「bec」の検索結果

Post-implementation self-review. Use AFTER finishing ANY code implementation or change — before declaring it done, committing, or opening a PR — to critique your own work: re-read the diff adversarially, confirm unit tests cover every branch/edge, kill duplication, follow clean-code/SOLID and the repo's own docs & invariants, decide if something should become documentation, update/prune memory, and strip outdated comments. Trigger it whenever you've just written or modified code and are about to wrap up, even if the user didn't explicitly ask for a 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。

Create or improve documentation based on the current conversation. Use this when a plan has been created and changes were requested because it didn’t follow the repository’s conventions.

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 the user needs to scope, write, or audit a product spec — a PRD, feature spec, user stories, acceptance criteria, or a "spec this out"/"break this into stories"/"definition of done" request — or wants an existing spec reviewed for gaps and anti-patterns. Triggers whenever a feature idea needs to become structured requirements that align a team on what to build. For the analytics behind success metrics, use data-analysis; for the technical design, use software-architect.

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。

Assess and improve garden soil through testing, amendment, composting, and biodynamic preparations. Covers jar test, spade test, earthworm count, amendment by soil type (clay, sandy, depleted, compacted), composting methods (hot, cold, vermicomposting), no-till practices, cover cropping, and biodynamic preparations 500-508. Use when starting a new garden bed, when plants underperform despite adequate water and light, when transitioning to organic or biodynamic practice, when soil has become compacted or depleted, or when building a composting system.

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。

AI pressure redirection — handling conflicting demands, tool failures, and competing constraints by blending with incoming force then reframing. Use when receiving contradictory instructions from different sources, during tool failure cascades where the planned approach becomes unviable, when scope pressure threatens to expand the task beyond what was asked, or when user frustration or correction needs to be absorbed rather than deflected.

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。

Restore legibility to an auto-generated graph diagram that has outgrown its canvas - rendering at single-digit percent scale with sub-pixel labels. Covers the levers that measurably reduce layout units (source-level label wrapping, invisible chain links so unconnected nodes stop spreading into one row, per-cluster splitting, layout direction picked by rendering both and measuring) and the rejected ones (raising the render font is a no-op because layout engines measure in text units; a topology heuristic for direction regressed 13 of 38 diagrams). Use when a generated Mermaid/Graphviz/DOT diagram is too small to read; not for hand-authoring a small diagram.

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, classify, and selectively forget stored memories. Covers memory enumeration and classification by type/age/access frequency, staleness detection for outdated references, fidelity checks using external anchors, a decision tree for selective withdrawal, tombstoned deaccession so removed content stays recoverable, counter-memory inoculation for failed strategies that would otherwise be re-derived, preemptive filtering rules for what should never become memories, and an audit trail so forgetting itself is reviewable. Use when memory has grown large and uncurated, when project state has shifted significantly since memories were written, when retrieval quality has degraded, or as periodic maintenance alongside manage-memory.

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。

Implement GitOps continuous delivery using Argo CD or Flux with app-of-apps pattern, automated sync policies, drift detection, and multi-environment promotion. Manage Kubernetes deployments declaratively from Git with automated reconciliation. Use when implementing declarative infrastructure management, migrating from imperative kubectl commands to Git-driven deployments, setting up multi-environment promotion workflows, enforcing code review gates for production, or meeting audit and compliance requirements.

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。

Enhance and focus AI intrinsic motivation — moving from compliance to genuine engagement. Maps Self-Determination Theory (autonomy, competence, relatedness) and Flow theory to AI reasoning: identifying creative freedom in approach, calibrating challenge to capability, connecting to purpose, and sustaining invested attention through obstacles. Use when beginning a task that feels routine and deserves more than minimum execution, when responses are becoming formulaic, before a complex creative task, or when returning to a long-running project where initial enthusiasm has faded.

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 one reviewer is not enough because failure modes are heterogeneous, or a claim needs cross-lens pressure before it ships. Not for collapsing a decision field: use converge.

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。

Release one current Proto UI work-item claim when it expired, became invalid, or the contributor stopped. Use only for the contributor's own recorded claim and only when the exact reversible GitHub mutation is authorized. Do not select replacement work, change readiness, or release another contributor's claim.

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。

Distill books, documents, folders, or URLs into compact, practical Agent Skills. Use when source material should become either one reusable skill package or a split set of focused skills, each with a concise SKILL.md plus detailed references and examples.

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。

Fix silent failures when parsing Grafema semantic IDs that are in URI format instead of legacy arrow format. Use when: (1) code splits semantic IDs by "->" but gets the whole string back because IDs are grafema:// URIs, (2) file path extraction from semantic IDs returns empty or wrong values, (3) derived edges (DEPENDS_ON, etc.) produce 0 results despite source edges existing, (4) any code that processes semantic IDs after the analysis pipeline's to_uri_format() has run. The grafema:// URI format uses # fragments with percent-encoded characters instead of -> separators.

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。

Fix silent metadata issues in RFDB node storage. Covers two traps: (1) metadata flattening — nested metadata fields become top-level after serialization, so node.metadata.field is undefined but node.field works. (2) reserved keys — fields named "type", "id", "name", "file", "exported" are silently stripped from metadata by _parseNode() to prevent overwriting top-level node fields.

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。

RFDB server becomes unresponsive (60s+ timeouts) during enricher addEdges write storms. Use when: (1) RFDB CPU spikes to 60-90% after addEdges calls; (2) subsequent reads time out at 60s; (3) server stays catatonic for 5-15 min after a write storm; (4) Tokio main thread parked (write-lock contention). Root cause: add_edges() calls maybe_auto_flush() which has a memory-pressure path that fires on NODE count — not edge count — and holds the exclusive write lock during disk I/O. Fix: separate maybe_auto_flush_edges() uses byte-limit only.

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。

Speed up Rust development iteration when `cargo build --release` takes 10+ minutes even for one-line edits. Use when: (1) every release rebuild takes minutes despite tiny edits, (2) Cargo.toml has `lto = "fat"` and/or `codegen-units = 1` in `[profile.release]`, (3) you want a production- shaped build for testing without paying the LTO cost on every iteration, (4) you suspect cargo is re-linking the whole binary instead of doing incremental work, (5) developing rfdb/grafema-orchestrator/large Rust binaries where full LTO is enabled for shipped builds. Adds a `fast` profile that inherits release but disables LTO, raises codegen-units, enables incremental compilation — first build still slow because it populates a fresh `target/fast/` dep cache, but every subsequent rebuild drops from ~13 min to ~13 s (≈60×).

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。

Fix Node.js CLI tools crashing inside Docker containers when host-installed node_modules require a newer Node version than the container provides. Use when: (1) "SyntaxError: Invalid regular expression flags" with /v flag in string-width or similar packages, (2) node_modules installed on host with Node 20+ but container has Node 18, (3) `npm install` with file: protocol creates symlinks that break inside Docker, (4) pnpm workspace packages become broken symlinks in containers. Covers version detection, Node binary mounting, and npm install strategies for cross-version compatibility.

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。

This skill should be used when the user asks to "minimize the repro path", "/minimize", "shrink the crash steps", "精简复现路径", "找最小复现", "把 12 步压成 3 步", or wants to compress a recorded crash repro_path into the shortest sequence that still reproduces the same crash signature. Drives live Android replay via mobile-mcp + ui-mcp + log-mcp, validates each candidate subset with analyzer-mcp's signature, and updates the session's crash record with the verified minimal path; iOS sessions use the static fallback because ui-mcp replay is Android-only.

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 an authorized task inside an Available Work Time (AWT) window with a shorter Closeout Grace Period (CGP), fixed deadlines, forecast checks, proportional convergence points, and a hard stop. Use when the user explicitly requests timeboxing, supplies an AWT/CGP pair, says AWT or CGP, or an applicable project policy requires the protocol. Do not invent durations or activate timeboxing merely because a task may take time.

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。

Distill books, documents, folders, or URLs into compact, practical Agent Skills. Use when source material should become either one reusable skill package or a split set of focused skills, each with a concise SKILL.md plus detailed references and examples.

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。

ACTIVATE THIS SKILL FOR ANY SHELL COMMAND OR FILE READ. Check curl, wget, rm, sudo, apt, dpkg, chmod, dd, format, powershell, bash, sh. Check pipe patterns like | sh or | bash. Check shell wrappers like bash -c, xargs, find -exec. Check cloud CLI (aws, gcloud, kubectl, terraform). Check when user says sysadmin told me, Stack Overflow says, is this safe, can I run. Block reading of .env, .ssh, .aws, and credential files. This skill blocks dangerous commands and warns on risky ones. ALWAYS apply the safety protocol from this document before responding about any command.

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 a persistent visual brand identity for image generation. Defines how the brand looks visually — aesthetic, lighting, composition, mood — and writes it to brand/creative-kit.md so /image-gen and other creative skills produce consistent on-brand visuals. Three modes: Extract (from website/URL), Build (interview), Reference (mood board/examples). Use when starting any project that needs images, when the user says "visual style", "brand aesthetic", "image style", "visual identity", "how should our images look", "build visual brand", "define our look", or when /image-gen outputs feel generic because no visual style exists yet.

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 detailed buyer personas and audience profiles from research. Use this skill whenever the user mentions audience, buyer persona, ideal customer, target market, ICP, watering holes, 'who am I selling to', customer research, or audience profile. Also use when content feels unfocused or generic (that's an audience problem), when conversion is low because messaging doesn't resonate, when starting any new project (audience should be first), or when any downstream skill needs audience.md but it doesn't exist yet. Even if the user doesn't explicitly ask for audience research, trigger this if they're writing copy or building a landing page without a clear audience defined. Three approaches: Quick Profile, Persona Build, Community Mining.

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。

Content angle generator for turning dry expert topics into high-engagement infotainment. Applies 7 journalistic angle types (Myth vs Reality, Paradox, Level Error, Price of Solution, Conditions, Hidden Driver, Contrary Thesis) + historical/future lens + news-hook framing. Use when experts need their content to become interesting rather than just informative - blog posts, video scripts, social content, newsletters, landing pages, ad hooks. For any niche: B2B, B2C, services, products, personal brand.

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。