「heap」の検索結果

Audit, recover, and prepare portable handoffs of existing private PFC2D/PFC3D 5.0 asphalt cases, including integrity manifests, path portability, saved-state claims, cheap restore-first validation, and legacy-state quarantine.

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。

"Apply Christensen's Disruptive Innovation theory to assess low-end and new-market threats to incumbents. Use this skill when the user needs to evaluate whether a new entrant poses a disruptive threat, analyze why incumbents fail against inferior-but-cheaper alternatives, or design a disruption strategy targeting overserved customers.".

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。

"Apply Granovetter's embeddedness theory to analyze how economic behavior is embedded in ongoing social relations, avoiding both over-socialized and under-socialized accounts. Use this skill when the user needs to explain why market transactions deviate from pure economic rationality, analyze how trust and social ties shape business dealings, evaluate structural vs relational embeddedness in inter-firm networks, or when they ask 'why do firms prefer existing partners over cheaper alternatives', 'how do social relationships shape economic outcomes', or 'is this market truly arms-length'.".

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。

Identify and exploit misconfigured Spring Boot Actuator endpoints. This skill covers how to extract sensitive configuration details, heap dumps, environment variables, and ultimately escalating to Remote Code Execution (RCE) via `spring-cloud-starter` vulnerabilities.

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。

Arbitrary write to RCE playbook. Use when you have an arbitrary write primitive (from heap exploitation, format string, or OOB write) and need to convert it into code execution by targeting GOT, hooks, _IO_FILE vtable, exit_funcs, TLS_dtor_list, modprobe_path, .fini_array, or C++ vtables.

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。

Code review by several models at once — Claude sub-agents, OpenAI Codex, and a cheap third reviewer via OpenCode — reconciled into one report. Reviews whatever is named: a diff, a branch, specific files, one function, a legacy module, a whole repo. Use for any review request, a second or third opinion, a cross-check before a PR, or "code-review", "multi", "consensus 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。

Divergent ideation across model families — the same brainstorm, but each cognitive frame is handed to a different model (Claude sub-agent, OpenAI Codex, a cheap third via OpenCode) so the ideas diverge by frame and by model priors at once. Use for open design questions, architecture and API shape, naming, schema choices, fuzzy bugs with no known root cause, or on "multi adhd", "brainstorm with everyone", "wide ideas from all models". Skip for lookups, syntax, and anything phrased as "quick", "standard" or "canonical".

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。

Put one question to several models at once — you, OpenAI Codex, and a cheap third via OpenCode — and show all the answers side by side. No judging, no consensus: three opinions, the user picks. Use for "ask everyone", "what do the other models think", "second opinion", "multi ask", or any open question where one model's answer is not enough.

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。

Search and inspect the user's photo library, including connected device galleries. Use for photo requests and whenever a photo could ground or personalize a response; lookups of uploaded photos are cheap, so check opportunistically and move on if nothing fits.

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。

Search travel with Amadeus: flight offers and prices, airport autocomplete, hotel offers, cheapest dates. Trigger phrases: amadeus, flight search, flight prices, hotel search.

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 a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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 Lightning record page, home page, or app page is slow to load or render — covers Experienced Page Time (EPT) analysis, component count reduction, progressive disclosure via tabs and conditional rendering, Lightning Experience Insights diagnostics, and DOM/XHR minimization strategies. Triggers: 'Lightning page is slow', 'EPT is high', 'record page takes too long to load', 'too many components on page', 'Lightning Experience Insights shows slow page', 'how to optimize Lightning page performance'. NOT for Visualforce page performance (separate concern). NOT for Apex or SOQL query optimization (use apex/apex-cpu-and-heap-optimization or data/soql-query-optimization). NOT for report or dashboard performance (use admin/report-performance-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。

Router for the 159 SfSkills `apex` skill packages. Apex and SOQL: triggers, Apex governor limits, async processing, OUTBOUND HTTP callouts, security enforcement, and test patterns. Owns calling an external API FROM Salesforce; salesforce-integration owns inbound. Generic nightly scheduling without naming code belongs to salesforce-flow. Codebase security review belongs to salesforce-security. NOT for SOSL — use salesforce-data. Use when the request mentions Apex, trigger, SOQL, Apex governor limit, batch, queueable, @future, schedulable, test class, CPU time, heap, with sharing, StripInaccessible, callout, HTTP callout, HttpRequest, call an external API, consume a REST API from Apex, Named Credential in Apex, HttpCalloutMock, Apex REST service. Finds and opens the exact skill package to read; it does not contain the guidance itself.

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。

Firmware development for the 30-pin NodeMCU ESP8266 devkit — NodeMCU DevKit V1.0 / Amica (CP2102) and LoLin NodeMCU V3 (CH340G), built on the Ai-Thinker ESP-12E or ESP-12F module (ESP8266EX, 4 MB flash), with PlatformIO and the Arduino ESP8266 core (`board = nodemcuv2`). Use when working on a NodeMCU, ESP-12E, ESP-12F or ESP8266 devkit: project setup, platformio.ini, the D0-D10 vs GPIO numbering, which pins are safe and which break the boot, GPIO15/GPIO0/GPIO2 strapping, the A0 divider and 1 V ADC, software I2C and software PWM, analogWrite range, LittleFS and flash layouts, OTA, deep sleep and the missing GPIO16-to-RST link, Wi-Fi, heap exhaustion, watchdog and Soft WDT resets, exception decoding, flashing over CP2102/CH340, or debugging why the board does not boot, prints garbage, or reboots by itself.

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。

Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.

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。

Teaches the AI to design like a high-end agency. Defines the exact fonts, spacing, shadows, card structures, and animations that make a website feel expensive. Blocks all the common defaults that make AI designs look cheap or generic.

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。

Framework-agnostic agent-to-agent payment system with SHIB on Polygon. Provides trustless escrow, price negotiation, and reputation system. 9,416x cheaper than traditional escrow (~$0.003 gas).

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 cheap, TEE-verified AI models from the 0G Compute Network as OpenClaw providers. Discover available models and compare pricing vs OpenRouter, verify provider integrity via hardware attestation (Intel TDX), manage your 0G wallet and sub-accounts, and configure models in OpenClaw with one workflow. Supports DeepSeek, GLM-5, Qwen, and other models available on the 0G marketplace.

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。

Commit, push, and open a PR using the cheap low-token git-ops agent. Use for git/github wrap-up — git commit, git push, gh pr create, "ship it", "open a PR", "create the PR", "push and PR", end-of-task git handoff.

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。

Turn a raw idea into ONE honest recommendation: interview one question at a time until the problem, requirements, goals, non-goals, and constraints are CONFIRMED, then deliver a single verdict — do, don't, cheaper alternatives, trade-offs, ordered checklist, success metrics. Required: the user wants exactly ONE recommendation — not options, not a debate — and their framing is open to challenge (advise may answer a different question than the one asked). Triggers on "what should I do about X", "advise me on X", "am I approaching this right", "is this the right move". Do NOT use to be interrogated with no recommendation (→ mk:grill) — advise also interviews one question at a time, but only to reach a verdict. Do NOT use when the framing is settled and only the options are open (→ mk:brainstorming). Do NOT use to decide whether something is worth building (→ mk:office-hours). Do NOT use when you want several perspectives to argue it out (→ mk:party).

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。

Apply recognition-primed decision making to agents operating under time pressure, uncertainty, and ambiguous cues. Use for situation assessment, RPD-style action selection, and decision-support design in operational settings. NOT for batch optimization, static planning with full information, or tasks where exhaustive search is cheap and harmless.

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。

Identify direct miRNA-target interactions from AGO HITS-CLIP, AGO-CLEAR-CLIP (chimeric reads), HEAP (Halo-Ago2 mouse), chimeric eCLIP / miR-eCLIP (deep miRNA-target profiling), or CLASH using chimeric-read processing pipelines, seed-pairing analysis, and 3' auxiliary pairing rules. Use when distinguishing direct miRNA targets from indirect, integrating CLIP-derived target maps with TargetScan / miRDB / DIANA predictions, applying canonical 7mer-8mer seed matching with 3' UTR context, or recovering miRNA-mRNA chimeras at scale.

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 and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.

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。

Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when correctness matters more than speed, when working in unfamiliar code, when stakes are high (production, security-sensitive logic, irreversible operations), or any time a confident output would be cheaper to verify now than to debug later.

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。