開発のスキル

GitHub - skyiron/crushAIcodingAgent: The glamourous AI coding … — The quickest way to get started is to grab an API key for your preferred provider su

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AI Agent Index Categories Find Agent + Submit Compare Alternatives Stacks Advertise API Home / AI Coding Agents Best AI Coding Agents (2026): IDEs, Te

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Protocol ACP | Techzine Oct 08, 2025 Zed Code Editor Adds Agent Protocol for Flexible AI Integration | WebProNews Aug 28, 2025 Google Integrates Gemin

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Mouse Precision Editing Tools for AI Coding Agents HIC Mouse gives AI coding agents precision file editing tools 56% higher accuracy, 58% lower cost,

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F Agent Memory: A Git-Native Persistent Memory Solution for AI Coding Agents and Project Knowledge Management OKF Agent Memory introduces a standardiz

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Terminal-Interface, Desktop-App und IDE-Extension Doku lesen Der Open-Source AI-Coding-Agent [*] Mit über 195,000 GitHub-Stars, 950 Contributors und ü

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Session stores conversation history for a specific session, allowing agents to maintain context without requiring explicit manual memory management.

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March 27, 2026 2 min read --> --> --> --> --> Uncomfortable Truths About AI Coding Agents: What the Industry Needs to Know Artificial intelligence is

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Manage Apple Notes via memo CLI: create, search, edit.

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Use for the self-evolving civilization - skills, memories, swarm agents, energy economy, grid duels, metacognition. Run `python scripts/autonomous_loop.py --loop` from the repo root (or `darwin_engine.py --full` for skill evolution only).

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Comprehensive Stata reference for writing correct .do files. Covers syntax, options, gotchas, and idiomatic patterns. Use this skill whenever the user asks you to write, debug, or explain Stata code. Generates ready-to-run .do files for the user to execute manually.

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Monitor PR review comments and automatically classify and address reviewer feedback including code changes, questions, and nits. Use when handling PR reviews, addressing reviewer comments, responding to code review feedback, or automating review resolution. Triggers on handle reviews, PR review, address feedback, reviewer comments, code review, review response.

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Detects deeply nested loops with duplicated inline logic and recommends extracting into small, named functions. Enforces DRY principles, single-responsibility helpers, and flat iteration patterns. Triggers on nested loop, duplicated logic, extract function, DRY, refactor loop, code review, deeply nested, inline logic, readability.

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Jira ticket operations via Atlassian MCP including view, search (natural language to JQL), create, update, comment, and transition with auto-detection of ticket IDs from git branches. Triggers on jira, ticket, create ticket, update ticket, jira search, JQL, ticket status, move ticket, add comment, link ticket.

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Create a pull request from the current branch. Triggers on create PR/open PR/make PR/submit PR/push PR/raise PR/open a pull request/create a pull request/ready to merge/branch is ready when the user wants to turn their current branch into a GitHub pull request with a well-structured description

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Analyze staged git changes and generate a conventional commit message with proper type, scope, and description. Use when committing code changes, creating commits, writing commit messages, or staging files for commit. Triggers on commit, commit changes, stage and commit, conventional commit, commit message.

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Interactive guided tour of all available AI coding skills with live demos. Walks through headline capabilities, offers try-it-now demos, discovers repo-specific tools, and provides a cheat sheet reference. Triggers on what can you do, show skills, skill tour, available tools, capabilities, what skills.

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Use when controlling NVIDIA Broadcast through MCP to apply effects, process local media, select devices, or change camera resolution, or when Broadcast is missing or too old to expose the gateway and the user wants it installed; not for Broadcast app settings outside the MCP gateway.

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Knowledge base from NIST AI Risk Management Framework 1.0 (NIST AI 100-1). Use for AI risk management, trustworthiness characteristics, GOVERN/MAP/MEASURE/MANAGE functions, bias management, AI actor roles, TEVV, AI RMF profiles, and socio-technical risk framing. Does not cover sector-specific regulations, EU AI Act, ISO 42001 in depth, or the AI RMF Playbook tactical detail.

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Knowledge base from NASA-STD-8719.14C (Process for Limiting Orbital Debris). Use for orbital-debris assessment planning, mission-related debris limits, explosion/passivation and collision risk, postmission disposal options, reentry casualty risk, and ODAR/EOMP governance. Covers STD-8719.14C (Approved 2021-11-05) synthesized notes only; does not replace NPR 8715.6 delivery timing, NASA-HDBK-8719.14 environment/modeling depth, or program-specific OSMA waivers.

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Knowledge base from NASA-STD-7009B (Standard for Models and Simulations) with implementation depth from NASA-HDBK-7009B. Use for M&S criticality and programmatics, development evidence and capability assessment, verification/validation domains, uncertainty and sensitivity, use-phase results assessment, risk-informed decision reporting, and handbook life-cycle products. Covers STD-7009B (2024-03-05) + HDBK-7009B (2026-02-03) synthesized notes only; does not replace program-specific TA direction, NPR 7150.2 software requirements detail, or full PRA methodology.

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Knowledge base from the GAO Technology Readiness Assessment Guide (GAO-20-48G, 2020). Use for evaluating technology maturity in acquisition programs and projects: Technology Readiness Levels (TRL 1–9) and related scales (MRL, IRL), identifying Critical Technology Elements (CTEs), the five-step process for a high-quality TRA, the four characteristics of a reliable TRA (comprehensive, well-documented, credible, defensible), Technology Maturation Plans (TMPs), and using TRA results in knowledge-based, milestone decisions. Assessment/maturity oriented: it measures readiness and frames risk — it is not a full SE process model, cost-estimating, or schedule guide (see sibling packs for those).

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Knowledge base from NASA Risk Management Handbook (NASA/SP-2011-3422). Use for risk-informed decision making, continuous risk management, performance commitments, risk scenario diagrams, risk disposition types, risk burn-down schedules, objectives hierarchies, deliberation and alternative selection, risk driver identification, and RM planning for space and safety-critical programmes. Does not cover domain-specific PRA methods (fault trees, event trees) in procedural depth — see NASA PRA Procedures Guide for those.

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Operaciones de archivos en lote (delete, move, copy, list) con patrones glob. Herramienta CLI para operaciones eficientes en el sistema de archivos. Sin dependencias.

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。