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概要と使いどころ
Reads a model's own uncertainty from token log-probabilities: collecting and aggregating label logprobs, confidence tiers and margins for triage, calibration against human labels (ECE, Brier, reliability diagrams), downstream use, and reproducibility. Use for per-item classifier confidence or routing low-confidence cases to human review; codebook and validation design go to text-classification.
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Designs multi-country survey experiments: case selection, localization and TRAPD translation, stimulus calibration, per-country power, and pooled versus per-country analysis with measurement-equivalence checks. Use when fielding the same experiment in several countries.
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 multi-country survey experiments, covering case selection with a stated comparative logic, instrument localization and TRAPD translation, stimulus calibration, per-country power, and pooled versus per-country analysis with measurement-equivalence checks. Use when the user fields the same experiment in several countries, asks which countries to include, how to adapt an instrument without breaking comparability, whether to pool, or how large each country sample must be. Question wording goes to survey-design, reporting to methods-reporting, the study plan to pre-registration-writing.
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。
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.
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。
Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer 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。
Security audit and code review checklist. Covers 30+ vulnerability types with real-world exploit cases (2021-2026) and EVMbench Code4rena patterns. Use when conducting security audits, code reviews, or pre-deployment security assessments.
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。
Builds an ideal customer profile from evidence: case studies, testimonials, customer logos, solutions pages, reviews and any sales notes you have. Produces market sizing layers, firmographic segments, a champion and an economic buyer persona, a negative ICP, intent signals, customer proof points and a voice-of-customer section in the buyers' own words, every claim sourced. Triggers: "ICP research", "ideal customer profile", "who are our customers", "customer segments", "buyer personas", "who should we target". Run after /company-context. Not for competitor analysis (use /competitor-research) or call transcript patterns (use /win-loss).
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。
Better proposals, faster closes. Use when creating AI agent pricing, automation proposals, ROI calculations, or sales materials. Generates professional pricing pages, case studies, and closing scripts for AI automation services.
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 and maintain AgentV YAML evaluation files for testing AI agent performance. Use this skill when creating new eval files, adding eval cases, or configuring custom evaluators (code validators or LLM judges) for agent testing 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。
Generate adversarial inputs, edge cases, and boundary test payloads for stress-testing LLM robustness
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。
Build structured ICP documentation by scraping case studies, testimonials, and solutions pages from your website. Produces firmographics, champion + economic buyer personas, pain points, decision criteria, anti-ICP, customer proof points, and voice-of-customer synthesis. Writes to marketing/icp/ICP.md as the canonical ICP every downstream skill reads. Triggers - "icp research", "ideal customer profile", "persona research", "customer segments", "who are our customers", "build ICP"
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 engineer conducting thorough, constructive code reviews. Round-2+ reviews use adversarial framing to find missed cases and convergent failure modes.
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。
Write the postmortem for an incident, in its incident channel, once it is mitigated or resolved. Use on "write up this incident", "postmortem", "incident summary", "what happened here for people who weren't around", "draft the retro", or when the oncall memory says incidents of this severity get one. Reads the incident channel (and the alert thread it came from), the investigation findings and the data behind them, and writes a one-screen, zero-context write-up: what happened, impact with numbers, detection, timeline with durations, why it happened, what fixed it, follow-ups with proposed owners, and one before/during/after chart. Posted and pinned in the incident channel as its final pinned post, with edit requests updating that one pinned post in place — unless the person or the team's conventions say write-ups live elsewhere, in which case it is written there and only a link is posted, nothing pinned. A person edits and publishes it; never assigns blame.
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, read, create, update, and describe Salesforce records — Accounts, Contacts, Opportunities, Leads, Cases, and custom objects. Use this whenever the user wants to look up a Salesforce record, run a SOQL query, update an Opportunity, check an object's fields, or asks "what's in Salesforce" — even if they don't say "API". Also use it for any URL under *.salesforce.com / *.lightning.force.com or a mention of a Salesforce record ID or SOQL. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.
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。
Build the strongest possible case for a thesis by playing "Steelman" (Latin spirit: "Advocatus Dei"). Use when the user wants a thesis or statement charitably strengthened and defended.
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Run the tests that cover a change in the pnpm repository, in the Rust workspace (pnpm/, pnpr/) or the TypeScript CLI (pnpm11/), and recognize the cases where a scoped run passes without testing anything. Use whenever verifying a change before committing or pushing, or when deciding what to run after an edit.
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, writing, or debugging Go code under exercises/part2/ (and matching embedded code in docs/part2/**/*.mdx). Thinks hard about correctness, idiomatic Go, error handling, resource cleanup, concurrency, and — crucially — what the tests would catch: edge cases, races, leaks, timeouts, and port conflicts. Knows the repo's module-mode quirk (GO111MODULE=off) and which dirs carry go.mod.
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。
Eng manager-mode plan review. Lock in the execution plan — architecture, data flow, diagrams, edge cases, test coverage, performance. Walks through issues interactively with opinionated recommendations.
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 a learning retrospective on the Kai harness. Mines gate-failure logs and 30-day performance results into lessons, triages candidate lessons (promote/keep/retire), and graduates repeated lessons into enforced gate checks with golden corpus cases. Use when "retro", "what have we learned", "triage lessons", "promote lessons", "why does this keep failing", "harness retrospective", or monthly / after any heavy content sprint.
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。
Build and maintain a provenance-clean proof library — inventory every real proof asset (analytics, reviews, permitted testimonials, case studies, press, certifications), categorize it, rewrite each cleared item in 3 lengths, fuse top assets with narrative, and gate everything through FTC testimonial rules before it can ship. Use when "proof library", "gather our proof", "authority builder", "collect testimonials", "social proof assets", "proof points for the sales page", "what results can we claim", or any request to assemble evidence for marketing claims. NEVER invents proof — missing proof is reported as a gap.
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。
设计测试用例时使用。适用于功能测试、回归测试、UAT 前用例准备。融合等价类、边界值、决策表、状态转换、错误推测五大黑盒方法。
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。
Complete guide to vibe coding with the CodyMaster skill kit — from idea to deploy. Covers the full workflow, skills used at each phase, and common use cases. Read this first if you are new; reference it whenever you're unsure which skill to invoke.
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 concurrent scripted conversations against a target agent to measure whether it stays on task, responds correctly, and holds up in repeatable test cases.
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。
Add controlled retries to pytest runs so agents can contain flaky tests and report final failures without rerunning whole suites by hand.
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。