「md」の検索結果

Bu Flutter template'inde uctan uca yeni feature iskelesi kurar — lib/feature/<ad>/ klasor yapisi, Freezed state + Cubit, StatelessWidget ya da StatefulWidget+ViewModel view, BlocProvider yerlesimi (global state_initialize mi lokal mi), TypedGoRoute, LocaleKeys cevirileri, AppPaddings/textTheme/colorScheme token'lari ve zorunlu <feature>.md modul dokumani. Kullanici "yeni feature ekleyelim", "su sayfayi projeye gore yaz", "cubit + state + view kur", "feature iskeleti cikar", "scaffold a feature" dediginde kullan.

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。

Bu template'in konvansiyonlarina gore degisen dosyalari denetler — Cubit+Freezed state kullanimi, servis/locator yerlesimi, view tipi secimi, dort-durum ele alinisi, tasarim token'lari (renk/tipografi/padding/radius), LocaleKeys, TypedGoRoute, zorunlu <feature>.md ve very_good_analysis kurallari. Kullanici "degisiklikleri incele", "bu PR'i review et", "projeye uygun mu", "mimari kontrol", "review my changes" dediginde kullan.

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。

Decide where to save any piece of information — monday.com, local file, daily notes, or MEMORY.md. Use this skill before saving anything to ensure the right destination. Prevents local clutter and ensures the owner can access all relevant content in monday.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。

DEPRECATED — merged into monday-for-agents. See skills/monday-for-agents/SKILL.md.

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。

Multi-tiered memory management (HOT/WARM/COLD) for context compaction. Invoke ONLY for explicit compaction events: post-`/compact` cleanup, MEMORY.md tier promotion, archive batch, or "trim my context". NOT for general recall (use deep-recall) or routine memory writes (use storage-router). Triggers: "compact memory", "promote to durable", "archive old context", "tier this".

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。

OpenClaw API gateway proxy for authenticated SaaS calls (100+ services in references/SERVICES.md). Use ONLY when making an HTTP request to a third-party service that requires the gateway's stored credentials. NOT for direct API calls with bearer tokens already in scope, NOT for monday.com (use monday-for-agents), NOT for WhatsApp (use heleni-whatsapp). Triggers: "call the X API", "fetch from <service>", "send to Slack via gateway", "use the gateway for".

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。

Single source of truth for all contacts, PAs, and WhatsApp groups. Use when: looking up a phone number, email, address, PA, group JID, adding/updating a contact, or before sending any WhatsApp message. Reads and writes contact-list.md in the workspace root.

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。

Runs before every git commit on heleni-memory. Checks: no credentials in tracked files, no oversized files, valid SKILL.md frontmatter for any new/modified skill. Triggers: "commit", "before push", "validate repo", or auto-runs as a git pre-commit hook.

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。

AiZynthFinder retrosynthetic route planning (CASP) from AstraZeneca Molecular AI. Monte Carlo tree search guided by a template-based neural expansion policy recursively disconnects a target SMILES until precursors are found in a purchasable stock. Covers config.yml (v4 format), aizynthcli batch screening, the AiZynthFinder/AiZynthExpander Python API, one-step disconnections, custom stocks via smiles2stock, scorers, Retro*/breadth-first/DFPN search alternatives, and reading output.json.gz / trees.json. Use for synthesis route planning, synthesizability screening, and building-block/precursor search. For reaction barriers use neb-irc-activation-energy; for 2D reaction scheme drawing use rdkit-chemdraw-cdxml.

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。

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

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, list, and apply Stitch design systems from DESIGN.md using the verified Stitch MCP design-system tools and safe upload fallbacks.

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。

Convert approved Stitch exports into accessible React and Vite dashboards with DESIGN.md tokens, TanStack Query data boundaries, responsive layouts, and optional read-only Web3 integrations.

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 opinionated premium DESIGN.md guidance for Stitch, emphasizing calibrated typography, restrained color, layout discipline, motion, and anti-generic UI 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。

Upload approved local HTML, markdown, or image assets to a Stitch project using direct MCP for small DESIGN.md files or the bundled API script for larger files.

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。

Convert an existing frontend into Stitch-ready design assets by extracting static HTML, writing DESIGN.md, creating the design system, and uploading approved files.

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。

Discover, validate, and invoke .agent.md custom agents. Use when finding agent files in the local Claude or VS Code Insiders directories, checking frontmatter, verifying disable-model-invocation, or determining agentName for runSubagent calls.

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。

Guides the discovery, design, validation, and delivery of Agent Skills. Use when identifying skill gaps, creating or revising a SKILL.md, improving skill descriptions, or checking a skill against the Agent Skills specification.

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。

Audit and update an arbitrary codebase to follow the Code.mil federal open-source licensing model (MIT with 17 U.S.C. Section 105 disclaimers and INTENT.md), including CRAN/R package DESCRIPTION setup.

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 planning workflow for any coding task. Use at the start of every new feature, bug fix, refactor, or implementation request. Analyzes the project, asks up to 5 clarifying questions, creates a TODO.md, gets user approval, then executes task by task. Never writes code before a plan is approved.

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。

Drive a whole tdmcp feature BACKLOG to completion as resumable, wave-by-wave releases — the campaign layer ABOVE tdmcp-pipeline/tdmcp-feature-lead. Use whenever the user wants to implement an ENTIRE backlog or discovery file (e.g. _workspace/discovery*/FEATURE_BACKLOG*.md), 'all the features', many features across multiple releases, or a long autonomous build campaign, AND for every follow-up: continue/resume the campaign, run the next wave, re-run a failed wave, fold in QA results, or check campaign status. Backed by a ledger.json so re-running is idempotent (skips shipped work, resumes interrupted work) and resilient (retry-once then quarantine-and-continue). For a SINGLE feature or one small batch, use tdmcp-pipeline instead; this skill is for the whole ledger.

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。

Updates tdmcp docs and roadmap for newly shipped or newly merged capabilities. Use when asked to update docs, update the roadmap, reconcile release docs, document new features, sync docs with CHANGELOG/package release, refresh cookbook examples after a feature wave, re-run/continue/fix a docs-roadmap pass, or check whether docs/ROADMAP.md is current. Runs a sub-agent workflow: release audit -> roadmap/docs edit + cookbook sync -> QA.

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。

Consolidate the five tdmcp surveyor reports into one prioritized FEATURE_BACKLOG.md — dedupe cross-surface overlaps, reconcile every candidate against the roadmap (NEW vs planned vs extension), rank by a selectable impact×effort weighting profile into P0/P1/P2 (high-confidence first), and emit per-surface tables + a recommended-next shortlist that hands straight to the build pipeline. Use when a td-synthesizer agent is consolidating surveys during the feature-discovery harness.

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。

Set up the current project folder for AOS: slug, wiki, memory, Synapse map, AGENTS.md. Generated from plugin-commands.json; invoke as $bdb-aos:init.

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。

Start a new private GitHub project the AOS way: create the private repo, bootstrap AOS (project card, AGENTS.md, wiki, memory, CI choice), push after GO, and confirm the first CI run is green and the repo is still private. Use for "new project", "start a repo", "bootstrap a 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。