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概要と使いどころ
Helps detection engineers identify coverage blind spots in system command execution detection rules, evaluate bypass risks, and reduce false negatives. Strictly limited to system command execution rules; not applicable to browser credential theft or abnormal process creation without command semantics.
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Use when: investigating a legacy, inherited, third-party-built, or otherwise newly-unfamiliar codebase before scoping a migration or major refactor. Formalizes a repeatable five-step investigation pass — entry-point census, business-rule classification, side-effect/idempotency cataloging, implicit-contract discovery, semantic-duplication detection — plus a canonical per-project Open Questions document. Requires checkable signals: ticket keywords or prior documentation gaps. Not a mandatory gate;
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Use when: investigating a legacy, inherited, third-party-built, or otherwise newly-unfamiliar codebase before scoping a migration or major refactor. Formalizes a repeatable five-step investigation pass — entry-point census, business-rule classification, side-effect/idempotency cataloging, implicit-contract discovery, semantic-duplication detection — plus a canonical per-project Open Questions document. Requires checkable signals: ticket keywords or prior documentation gaps. Not a mandatory gate; reserved for entry-point-heavy work in code areas with no prior investigation record.
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Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.
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Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
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Semantic search, similar content discovery, and structured research using Exa API
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Comprehensive patterns and techniques for analyzing compiled binaries, understanding assembly code, and reconstructing program logic.
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Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search.
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Build search applications with vector, hybrid, and semantic search capabilities.
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Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.
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Rev kategorisi SKILL.md — Binary analysis, disassembly, symbolic execution araçları kurma rehberi
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Guide branch creation, commit messages, and titles so the repo’s semantic workflow is enforced
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Generates and maintains changelogs following Keep a Changelog format. Use when updating release notes, finishing tasks, or tagging versions. For version bumps, see semantic-versioning.
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Cross-Entity Disclosure Tracker App-in-Skill — a Busabase review workspace that helps a compliance/IR team assemble and track a standardized disclosure package per financing vehicle (fund/SPV), across a generic onshore origination entity, an offshore fund-manager entity, and a listing/exchange venue. Use when the user invokes $kelly-disclosure-tracker or /kelly-disclosure-tracker, wants to review disclosure checklists, vehicle readiness, cross-entity reconciliation flags, or reviewer notes across multiple financing vehicles. Review workspace only — it never files anything or calls any external system.
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Run a Busabase-backed sandbox desk for scoring and triaging course-instructor ("讲师") candidates found on a recruiting-style platform. Use whenever the user invokes $kelly-instructor-sourcing or /kelly-instructor-sourcing, wants a lightweight instructor candidate tracker, needs to score sourcing-platform leads against a three-axis rubric (background endorsement, expertise depth/breadth, teaching-service ability) before reaching out, or wants to log a WeChat add without building a live-connected outreach tool. Three subcommands - setup, criteria, review - plus the desk itself. v1 is sandbox-only — every dataset is deterministic mock data, it never connects to a real sourcing-platform or messaging account, and it never sends a message or automates a WeChat add - that stays entirely human-performed outside the app.
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Finish an existing Japanese creator, challenge, comeback, vlog, or community-building vertical social rough cut as an evidence-grounded short-form story. Use for strong opening declarations, identity and struggle arcs, crisis-to-goal recovery, multi-source B-roll and evidence stills, emotional typography, semantic punch-ins, pinned SFX, dialogue-first audio, platform-added music delivery, and creator-short retention QA.
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Building a Threat Intelligence Platform (TIP) involves deploying and integrating multiple CTI tools into a unified system for collecting, analyzing, enriching, and disseminating threat intelligence. T
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Reverse engineer Go-compiled malware using Ghidra with specialized scripts for function recovery, string extraction, and type reconstruction in stripped Go binaries.
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Shape an article as a journey of beats, choose-your-own-adventure style. The user picks a starting beat from the raw material, you write only that beat, then offer options for where to pivot next, beat by beat, until the article reaches a natural end. Use when the user has raw material and wants to assemble it as a narrative rather than an argument.
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How to query the document_embeddings table for raw signal data using HogQL. Use when you need to perform semantic search over signals, fetch every signal that contributed to a specific report, or list signal types. For browsing the curated report layer (the Inbox) — listing reports, filtering by status/source, drilling into a single report by ID — use the `inbox-exploration` skill first; drop into this skill afterwards if the user wants the underlying observations.
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Explore PostHog's Inbox — the surface where signal reports surface as actionable issues and trends. Use when the user asks "what's in my inbox?", "what should I look at?", "which reports are actionable?", "what's PostHog flagged recently?", asks about a specific report by ID or title, or wants to see which signal sources are configured. Covers listing, filtering, and drilling into reports, plus pointers to the deeper `signals` skill when raw signals or semantic search are needed.
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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
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Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
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End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.
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