Resolve reproduction-critical gaps by extracting dataset, preprocessing, and protocol details from primary papers. Targets narrow reproduction questions (dataset splits, preprocessing steps, evaluation protocols, checkpoint mappings) rather than general paper summaries Requires concrete reproduction context: existing README, repo evidence, and a specific gap to fill Documents conflicts between README guidance and paper sources explicitly Designed as a helper skill, typically invoked by orchestrators to supplement README-first reproduction workflows Skips general paper explanation, title-only lookups, and environment setup tasks
Standardized execution and audit reporting for deep learning repository reproduction runs. Captures evidence from smoke tests, inference runs, and evaluation commands; writes normalized outputs to repro_outputs/ with patch tracking when repository files change Generates SCIENTIFIC_CHANGELOG.md to document changes affecting evaluation, preprocessing, or metrics, and COMPARABILITY_REPORT.md to assess alignment with README and paper baselines Applies only after a reproduction target and setup plan exist; does not handle initial repo intake, training execution, or target selection Distinguishes between verified, partial, and blocked execution states; refuses to hide changes that alter scientific meaning
Rigor Analyze / Rigor Audit read-only skill for deep learning research repositories. Use when the user wants to read and understand a repository, inspect model…
Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family,…
Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow…
Rigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical…
Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape…
Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup…
Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly…
Web search with optional full-page content extraction from results. Returns real search results as JSON with optional --scrape flag to fetch complete page markdown for each result, avoiding redundant fetches Supports filtering by source type (web, images, news), category (GitHub, research, PDF), time range (past hour/day/week/month/year), location, and country Use --limit to control result count and --scrape-formats to customize output formats when extracting full content Part of a workflow escalation pattern: search first to discover URLs, then use dedicated scrape/map/crawl skills for deeper extraction
Uncover what customers think, say, and struggle with through transcript analysis and online research. Analyze existing research assets (interview transcripts, surveys, support tickets, NPS responses, win/loss notes) to extract jobs to be done, pain points, trigger events, and desired outcomes Mine online communities (Reddit, G2, Hacker News, LinkedIn, forums) for authentic customer language and sentiment across your ICP type Generate personas, VOC quote banks, and research synthesis reports with confidence-level labeling and sample bias checks Segment findings by customer profile, flag contradictions between what customers say and do, and identify 5–10 money quotes per theme for downstream use in copy and positioning
Structured framework for planning product launches, feature announcements, and go-to-market strategies. Organizes launches across five phases (internal, alpha, beta, early access, full) with specific actions and goals for each stage Uses the ORB framework to balance owned channels (email, blog, community), rented channels (social, app stores), and borrowed channels (guest posts, influencers, partnerships) Includes dedicated Product Hunt strategy with pre-launch preparation, launch-day tactics, and post-launch conversion guidance Provides launch checklist covering pre-launch setup, launch-day coordination, and post-launch user education and retention Emphasizes treating launches as ongoing moments rather than one-time events, with guidance on spacing feature announcements and maintaining momentum
SEO-optimized competitor comparison and alternative pages that position your product against rivals. Covers four page formats: singular alternatives, plural alternatives, you vs. competitor, and competitor vs. competitor comparisons Includes centralized competitor data architecture for consistent, maintainable information across all comparison pages Provides structured research process covering product features, pricing, reviews, and customer feedback with quarterly update cadence Emphasizes honest positioning that acknowledges competitor strengths and clearly defines who each product serves best
Web search discovery and source ranking for query-driven workflows. Designed for applications that start with a search query rather than a known URL, enabling discovery before extraction Returns ranked search results with snippets and URLs suitable for answer generation, competitive research, or topic exploration Integrates with downstream Firecrawl skills: escalate to /scrape for content extraction or /interact for pages requiring clicks and form submission Supports both hosted Firecrawl and self-hosted deployments via configurable API endpoint
Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract.…
Comprehensive competitor analysis from URLs, combining site scraping with SEO and market data into structured profiles. Scrapes key pages (homepage, pricing, features, about, customers, integrations) and extracts positioning, messaging, pricing tiers, and product direction signals Pulls SEO metrics via DataForSEO including domain authority, organic traffic estimates, ranked keywords, backlink profiles, and top-performing pages Mines review sites (G2, Capterra, Product Hunt) for ratings, common praise/complaint themes, and representative quotes Generates comparable markdown profiles with consistent structure across all competitors, plus a cross-competitor summary with positioning maps and strategic takeaways Supports quick scans (homepage + pricing only) or deep profiles (all pages + reviews + full SEO analysis); parallelizes research for multiple competitors
Real-time search engine supporting web search, vertical domain search, parallel batch search, and URL content extraction.
Provides resources to stay updated with Golang news, communities and people to follow. Use when seeking Go learning resources, discovering new libraries,…
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