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概要と使いどころ
Research what people have discussed in the last 30 days across high-signal public platforms. Use when the user asks for `/last30days`, recent trend research, competitive intelligence, market pulse briefs, or engagement-ranked synthesis from Reddit, Hacker News, GitHub, X, YouTube, Polymarket, and similar sources.
日本語の概要は準備中です。原文の説明を表示しています。
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
日本語の概要は準備中です。原文の説明を表示しています。
Run this skill for Phase 3 of the evidence verification pipeline — the adversarial audit step. Use when you need to check the evidence data lake for bad data before ingestion: dead links, wrong URL formats (tree/ vs blob/), subjective wording ("elite", "high-quality"), stale migration notes, benchmark catalog misuse/vendor-claim leakage, or skills whose star evidence conflicts with classified evidence level. Triggers on phrases like: "audit the data lake", "adversarial check", "ev-adversarial-audit", "check for noise in evidence", "flag bad evidence", "run the audit phase", "quality check the by-type files", or any reference to Phase 3 of the pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Skippable Phase 0 of the Gaia evidence verification pipeline. Use only on declared need for higher-quality Stage-2 evidence: a promotion candidate, or a skill that gaia-meta-sweep flagged as under-evidenced. Given a skill id, source repo/context, and needed evidence types (benchmark-result, arxiv, peer-review, social-signal), it runs Firecrawl searches, scrapes top hits to markdown, and appends discovered sources into Phase 1 inputs. Missing benchmark evidence is recorded as benchmark-source candidates first, not direct scoring rows. Trigger phrases: "discover evidence", "find new evidence", "Phase 0", "run ev-discovery", "search for benchmarks/papers", "under-evidenced", "promotion candidate needs stronger proof". This is the ONLY ev-* skill that searches the web for NEW evidence; other ev-* skills aggregate or verify known sources. Requires Firecrawl to be usable (`firecrawl --status` authenticated OR FIRECRAWL_API_KEY set); skips gracefully when neither holds.
日本語の概要は準備中です。原文の説明を表示しています。
Generate high-CTR YouTube thumbnails using Nano Banana Pro via the Novoads API. Handles reference image upload, character likeness alignment, proven CTR-tested prompt formulas, and bounded batch generation. Use when the user asks to create a YouTube thumbnail, video thumbnail, A/B test thumbnail variations, or refers to thumbnail design with their face, brand assets, or product photos.
日本語の概要は準備中です。原文の説明を表示しています。
Detects packing and encryption by measuring Shannon entropy across a binary's sections and regions: flagging high-entropy executable sections, entropy spikes, and size/raw-vs-virtual anomalies that indicate compression or encryption. Activates for requests to measure entropy, detect packing, or assess whether a sample is compressed/encrypted.
日本語の概要は準備中です。原文の説明を表示しています。
Hunts for domain fronting and CDN abuse by detecting TLS SNI versus HTTP Host header mismatches and high-reputation CDN domains carrying anomalous traffic in network telemetry. Activates for requests to hunt domain fronting, detect SNI/Host mismatch, or find CDN abuse used to mask C2.
日本語の概要は準備中です。原文の説明を表示しています。
Locates candidate encryption keys in a binary by finding high-entropy fixed-size regions (16/24/32 bytes) near cryptographic constants or crypto API references, and surfacing printable key/passphrase strings. Activates for requests to extract encryption keys, find an AES key in a sample, or recover hardcoded crypto material.
日本語の概要は準備中です。原文の説明を表示しています。
Hunts for unusual outbound network connections by flagging direct-to-IP traffic, uncommon ports, rare destinations, high data-egress ratios, and connections from unexpected processes in network telemetry. Activates for requests to hunt unusual outbound traffic, detect suspicious egress, or find anomalous external connections.
日本語の概要は準備中です。原文の説明を表示しています。
Maps hunts and detections to MITRE ATT&CK for coverage analysis: tagging hypotheses with techniques, building a coverage matrix from completed hunts, and identifying high-risk gaps to prioritize next. Activates for requests to map hunts to ATT&CK, build a coverage heatmap, or find detection gaps by technique.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes API call traces from a sandbox or API monitor (JSON) to group calls by category, reconstruct high-level behaviors (process injection, file drops, network, crypto), and flag suspicious call sequences. Activates for requests to analyze an API trace, interpret sandbox API logs, or identify behavior from Win32/Native API calls.
日本語の概要は準備中です。原文の説明を表示しています。
High-throughput inference on FriendliAI — optimized serving engine for low-latency, cost-efficient LLMs.
日本語の概要は準備中です。原文の説明を表示しています。
Master professional email — inbox zero systems, high-impact writing, etiquette, and managing email at scale.
日本語の概要は準備中です。原文の説明を表示しています。
ClickHouse columnar analytics — schema design, MergeTree tuning, and fast queries — use for OLAP and high-volume event data.
日本語の概要は準備中です。原文の説明を表示しています。
Deploy ML models to production with Baseten — high-performance inference with autoscaling and custom runtimes.
日本語の概要は準備中です。原文の説明を表示しています。
Profiling strategy for audio CPU issues — xruns, spikes, and buffer underruns. Use when the user reports CPU overload, audio glitches under load, or performance regressions. Trigger on phrases like "I'm getting xruns", "my plugin causes CPU spikes", "dropouts under high voice count", "audio thread is too slow", "how do I profile my DSP". References audio-numerics-review for denormal slowdown and audio-dsp-review for lock contention.
日本語の概要は準備中です。原文の説明を表示しています。
Diagnose whether an LLM classifier's validation-gate failure is GOLD-BOUND before spending on prompt revision or model changes. Use when: (1) a scoring pipeline over-predicts a label (precision low, recall high) and a prompt clarification is proposed to tighten it, (2) a pilot/validation gate fails and the fix candidates are prompt edits, (3) inter-rater agreement on the weak label was already low (κ < ~0.6). Core check: if gold POSITIVES share the exact feature the revision would exclude, no prompt can pass a gold-scored gate — recall craters while precision barely moves. Also documents the verified surgical-pilot design (single-section diff, tune/holdout split, pre-registered gate, perturbation check on untouched sections).
日本語の概要は準備中です。原文の説明を表示しています。
Convert between LaTeX and Microsoft Word for academic manuscripts in either direction, and assemble .tex from mixed sources. Fires on: converting .tex/LaTeX to Word/.docx ("tex to docx", "latex to word", "tex2docx", "convert to word", "pandoc convert"); converting .docx manuscripts to LaTeX for editing and back ("convert to latex", "manuscript", "footnotes", "reference doc", the docx-to-tex-to-docx round-trip / academic paper editing pipeline); high-fidelity delivery where plain pandoc loses tables, mangles cross-references, or fails on custom macros — booktabs/regression tables, OMML equations, cleveref, \estauto, \@@input, \thanks, TikZ, native Word tables, longtable, siunitx; and building .tex from mixed PDF/docx/LLM-generated sources. Replaces and reroutes the retired skills manuscript-editing-template-latex, latex-to-docx-fidelity, tex2docx, and latex-from-mixed-sources.
日本語の概要は準備中です。原文の説明を表示しています。
Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (AsyncOpenAI, including OpenAI-compatible providers like DeepSeek). Use when: (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with no hard rate limit (DeepSeek v4-flash tolerates 2000+ in flight). Root cause: AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.
日本語の概要は準備中です。原文の説明を表示しています。
Upgrades existing websites and apps to premium quality. Audits current design, identifies generic AI patterns, and applies high-end design standards without breaking functionality. Works with any CSS framework or vanilla CSS.
日本語の概要は準備中です。原文の説明を表示しています。
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
日本語の概要は準備中です。原文の説明を表示しています。
Automate App Store & Google Play screenshot generation for Flutter apps. Scans routing and screens, scores high-value marketing screens, generates self-contained mock harness, runs native simulator captures (iPhone 6.9", iPad 13", Android Phone/Tablet), strips alpha channels, and validates exact store resolutions.
日本語の概要は準備中です。原文の説明を表示しています。
MemoryPack high-performance binary serialization for Unity including custom formatters, versioning, and performance patterns.
日本語の概要は準備中です。原文の説明を表示しています。
PrimeTween high-performance tweening library patterns for animations, UI, and sequencing.
日本語の概要は準備中です。原文の説明を表示しています。