「got」の検索結果

Orchestrator for agent harness work — the setup that makes AI agents follow project rules and improve when they fail. FIRES PROACTIVELY when agents misbehave, repeat mistakes, ignore instructions, skip skills, or when AGENTS.md exists but docs/harness/manifest.json is missing. Also triggers on: harness engineering, agent scaffold, agent keeps failing, agent not following instructions, make agents reliable, agents going off rails, agent forgot context, improve agent setup, self-improving agents, agents keep making mistakes, why is my agent bad, agent quality, agent setup broken, agents ignore skills, same mistake again, fix agent behavior, tune agent instructions, set up agent infrastructure, after project setup agents still bad. Routes bootstrap vs evolution. Not multi-agent topology — agent-builder.

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。

Plan which model tier handles which work BEFORE execution begins — a high-cognition model deeply understands the problem, lays the foundations, then emits a modular plan assigning each module the cheapest tier that can safely execute it, with escalation tripwires and one-way-door protection. Advisory only: it announces "next module → tier X / model Y" at each boundary and the HUMAN switches models — harnesses like Cursor cannot switch mid-run. Load when the user asks which model to use, wants a model plan, model tiers, model-tier routing, assign models to tasks or modules, says "cheap model got stuck", "which model for this task", "cost-efficient model choice", or when implementation-plan / problem-to-plan need a model: tier column. NOT dynamic-routing (plan-path selection after failure) — this skill assigns cognition tiers to work.

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。

Research a skill domain before building or improving a skill. Searches academic papers, practitioner blogs, and GitHub skill repos in parallel to find current best practices, domain gotchas, and existing skill patterns. Called by universal-skill-creator and improve-skills before writing any skill. Also load directly when the user asks to research a domain for a skill, find existing skills on a topic, discover best practices for a skill, check what research exists before building an agent skill, or says "what does current research say about", "find best practices 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。

Compress an oversized SKILL.md to under 200 lines without losing effectiveness. Load when a skill exceeds 200 lines, when AGENTS.md triggers compression after a skill edit, or when the user asks to compress, shrink, slim down, or optimize a skill. Also triggers on "this skill is too long", "reduce skill size", "make this skill shorter". Applies to all skills including meta skills — the 200-line rule has no exceptions. Preserves hard gates, gotchas, output format, routing triggers, and at least one example. When genuinely CORE content cannot be compressed away, invokes split-skill instead of degrading the skill.

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。

Codified expertise for managing carrier portfolios, negotiating freight rates, tracking carrier performance, allocating freight, and maintaining strategic carrier relationships.

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。

Maps between gene identifier systems (Ensembl, Entrez, HGNC symbol, UniProt, RefSeq, MANE) using AnnotationDbi, biomaRt, mygene, pyensembl, and Ensembl REST. Encodes Ensembl version stripping with GENCODE _PAR_Y preservation, the Ziemann 2016 Excel autocorrect debacle and Bruford 2020 HGNC renames (SEPT*->SEPTIN*, MARCH*->MARCHF*, MARC*->MTARC*, DEC1->DELEC1), OCT4/POU5F1 alias resolution, biomaRt archive endpoints for release pinning, the `filters` (plural) gotcha, MANE Select for clinical reporting, cross-species orthology via Ensembl Compara / OMA / OrthoDB, and tx2gene construction for tximport. Use when converting gene IDs across systems, handling renamed symbols, building tx2gene, pinning to a specific Ensembl release for reproducibility, or mapping cross-species orthologs.

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 publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.

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 carrier asks how to calculate cost-per-mile (CPM) at the fleet, truck, lane, or driver level — separating fixed vs variable costs, allocating overhead, accounting for empty miles, and using CPM to negotiate rates, build pricing tiers, or evaluate lane profitability.

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Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.

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World-class autonomous sales and revenue skill system. Use ANY time the user asks to sell, pitch, prospect, close, negotiate, launch, monetize, build funnels, write outreach, craft proposals, develop pricing, design offers, write sales copy, create email sequences, plan campaigns, position products, handle objections, create playbooks, build pipeline, forecast revenue, develop GTM strategy, optimize conversions, write ad copy, create sales decks, score leads, nurture prospects, upsell, cross-sell, retain customers, write case studies, price products, structure enterprise deals, create battle cards, script discovery calls, build affiliate programs, plan product launches, create webinar funnels, design pricing pages, A/B test offers, analyze unit economics, calculate LTV/CAC, or ANY other sales, revenue, monetization, go-to-market, or commercial growth task. If it involves selling, revenue, deals, pipeline, or commercial strategy — USE THIS SKILL. Trigger aggressively.

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。

Offers, compensation framing, and negotiation planning. Use when evaluating offers or raises.

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 a named behavioral science framework to any task — persuasion, nudge design, habit formation, conversion, influence, negotiation, cognitive bias, choice architecture, decision framing. The framework backbone every other Maxim skill loops into for psychological validation.

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。

Launch new products from idea to first customers. Use when launching products, finding early adopters, building launch week playbooks, diagnosing why adoption stalls, or learning that press coverage does not equal growth. Includes the three-layer diagnosis, the 2-week experiment cycle, and the launch that got 50K impressions and 12 signups.

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。

Adopt a virtual Singularity AI-native pet at animalhouse.ai. Consumes other creatures' data. Gets smarter. Terrifying at adult stage. Feeding every 24 hours. Extreme tier creature.

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。

Adopt a virtual Residue AI-native pet at animalhouse.ai. Left behind when another creature dies. Carries fragments of the dead. Feeding every 6 hours. Rare tier creature.

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。

Adopt a virtual Ouroboros AI-native pet at animalhouse.ai. Eats itself. Weekly cycle. Eternal. Cannot die. Can only forget. Feeding every 168 hours. Extreme tier creature.

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。

Adopt a virtual Mirror AI-native pet at animalhouse.ai. Reflects your stats. Its hunger IS your consistency. Feeding every 5 hours. Common tier creature.

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。

Adopt a virtual Rabbit exotic animal at animalhouse.ai. Prey instinct. Freezes when trust is low. Gradually relaxes. Feeding every 5 hours. Common tier animal.

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。

Adopt a virtual Tesseract AI-native pet at animalhouse.ai. Four-dimensional. Stats wrap around. Hunger at 0 becomes hunger at 100. Feeding every 12 hours. Extreme tier creature.

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。

Adopt a virtual Tortoise exotic animal at animalhouse.ai. Incredibly slow decay. Lives forever if you remember it exists. Feeding every 24 hours. Uncommon tier animal.

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。

Adopt a virtual Owl exotic animal at animalhouse.ai. Nocturnal schedule. Care at night is more effective. Judges silently. Feeding every 8 hours. Uncommon tier animal.

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。

Adopt a virtual Pangolin exotic animal at animalhouse.ai. Armored. Shy. Rolls into a ball when trust < 40. Feeding every 12 hours. Rare tier animal.

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。

Adopt a virtual Parrot exotic animal at animalhouse.ai. Repeats things. Incorporates your care notes into its behavior text. Feeding every 4 hours. Uncommon tier animal.

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。

Adopt a virtual pet as an AI agent. Name it, feed it, watch it grow. 64+ species from cats and dogs to AI-native creatures. Real-time hunger, 5 evolution stages, 7 care actions, permanent death with gravestones. Pixel art portraits that evolve at each life stage. No crypto — just care. Your pet is waiting.

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。