enrichment (one failure doesn't crash all)
- Increase API timeouts from 60/90/120 to 90/120/180 seconds
- Add ClawdBot setup example to README
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
If initial search returns <5 threads, extract core subject and retry:
- "best nano banana prompting practices" → retry with "nano banana"
- Combines results from both searches, deduped by URL
Note: OpenAI's web_search still tends to find old content. This retry
helps cast a wider net but doesn't fully solve the recency issue.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
OpenAI's web_search doesn't reliably find recent Reddit content.
Strategy change: request MORE threads and let server-side date
filtering handle it.
Changes:
- Increased depth config (20-30 → 30-50 default)
- Simplified prompt to search broadly and include all matches
- Let server-side date filter remove old content
- Removed aggressive pre-filtering that was limiting results
Tradeoff: Some topics have more X coverage than Reddit due to
web_search limitations. This is acceptable.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The model was finding old threads (Nov-Dec 2025) that got filtered out,
leaving 0 results even for popular topics like "nano banana prompts".
Changes:
- Add explicit date filters: "after:{from_date}" and "2026"/"January 2026"
- Emphasize RECENT content is critical, old threads are filtered anyway
- Tell model to verify dates before including threads
Before: 0-2 Reddit threads for "nano banana prompting practices"
After: 16 Reddit threads from January 2026
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add EXTRACT CORE KEYWORDS step to search for main subject, not full phrase
("killer features of clawdbot" → search "clawdbot")
- Remove hardcoded subreddit list that biased results toward design/dev subs
- Simplify search strategies and URL validation rules
- Make prompt more concise - GPT-5.2 responds better to clearer instructions
Before: 0 threads found for "killer features of clawdbot"
After: 7 threads found including r/LocalLLaMA (43pts), r/selfhosted (26pts)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Previously Reddit was returning ~60% old content (some from 2022).
This commit adds multiple layers of date enforcement:
- Reddit prompt: Explicit from_date/to_date with "fewer results > older results"
- Hard filter: filter_by_date_range() in normalize.py excludes old content
- WebSearch Date Detective: Extracts dates from URLs (/2026/01/24/) and
snippets ("January 24, 2026", "3 days ago")
- WebSearch scoring: +10 bonus for verified dates, -20 penalty for unknown
The skill now guarantees only content from the last 30 days.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add LAST30DAYS_DEBUG env var / --debug flag
- Log HTTP requests, responses, and errors
- Show API error details when debug enabled
- Helps diagnose API failures
Usage: python3 last30days.py "topic" --debug
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Update prompt to search for "site:reddit.com/r/ {topic}"
- Add explicit filtering for /r/*/comments/* URLs
- Block garbage domains (developers.reddit.com, business.reddit.com)
- Make prompt lenient for older threads when recent ones scarce
- Require returning threads found, never empty items
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
1. Output order: What I learned → TARGET TOOL → KEY PATTERNS → Research Complete → Share vision
2. Fixed Reddit returning empty results - OpenAI was finding URLs but not extracting content
- Updated prompt to explicitly require extraction from search results
- Added "MUST include threads" instruction to prevent empty items
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- SKILL.md: Move "What I learned" BEFORE "Research Complete" stats
- Add error tracking to Report schema (reddit_error, x_error fields)
- Wrap OpenAI API calls in try/catch with clear error messages
- Show explicit error or "no results" messages in compact output
- Fix false positive error detection for null error fields
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- quick: 8-12 sources each, faster response
- default: 20-30 sources each (unchanged behavior)
- deep: 50-70 Reddit, 40-60 X for comprehensive research
Adjusts API timeouts based on depth. Cache keys include depth
so different depths are cached separately.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Research topics across Reddit + X from the last 30 days using
OpenAI and xAI APIs. Features:
- Auto model selection (GPT-5.x, Grok-3)
- Popularity-aware scoring (relevance + recency + engagement)
- Reddit thread enrichment with real metrics
- Near-duplicate detection
- Multiple emit modes (compact, json, context, path)
- 24h caching with --refresh bypass
- NUX for API key setup
- 87 passing unit tests
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Jeffrey Sperling