「eas」の検索結果

Design phishing and social engineering simulations with explicit authorization and measurable outcomes.

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。

Pull the latest GEO-SEO skill updates from the upstream repository. Compares installed files against the latest release, shows what changed, and updates all skills, agents, scripts, and schema templates in place.

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 one release phase against the specs — plan it on an interactive board (endpoints, components, resources, with what already exists read from the codebase), let the user adjust, then build node by node while the board shows live progress. Use when the user wants to build a phase, implement the phase-1 slice, execute the plan from a story map or spec, or watch a build happen visually.

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。

Pressure-test startup ideas against real user pain, current behavior, competitors, first-customer access, MVP scope, founder-market fit, and a two-week launch test. Produces a direct strong, weak, or pivot verdict.

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。

GStack canary method for staged release thinking, smoke gates, rollout checks, and rollback signals before or after deployment.

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。

GStack learn method for capturing reusable lessons, triggers, and future retrieval cues after debugging, implementation, or release work.

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。

GStack engineering-manager plan review for architecture, data flow, APIs, migrations, edge cases, tests, performance, release risk, and implementation boundaries.

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。

GStack health-check method for broad project or repository health review across tests, docs, DX, release readiness, and maintainability.

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。

GStack CSO method for broad infrastructure-first security posture review, threat areas, severity calibration, and safe remediation scope.

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。

GStack land-and-deploy method for explicit landing and deployment workflows with tests, release notes, rollout gates, and rollback planning.

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。

GStack ship method for final landing, PR, changelog, release, and deploy readiness checklists when the user explicitly wants to ship.

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。

GStack office-hours method for early product ideas, startup demand checks, builder-mode brainstorming, narrow wedge selection, and pre-architecture clarification before plan review.

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。

GStack review method for pre-landing code, diff, PR, or implementation review focused on bugs, regressions, missing tests, and release risks.

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。

GStack deployment setup method for planning environment, config, secrets, build, deploy, and verification gates before release operations.

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。

Design and implement unit, conformance, integration, timing, boot, trace, video, audio, and regression tests for EMULATORS processors, chips, peripherals, and complete machines. Use when creating a verification strategy for a new emulator, converting hardware documentation into tests, adding diagnostic programs or golden artifacts, measuring behavioral coverage, or proving that components interact correctly.

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。

Measure and audit C/C++ code size, namespace distribution, function complexity, nesting, recurrence, and namespace coupling in the EMULATORS framework. Use when Codex needs codebase statistics, complexity baselines, namespace comparisons, duplication indicators, hotspots, architectural growth analysis, or before/after complexity reports while excluding third-party code.

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。

Structured Domain Modeling Architecture (SDMA) principles. Trigger when: - Designing domain models, building ologs (ontological graphs), verifying protocol equivalence, selecting schemas, or measuring architectural entropy. - Modeling structures using Category Theory, Coalgebra, or Information Theory. - Prompt contains keywords: sdma, olog, Category Theory, Coalgebra, Information Theory, entropy, schema, pullback, functor, monad.

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。

Observe the harness itself — (WS1) measure context budget as a deterministic, trendable inventory (tokens per CLAUDE.md / skill / agent, lexical redundancy, per-CI cold-write totals; committed history.jsonl), (WS2) reconstruct the real invocation trace (which skills/agents/tools fired and how they nested) from session transcripts, and (WS-A) roll the per-run CI traces up into a committed weekly usage record (per-skill invocation counts; usage.jsonl) — the usage side of the dead-weight join. Use for "context budget", "how big is CLAUDE.md / our skills", "is the harness bloating", "how do our agent loops actually run", "which skills actually get used", "trace this session", "loop station".

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。

How to MEASURE this repo instead of eyeballing it — interpret the loop-station context-budget trend (history.jsonl, thresholds, the tokenizer-switch gotcha), read a session trace, use the eval ledger (cost-per-success), and pick the right evidence tool (app-shots, smoke-deployed, db-counts, --check modes). Use when asked "is the harness bloating", "how big is CLAUDE.md really", "did this agent run actually succeed", "prove this works", "what evidence do we have", or when tempted to declare a capability (browser, tool) unavailable. For RUNNING the loop-station scripts, defer to the loop-station skill — this skill is interpretation + the wider evidence toolkit.

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。

A pessimistic go/no-go the moment you pick up a GitHub issue — read it skeptically and hunt for reasons NOT to do it (already done? obsolete? duplicate? premise wrong? cheaper way? net-negative?), returning proceed / reshape / drop with one-line evidence. The pickup-time half of the dedup gate (the create-time half is /issue-dedup). Use as step 1 of the Task Execution Workflow, or run /issue-sanity-check

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 a crouton app to Cloudflare Workers PRODUCTION (<app>.friendlyinter.net). A deliberate, human-initiated action — separate from the default staging /deploy skill. Use ONLY when explicitly asked to ship/release to production. Never run as part of routine work or an automated flow.

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。

System reference for the crouton layout engine (@fyit/crouton-layout) — the LayoutTree/layout_configs data model, the croutonLayoutBlocks registry and sizing contract, the viability metric, the deterministic placer (#709), and how layouts persist and seed. Use when reasoning about WHY a layout renders/composes/persists the way it does, when a question is shaped like "what is a LayoutTree / where do layouts live / why did the placer pick this arrangement / why won't this pane shrink / where does minWidth get read", or when a generated POC boots with the wrong (or no) default layout. Trigger phrases: "layout tree", "layout_configs", "viability check", "composeDefaultLayout", "block sizing contract", "why is my layout not viable". For authoring a block component, use the block-authoring skill instead.

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。

Data Scientist role within the AID methodology. Activate when working on: EDA, feasibility assessment, experiment planning, ML architecture, model development & validation, or production deployment & monitoring. Stack-agnostic — ask the user for their stack at the start of any new project.

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 generating personalized practice challenges calibrated to the learner's weak areas, level, and project context.

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。