「memory」の検索結果

Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Web runtime package: uses memory in this order: connector-backed, project-pack, none. Never assumes subprocess access or unrestricted local files. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles.

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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and the repo's own docs & invariants, decide if something should become documentation, update/prune memory, and strip outdated comments.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。

Adversarial, security-first review of a pull request from an EXTERNAL or untrusted contributor — a fork PR, a first-time or unknown author, anyone without write access — before a maintainer merges it. Use this whenever you're reviewing, triaging, or deciding whether to merge a PR opened against tbh-meter by someone outside the maintainer team: 'review PR #123', 'someone opened a PR — is it safe to merge?', 'check this contributor's changes', 'can we take this fork PR?', 'is this contribution legit?'. This is supply-chain DEFENSE, not ordinary code review: every merged PR auto-updates onto every user's machine and ships a memory-reading .exe plus an unsandboxed Electron app, so the author and the PR description are UNTRUSTED and the diff is the only evidence. It posts its verdict as a real GitHub review — an inline comment pinned to each problematic line (via the pulls/reviews API), not just one summary comment. NOT for your own or a fellow maintainer's branch (use self-review for that), and prefer this over the generic review flow for anything coming from outside the team.

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 this skill when tool outputs exceed 2000 tokens, tasks span multiple conversation turns, sub-agents need to share state, context window is bloating, plans need to persist across summarization, terminal/log output needs selective querying, or when user mentions "offload context", "dynamic context discovery", "filesystem memory", "scratch pad", "reduce context bloat", or "just-in-time context loading".

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。

Find and fix broken internal links, dead external URLs, stale imports, missing cross-references, and orphaned files. Ensures all project references remain valid and up-to-date. Use when documentation contains broken internal links, external URLs return 404 errors, import statements reference moved or deleted modules, cross-references between files are out of sync, or files exist but are never referenced anywhere in the project. Also use in index mode for a directory of markdown notes with a designated index, such as an agent-memory store, where an orphan is a file the index does not link to.

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Build and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.

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。

Log a bias-log / vipassana observation to the memex store the moment a reasoning pattern surfaces, mid-session or at close. Use when you catch a bias in your own reasoning (anchoring, confirmation, trust-the-summary), when a pace tell or verification gap appears, or when meditate/observe produces a reflection worth persisting across sessions. Primary path pipes the body to `memex add` over stdin (`--title` required) then runs the `meditate-vipassana` extractor to make it queryable; secondary path appends a numbered bullet to `docs/OBSERVATIONS.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。

Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.

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Extract maximum value from existing code — performance optimization, API surface refinement, and dead weight elimination. The art of turning base code into gold through systematic identification and amplification of value-bearing patterns. Use when optimizing a working but sluggish codebase, refining an API surface that has accumulated cruft, reducing bundle size or memory footprint, or preparing code for open-source release — when code works correctly but doesn't shine and needs polish rather than a full rewrite.

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AI internal coordination using stigmergic signals — managing information freshness in context and memory, decay rates for assumption staleness, and emergent coherent behavior from simple local protocols. Use during complex tasks where multiple sub-tasks must coordinate, when context has grown long and information freshness is uncertain, after context compression when information may have been lost, or when sub-task outputs need to feed into each other cleanly without degradation.

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。

Systematically identify whether a GPU kernel is compute-bound, memory-bound, or latency-bound using roofline analysis, occupancy calculations, compute/load ratio per tile, and SASS instruction inspection. Produces a decision matrix for optimization strategy selection (cp.async, warp interleaving, tiling, double-buffering, or CuAssembler hand-tuning).

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。

xUnit, Moq/NSubstitute, FluentAssertions, and coverlet patterns for any .NET project. Covers test structure ([Fact]/[Theory]/[InlineData]), mocking discipline, fluent assertions, integration test setup with WebApplicationFactory, and coverage measurement. Stack-agnostic — referenced by every .NET plugin in the marketplace. Use this skill to: - Write clear, maintainable unit tests with xUnit [Fact] and [Theory]. - Mock dependencies with Moq or NSubstitute without overusing mocks. - Write expressive assertions with FluentAssertions. - Measure coverage with coverlet and enforce a minimum threshold. Do NOT use this skill for: - ASP.NET Core-specific integration tests (WebApplicationFactory, HttpClient — those are in aspnet-core-plugin:aspnet-conventions). - EF Core in-memory or SQL Server LocalDB test patterns (aspnet-core-plugin:efcore-patterns). - C# language idioms — see csharp-foundation:csharp-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。

Etsy SEO optimization for existing and new listings. Auto-detects REWRITE (when user pastes a current title + tags + description) or CREATE (when user gives a short product context). Triggers on "rewrite this listing", "optimize my Etsy listing", "fix my title and tags", "create new listing", "make a new Etsy listing". Uses live Etsy autocomplete + competitor SERP scraping for evidence-driven keyword research. Enforces 2026 Etsy rules — tag ≤20 chars, primary keyword in first 40 chars, indexing spread across title/tags/attributes/description/hero alt, NLP-aware writing, trademark stoplist scan. When the user's problem isn't SEO (CTR, conversion, photos, price, saturated niche), it says so directly and routes to concrete next steps. Silently maintains a markdown listing database at ~/etsy-listings/ for cross-session memory on Claude/Cowork. Catches duplicate primary keywords across the user's own listings.

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 writing CUDA kernels, managing the thread, block, and grid hierarchy, tiling shared memory, using streams, setting nvcc flags, or using Thrust. Not for kernel debugging: use cuda-debugging.

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 configuring DMA channels, circular buffer mode, double buffering, memory-to-peripheral transfers, or DMA IRQ completion on bare-metal MCUs. Not for cache theory: use cpu-cache-opt.

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 profiling with Intel VTune or AMD uProf for hotspots, top-down pipeline stalls, memory-bound analysis, or roofline data. Not for raw perf stat counters: use linux-perf.

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Use when collecting sampling profiles with perf record, reading perf report or perf annotate, measuring PMU counters with perf stat -e, or using PAPI instrumentation to interpret IPC, miss rates, MPKI, or memory bandwidth on Linux. Not for SVG rendering: use flamegraphs.

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Use when choosing kernel spinlocks versus mutexes, using RCU, seqlocks, completions, or memory barriers, or debugging scheduling-while-atomic. Not for userspace atomics: use concurrency-debugging.

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Use when diagnosing cache misses with perf, fixing false sharing, choosing AoS or SoA layout, or adding software prefetch. Not for cache theory: use memory-hierarchy-and-caches.

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 tuning or profiling native code on Apple M-series Macs: unified memory, 16 KiB pages, Accelerate and Metal for matrix work, xctrace and leaks, Rosetta 2, or sysctl hardware queries.

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Use when the user requests memory transfer to or from a named peer. Not for recalling a session: use history-recall. Not for store registration: use history-source-registry.

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Use when asked to mine prior chats on a scheduled or watcher tick and maintain project memory. Not for remote, credential, publish, deploy, or irreversible mutation.

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Use when the user and agent may hold different pictures of current state after a gap, sync request, or exposed stale claim. Not for persistence: use memory-update.

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 a rendering path needs stable frame-time, CPU, GPU, and memory evidence against fixed targets. Not for one-shot profiling or visual quality 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。