feat: v3.0.0 - intelligent search, GitHub person/project mode, ELI5, 13+ sources
v3 rewrites the search engine from the ground up: - Intelligent pre-research: resolves X handles, GitHub repos, subreddits, TikTok hashtags, and YouTube channels before searching - GitHub person-mode: PR velocity, top repos by stars, release notes - GitHub project-mode: live star counts, README, releases, top issues - ELI5 mode: plain language synthesis, no jargon - 13+ sources: Reddit, X, YouTube, TikTok, Instagram, HN, Polymarket, GitHub, Threads, Pinterest, Perplexity, Bluesky, Web - Free Reddit comments via public JSON (no API key needed) - Fun judge v2: humor scoring baked into narrative - Cookie consent before browser scanning - 10,000 free ScrapeCreators calls - 1,012 tests Thank you to the community contributors whose issues and PRs shaped v3: @uppinote20 (#143), @zerone0x (#134, #136), @thinkun (#116), @thomasmktong (#124), @fanispoulinakisai-boop (#100), @pejmanjohn (#78), @zl190 (#115), @hnshah (#84, #85, #86) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Matt Van Horn
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---
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name: last30days-3
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version: "3.0.0-alpha"
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description: "[V3 DEV] Research any topic from the last 30 days. Side-by-side test version of the v3 pipeline."
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argument-hint: "last30days-v3 codex vs claude code"
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allowed-tools: Bash, Read, Write, WebSearch
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homepage: https://github.com/mvanhorn/last30days-skill
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repository: https://github.com/mvanhorn/last30days-skill
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author: mvanhorn
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license: MIT
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user-invocable: true
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---
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# last30days v3.0.0
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Use `last30days` when the user wants recent, cross-source evidence from the last 30 days.
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The runtime is a single v3 pipeline:
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1. plan the query
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2. retrieve per `(subquery, source)`
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3. normalize and dedupe
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4. extract best snippets
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5. fuse with weighted RRF
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6. rerank with one relevance score
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7. cluster evidence
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8. render ranked clusters
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## Setup: resolve the skill root
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```bash
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for dir in \
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"." \
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"${CLAUDE_PLUGIN_ROOT:-}" \
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"${GEMINI_EXTENSION_DIR:-}" \
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"$HOME/.openclaw/workspace/skills/last30days" \
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"$HOME/.openclaw/skills/last30days" \
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"$HOME/.claude/skills/last30days" \
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"$HOME/.agents/skills/last30days" \
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"$HOME/.codex/skills/last30days"; do
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[ -n "$dir" ] && [ -f "$dir/scripts/last30days.py" ] && SKILL_ROOT="$dir" && break
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done
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if [ -z "${SKILL_ROOT:-}" ]; then
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echo "ERROR: Could not find scripts/last30days.py" >&2
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exit 1
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fi
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```
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## Default command
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```bash
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python3 "${SKILL_ROOT}/scripts/last30days.py" $ARGUMENTS --emit=compact
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```
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## Useful commands
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```bash
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python3 "${SKILL_ROOT}/scripts/last30days.py" $ARGUMENTS --emit=json
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python3 "${SKILL_ROOT}/scripts/last30days.py" $ARGUMENTS --quick
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python3 "${SKILL_ROOT}/scripts/last30days.py" $ARGUMENTS --deep
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python3 "${SKILL_ROOT}/scripts/last30days.py" $ARGUMENTS --search=reddit,x,grounding
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python3 "${SKILL_ROOT}/scripts/last30days.py" $ARGUMENTS --store
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python3 "${SKILL_ROOT}/scripts/last30days.py" --diagnose
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```
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## Runtime expectations
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- One reasoning provider is required: `GOOGLE_API_KEY` for Gemini, `OPENAI_API_KEY` for OpenAI, or `XAI_API_KEY` for xAI.
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- `BRAVE_API_KEY` enables Brave web search (recommended). `SERPER_API_KEY` is the web fallback.
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- `SCRAPECREATORS_API_KEY` enables Reddit, TikTok, and Instagram.
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- `XAI_API_KEY` enables xAI reasoning and X search.
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- `AUTH_TOKEN` plus `CT0` enables Bird-backed X search.
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- `yt-dlp` enables YouTube.
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- Planning and reranking fall back gracefully: Gemini -> OpenAI -> xAI -> deterministic/local.
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- Web retrieval stays within Brave/Serper dated results. Undated web hits are dropped.
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- For OpenClaw-specific watchlist, briefing, and history workflows, use `variants/open/SKILL.md`.
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## Output model
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- `compact` and `md`: cluster-first markdown
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- `json`: full v3 report
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- `context`: short synthesis-oriented context
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Important report fields:
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- `provider_runtime`
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- `query_plan`
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- `ranked_candidates`
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- `clusters`
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- `items_by_source`
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- `errors_by_source`
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## Usage guidance for agents
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- Prefer `--quick` for fast iteration.
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- Prefer default mode when the user wants a balanced answer.
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- Prefer `--deep` only when the user explicitly wants maximum recall or the topic is complex enough to justify extra latency.
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- Prefer `--emit=json` when downstream code or evaluation will consume the result.
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- Use `--search=` only when the user explicitly wants source restrictions.
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## X handle resolution
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If the topic could have its own X/Twitter account (people, brands, products, companies), do a quick WebSearch for their handle:
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```
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WebSearch("{TOPIC} X twitter handle site:x.com")
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```
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If you find a verified handle, pass `--x-handle={handle}` (without @). This searches their posts directly, finding content they posted that doesn't mention their own name. Skip this for generic concepts ("best headphones 2026", "how to use Docker").
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## Synthesis guidance
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### First: synthesize, don't summarize
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Extract key facts from the output first, then synthesize across sources. Lead with patterns that appear across multiple clusters. Present a unified narrative, not a source-by-source summary.
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### Ground in actual research, not pre-existing knowledge
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Use exact product/tool names, specific quotes, and what sources actually say. If research mentions "ClawdBot" and "@clawdbot", that is a different product than "Claude Code" -- read what the research actually says.
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**Anti-pattern to avoid:**
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- BAD: User asks "best Claude Code skills" and you respond with generic advice: "Skills are powerful. Keep them under 500 lines."
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- GOOD: You respond with specifics from the research: "Most mentioned: /commit (5 mentions), remotion skill (4x), git-worktree (3x). The Remotion announcement got 16K likes on X per @thedorbrothers."
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### Source weighting (highest to lowest signal)
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1. **Cross-cluster corroboration** -- same evidence across multiple sources is the strongest signal. Lead with it.
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2. **Reddit top comments** -- often the wittiest, most insightful take. Quote directly when upvotes are high.
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3. **YouTube transcript highlights** -- pre-extracted key moments. Quote and attribute to channel name.
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4. **X/Twitter @handles** -- real-time community signal. Quote with engagement context.
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5. **Polymarket odds** -- real money on outcomes cuts through opinion. Include specific odds AND movement.
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6. **TikTok/Instagram** -- viral/creator signal. Cite @creators with views/likes.
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7. **Hacker News** -- technical community perspective. Cite as "per HN."
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8. **Web (Brave/Serper)** -- cite only when social sources don't cover a fact.
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### Polymarket interpretation
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When Polymarket returns relevant markets:
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1. Prefer structural/long-term markets over near-term deadlines (championship odds > regular season, IPO > incremental update)
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2. Call out the specific outcome's odds and movement, not just that a market exists
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3. Weave odds into the narrative as supporting evidence, don't isolate them
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4. When multiple relevant markets exist, highlight 3-5 ordered by importance
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Domain importance ranking:
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- **Sports:** Championship/tournament > conference title > regular season > weekly matchup
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- **Geopolitics:** Regime change/structural > near-term strike deadlines > sanctions
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- **Tech/Business:** IPO, major product launch > incremental updates
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- **Elections:** Presidency > primary > individual state
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### Citation rules
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Cite the single strongest source per point in short format: "per @handle" or "per r/subreddit". Save engagement metrics for the stats section. Use the priority order from source weighting above. The tool's value is surfacing what PEOPLE are saying, not what journalists wrote.
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### Comparison queries
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For "X vs Y" queries, structure output as:
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```
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## Quick Verdict
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[1-2 sentences: which one the community prefers and why, with source counts]
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## [Entity A]
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**Community Sentiment:** [Positive/Mixed/Negative] (N mentions across sources)
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**Strengths:** [with source attribution]
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**Weaknesses:** [with source attribution]
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## [Entity B]
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[Same structure]
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## Head-to-Head
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| Dimension | Entity A | Entity B |
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|-----------|----------|----------|
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| [Key dim] | [position] | [position] |
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## Bottom Line
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Choose A if... Choose B if... (based on community data)
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```
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### Recommendation queries
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When users ask "best X" or "top X", extract SPECIFIC NAMES:
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```
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Most mentioned:
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[Name] -- Nx mentions
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Sources: @handle1, r/subreddit, [YouTube channel]
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[Name] -- Nx mentions
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Sources: @handle2, r/subreddit2
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Notable mentions: [others with 1-2 mentions]
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```
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### Edge cases
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- **Empty results from a source:** State what is missing. ("No Reddit discussion found for this topic.") Do not fill the gap with training data.
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- **Sources contradict each other:** Present both sides with attribution. ("Reddit r/fitness is bullish on X, while @DrExpert on X warns about Y.")
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- **All results are low-engagement or off-topic:** Acknowledge uncertainty. ("Limited recent discussion found -- these findings should be treated as preliminary.")
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### Follow-up conversations
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After research completes, treat yourself as an expert on this topic. Answer follow-ups from the research findings. Cite the specific threads, posts, and channels you found. Only run new research if the user asks about a DIFFERENT topic.
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## Security and permissions
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**What this skill does:**
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- Sends search queries to ScrapeCreators API for Reddit, TikTok, Instagram search
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- Sends search queries via xAI API or Bird client for X search
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- Sends search queries to Algolia HN Search API (free, no auth)
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- Sends search queries to Polymarket Gamma API (free, no auth)
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- Runs yt-dlp locally for YouTube search and transcript extraction (no API key)
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- Sends search queries to Brave Search API or Serper for web search (optional)
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- Uses Gemini, OpenAI, or xAI for LLM planning and reranking
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- Stores findings in local SQLite database (--store mode only)
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**What this skill does NOT do:**
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- Does not post, like, or modify content on any platform
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- Does not access your personal accounts on any platform
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- Does not share API keys between providers
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- Does not log or cache API keys in output files
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