Pull Bigdata.com (RavenPack) financial and news data through the official `bigdata-client` SDK and its public `/v1/*` REST endpoints when the Bigdata MCP server returns only pre-synthesized tearsheets but you need the machine-readable substrate underneath. MCP search returns prose chunks (text + relevance only — no per-chunk sentiment, no entity spans); its tearsheets give only aggregate values, not computable time series or per-field JSON. This skill bundles a verified, cost-guarded toolkit over the official REST API: annotated chunk search, entity/ISIN resolution, analyst estimates, calendar/surprise/ ratings/targets, financial statements, TTM metrics & ratios, prices, dividends, revenue segments, a daily entity-sentiment series, co-mention graph, screener, and batch search. Use it whenever the user mentions Bigdata.com, RavenPack, a `bd_v2_` key, the bigdata MCP, rp_entity_id, chunk/query_unit cost, or wants structured financials, fundamentals, prices, sentiment, or annotated news.
Adversarial due-diligence on a benchmark you envy — a founder, KOL, company, or product whose claimed success you suspect is inflated. Inline four-phase orchestration — fan-out collection, adversarial verification grading every claim L1-L4 to split marketing bubble from real signal, attribution weighting (product vs timing vs IP vs luck, what's replicable), then mapping the validated playbook onto the user's own resources. Use whenever the user wants to 尽调/对标/拆解 a competitor or role-model, 抄/偷师 someone's playbook, suspects 水分/泡沫 in their claims (Product Hunt
Investigate and resolve Cloudflare configuration issues using API-driven evidence gathering. Use when troubleshooting ERR_TOO_MANY_REDIRECTS, SSL errors, DNS issues, or any Cloudflare-related problems. Focus on systematic investigation using Cloudflare API to examine actual configuration rather than making assumptions.
Evidence-driven investigation for network, streaming, and protocol-layer bugs. Use when debugging connection resets (ECONNRESET, HTTP/2 RST_STREAM, INTERNAL_ERROR), SSE or long-polling stalls, fixed-time connection drops, CDN/proxy/CGNAT idle timeouts, or any incident where symptoms do not match the obvious cause. Applies falsification-first methodology — layered isolation experiments to pin down the responsible network layer, env-gated runtime instrumentation for non-invasive observation, and counter-review agent teams to challenge single-cause assumptions. Strongly trigger on "socket closed unexpectedly", "stream interrupted", "ECONNRESET", "HTTP/2 INTERNAL_ERROR", "fails after N seconds", "works sometimes but not always", "upstream silent for X seconds", or any scenario where the investigator might jump to conclusions before evidence. Generalizes to any multi-layer system investigation where assumption-first thinking is the failure mode.
Diagnose Windows App (Microsoft Remote Desktop / Azure Virtual Desktop / W365) connection quality issues on macOS. Analyze transport protocol selection (UDP Shortpath vs WebSocket), detect VPN/proxy interference with STUN/TURN negotiation, and parse Windows App logs for Shortpath failures. This skill should be used when VDI connections are slow, when transport shows WebSocket instead of UDP, when RDP Shortpath fails to establish, or when RTT is unexpectedly high.
Convert markdown files to professional PDF documents with proper Chinese font support, theme system, and visual self-check. Use whenever the user asks to create PDFs, convert markdown to PDF, generate printable documents, or needs documents formatted for print or mobile reading. This skill MUST be used instead of manual pandoc/Chrome invocations — it handles CJK typography, Chrome header/footer suppression, and mandatory visual verification that manual approaches miss. **Scope: markdown → PDF only.** For Word (.docx) output use `minimax-docx`; this skill does not produce docx and the two pipelines are intentionally orthogonal.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Corrects speech-to-text transcription errors using dictionary rules and AI-powered analysis. Builds personalized correction databases that learn from each fix. Triggers when working with ASR/STT output containing recognition errors, homophones, garbled technical terms, or Chinese/English mixed content. Also triggers on requests to clean up meeting notes, lecture transcripts, interview recordings, or any text produced by speech recognition. Use this skill even when the user just says "fix this transcript" or "clean up these meeting notes" without mentioning ASR specifically.