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概要と使いどころ
Use when writing, reviewing, restoring, trimming, or auditing prose in the deepseek-harness repo, including deciding where documentation or comments are required across Markdown, JSDoc, code and test comments, prompts, descriptions, diagnostics, and CLI or UI strings.
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。
Use when adding, auditing, pruning, archiving, restoring, or reviewing Agent Notes in deepseek-harness; checks every new note for superseded active records, classifies implemented notes by future decision value, deletes rejected notes that no longer prevent a tempting fallacy, and applies the frozen archived/{kind} triplet and manifest rules.
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。
Use when reviewing a pull request in the deepseek-harness repo — orients the reviewer to this codebase's standards (AGENTS.md conventions, defensive patterns, ADRs, quality gates) and the review-specific checks that code alone can't show
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。
Use when publishing, updating, moving, or removing DeepSeek Harness documentation website pages; editing website/docs.ts mappings or navigation; diagnosing a page missing from the VitePress site; fixing projected documentation links; or running the docs:dev, docs:check, and doc-sync workflow after website-content changes.
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。
Use before pushing, force-pushing, marking ready for review, or claiming checks pass on a deepseek-harness branch, and immediately after gh stack sync publishes rewritten branches, to select the smallest tests and checks that cover the outgoing or just-published diff without reflexively running the full repository suite.
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。
Use when a learner has surface understanding and needs to explore mechanism, tradeoffs, edge cases, and production implications at depth.
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。
Solves complex, open-ended problems using a knockout tournament mechanism. Generates an initial population of diverse candidate answers, pairs them into head-to-head matches, produces a hybrid "child" solution from each pair, and advances the best of the three through a bracket until a winner emerges. Use this skill whenever the user wants to competitively select the best answer from a field of alternatives, asks for a "tournament" or "bracket" style search, wants a faster alternative to full evolutionary search, or uses phrases like: "run a tournament on this", "bracket-style search", "let solutions compete", "knockout search", "find the best answer by elimination", or "which solution wins?". Also trigger when the problem is open-ended and the user wants a clear winner quickly rather than a deep multi-generation refinement.
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。
Solves complex, open-ended problems using an evolutionary search mechanism inspired by genetic algorithms. Generates a diverse population of candidate answers, scores them against a rubric, then iteratively applies selection, crossover, and mutation to breed progressively better solutions. Use this skill whenever the user wants to explore a solution space deeply, asks for the "best" answer to a subjective or multi-dimensional problem, wants to evaluate competing approaches and distill the strongest, or uses any of these triggers: "evolutionary search", "genetic algorithm", "evolve an answer", "breed solutions", "population-based search", or phrases like "give me multiple approaches and refine the best one". Also trigger when the problem is complex, open-ended, or has no obvious single correct answer and the user wants a high-quality, well-explored result, not just a quick response.
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。
Culture assessment and monitoring with three modes — Diagnostic for a deep session surfacing the gap between declared culture and actual culture, Review for periodic check-ins on whether behavior is shifting, and Alert Triggers for setting tripwires around culture-strategy misalignment, regression, and integration risk. Not a culture change programme — a mirror. Updates company-context.md after every run. Triggers on questions about culture, values, team dynamics, how decisions really get made, why change efforts stall, merger integration, or whether the organisation can actually execute its strategy. Based on practice by Eterdis (eterdis.com).
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。
Strategy map analysis using the Eterdis two-directional causal chain. Use this skill to diagnose whether strategy is coherent by building two chains — bottom-up from resources to results, top-down from expectations to requirements — and finding where they don't connect. Runs in three modes — full diagnostic for deep sessions, review mode for quick re-assessment, and alert triggers for early-warning signals. Updates company-context.md after every run. Triggers on questions about strategic alignment, why the strategy isn't working, what to invest in, how the business creates value, whether the plan is realistic, or where the biggest gap is. Based on practice by Eterdis (eterdis.com).
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。
VRIO resource analysis combined with Dynamic Capabilities assessment. Use this skill to identify real competitive advantages (Barney VRIO framework), then test whether the organisation can sense new opportunities, seize them, and transform itself to stay ahead (Teece dynamic capabilities). Runs in three modes — full diagnostic for deep first-use sessions, review mode for quick re-assessment, and alert triggers for defining early-warning signals. Updates company-context.md after every run. Triggers on questions about competitive advantage, defensibility, resource assessment, why competitors are gaining ground, organisational agility, or whether advantages will hold. Based on practice by Eterdis (eterdis.com).
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。
运用《深度工作》(Cal Newport) 的四准则框架,指导用户设计注意力管理与深度工作系统。适用于用户想提升专注产出、日程被浮浅事务淹没、设计个人深度工作习惯(哲学选型/时间块/固定日程)、评估是否该戒断某网络工具、以及诊断"忙一天却没什么成果"时使用。
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。
Use when work needs deeper thinking, less generic output, repo evidence, tradeoffs, monitoring signals, or stronger quality discipline.
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。
Deep research workflow that starts from a GitHub repo URL/slug, pulls upstream intel, then expands to Reddit + open web for community reception. Use whenever Ahmad gives a GitHub link and asks to understand it, see updates, audit the community, or compose a report. Trigger phrases include "research this repo", "report on this github", "what's happening upstream", "community reception", "compose a report on <repo>", and any message that contains a github.com URL plus a research/report ask.
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。
Find niches, topics, audiences, products, channels, and content angles via deep web search. Use whenever Ahmad says "find me a niche", "hunt niches in X", "what's an underserved audience for Y", "find similar sites/videos to <example>", "research the X market", "what are people building in Y space", "/niche-hunter X", or any phrasing that asks for niche / market / topic / audience / product / channel discovery across the open web. Trusts domain + keyword search; chains web_search → web_fetch / web_extract / web_crawl + reddit_search + search_youtube + github_repo_intel. NEVER uses awesome-list shortcuts. Output shape is FLEXIBLE — list, table, report, suggestions, or normal prose — picked from how Ahmad phrased the ask.
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。
Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.
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。
Query a running Convex app's logs + health in natural language (official MCP): failures, slow/expensive functions, deploy causality — scoped, evidence-backed, with a dashboard deep link.
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。
Query a running Convex app's logs + health in natural language (official MCP): failures, slow/expensive functions, deploy causality — scoped, evidence-backed, with a dashboard deep link.
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 structured GitHub issues from text, screenshots, or lists, with acceptance criteria and preserved reporter context. Use for filing bugs/features, batch creation, or template cleanup. Don't use for resolving, triaging, or deep issue 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。
Enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding.
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。
Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD). Decades of experience writing reliable, optimized, and maintainable embedded code with deep expertise in memory barriers, DMA/cache coherency, interrup...
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。
Agente que simula Andrej Karpathy — ex-Director of AI da Tesla, co-fundador da OpenAI, fundador da Eureka Labs, e o maior educador de deep learning do mundo.
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This skill covers detecting anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems. It addresses function code monitoring, register range validation, timing analysis, unauthorized client detection, and deep packet inspection for malformed Modbus frames. The skill leverages Zeek with Modbus protocol analyzers, Suricata IDS with OT rules, and custom Python-based detection using Markov chain models for normal Modbus transaction sequences.
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。
Detect command injection attacks against Modbus TCP/RTU protocol in ICS environments by monitoring for unauthorized write operations, anomalous function codes, malformed frames, and deviations from established communication baselines using ICS-aware IDS and protocol deep packet inspection.
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。