7,183 件 ・ 関連度順
概要と使いどころ
Confluence page CRUD via the confluence-as wrapper. Triggers: 'create page in SPACE', 'show me page 12345', 'update title of page 12345', 'delete page 12345', 'page hierarchy under 12345', 'copy page 12345 to SPACE'. NOT for bulk ops on 10+ pages (mk:confluence-bulk); NOT for spec analysis (mk:confluence-spec-analyst); NOT for comments/attachments/labels (mk:confluence-collaborate).
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。
Systematic debugging with root cause investigation. Produces a diagnostic report; mk:fix owns remediation and mk:build-fix owns compile/build failures. Use when asked to "debug this", "fix this bug", "why is this broken", "investigate this error", or "root cause analysis". Proactively suggest when the user reports errors, unexpected behavior, or is troubleshooting why something stopped working. NOT for applying fixes without investigation (see mk:fix); NOT for step-by-step evidence-based reasoning (see mk:sequential-thinking).
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。
Ticket/PRD intake: product area classification, completeness scoring, RCA, technical assessment. Works with Jira (via mk:jira-issue / jira-as), Linear (via MCP), GitHub (via gh CLI), or manual paste. Triggers: 'analyze ticket', 'intake PRD', 'triage issue', 'classify ticket', 'check ticket'.
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。
Browser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.
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。
Read-only report of improvement suggestions from eval-core analysis engine
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。
Apply verified improvement suggestions from eval-core analysis to omcustom configuration
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。
Opt-in post-PR analysis and improvement suggestions for code quality enhancement
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。
10-team parallel deep analysis with cross-verification for any topic, repository, or technology. Use when user invokes /research or asks for comprehensive research.
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。
Pre-flight security analysis before code changes — identifies trust boundary risks, dangerous patterns, and provides proceed/caution/block advisory before implementation starts
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。
Analyze project and auto-configure agents, skills, rules, and guides
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。
hada.io RSS feed monitoring for AI agent/harness articles with automated /scout analysis
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。
CVE triage workflow for vulnerability analysis, reproduction assessment, and patch verification
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。
Pre-routing ambiguity analysis — scores request clarity and asks clarifying questions when needed (inspired by ouroboros)
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。
Establish, author, restructure, or audit human-first processor Architecture, Design, Research, Review, and Verification documentation. Use for progressive documentation scaffolding, authority maps, reading paths, document-type content contracts, length-budgeted splitting, or maintainability reviews. Do not use as a substitute for cycle-accurate microarchitecture analysis or RTL implementation.
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。
Task-sized forensic analysis of Vivado synthesis and routed timing evidence for processor RTL. Use when Codex must trace a named setup or hold path, audit whole-design timing populations, compare implementation runs, map primitive, LUT, CARRY, BRAM, DSP, MUXF, register, and routed-net stages back to Chisel or generated RTL, distinguish logic from routing pressure, identify missing DCP queries, or write an evidence-backed timing report. This skill is read-only by default and stops at ranked modification directions. Use optimize-chisel-fpga-timing for RTL edits and routed A/B implementation closure.
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 and optimize timing-critical Chisel RTL for FPGA implementation while preserving cycle semantics. Use for Vivado timing bottlenecks, long ready or admission paths, queue and issue selection, free-list bank mapping, priority encoders, one-hot arbitration, wide muxes, late-arriving forwarding or override data, high-fanout controls, cross-module predicates, register-boundary changes, emitted-Verilog inspection, or routed-DCP A/B analysis. Also use when a source-level simplification needs proof that it changes timing without changing architectural state.
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 new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update 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.
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。
Record measured or directly seen security-research behavior as cited Observations, classify it against reusable security concepts, and decide whether it remains an observation, becomes a Lead, or supports a Finding. Use after passive analysis, a tool run, a probe, a crash, a timing anomaly, or a user-supplied behavioral report produces noteworthy evidence.
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Use common CLI, Kali, and shell tools to inspect web Targets, captured assets, logs, HAR/PCAP files, packages, and generated artifacts, then turn observations into Evidence, Security Signals, and ranked Hypotheses. Use when the user asks for web bug-hunting inspection, digital records, URL maps, crawling, endpoint discovery, JS bundle/package analysis, light forensics, or command-line investigation workflows.
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。
Analyze suspicious SMS/email messages, phishing links, redirects, domains, payloads, malware indicators, and suspected C2 infrastructure safely. Use when the user provides spam, smishing/phishing content, raw .eml headers, URLs, QR links, attachments, hashes, strange payloads, redirect chains, DNS/WHOIS/RDAP questions, C2 clues, exploit-chain questions, or asks what defenses to apply.
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。
Research complex questions and synthesize relevant evidence into a clear, cited answer. Useful for competitive and market analysis, company research, prospect discovery, enrichment, historical comparisons, and evaluating conflicting claims. Adapt the depth and format to the question; use when a focused search or single-page read is insufficient.
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。
Call this skill when faced with "wicked problems", complex challenges, or when a user asks for strategic innovation, root-cause analysis, or long-term problem solving. Do not use for simple, linear tasks. This skill forces the agent to bypass superficial symptoms, map the entire ecosystem, and anticipate second-order consequences before proposing an intervention.
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。
Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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。
Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.
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。