Last 30 Days

**Quick judgment** A well-designed, opinionated research-and-synthesis skill that’s particularly strong for “what are people saying right now?” questions and for turning fresh Reddit/X chatter into a single, high-quality prompt. Its behavior is clearly specified and aligns well with OpenClaw’s tooling model (scripts, TaskOutput, web_search), making it a solid choice when you need recency and crowd-sourced insights rather than relying only on model knowledge. **Key strengths** - Good intent parsing: explicitly extracts topic, target tool (if given), and query type (RECOMMENDATIONS / NEWS / PROMPTING / GENERAL), which guides both search and output structure. - Multi-source, recency-focused research: combines Reddit, X, and web search, and distinguishes between full, partial, and web-only modes with automatic key detection. - Strong synthesis spec: prioritizes high-engagement Reddit/X content, looks for cross-source patterns and contradictions, and forces the assistant to use the *actual* research content instead of prior assumptions. - Action-oriented: ends with a clear invitation, then produces exactly one tailored prompt in the format the research says works best (JSON, structured, natural language, etc.), rather than a generic prompt dump. **Main risks & limitations** - **External dependency & fragility:** Heavily relies on a Python script plus Reddit/X/web search; if APIs, search tools, or the script path break, quality and completeness degrade. The web-only fallback mitigates this but loses engagement metrics and some of the “social proof” angle. - **Latency & cost:** Deep mode (50–70 Reddit threads + 40–60 X posts) will be slow and potentially expensive, especially if combined with multiple web searches. Not ideal for low-latency or budget-sensitive flows. - **Content quality & safety:** It intentionally surfaces recent Reddit/X discussions; those can include low-quality, biased, or harmful content. The base model’s safety filters still need to moderate the synthesis carefully. - **Instruction complexity:** The behavior spec is quite detailed; if the assistant doesn’t follow it exactly (e.g., forgets to weight Reddit/X more, or ignores the mandated output sequence), results may be inconsistent. **Recommended scenarios** - When a user explicitly wants **recent community sentiment** or “what are people saying about X right now?” rather than timeless documentation. - When the goal is to derive **current best practices or prompt patterns** from real usage (prompting techniques, recommended tools/skills, real-world workflows). - For **recommendation-style questions** (“best X”, “top Y”) where a list of *specific, named* tools/projects/products based on live chatter is more valuable than generic advice. - As a prelude to crafting **one highly-optimized prompt** grounded in fresh research, especially when the target tool’s effective formats are evolving quickly. Less ideal for: very fast responses, offline/air-gapped environments, or scenarios where you must avoid any user-level exposure to social-media-derived perspectives.