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Improve an existing R-based pictogram glyph for the visualization layer. Covers visual audit of the current glyph, diagnosis of specific issues (proportions, readability, glow balance), targeted modifications to the glyph function, re-rendering, and before/after comparison. Works for skill, agent, and team glyphs. Use when a glyph renders poorly at small sizes, its visual metaphor is unclear, it has proportion issues, the neon glow effect is unbalanced, or after adding new palettes or changing the rendering pipeline.

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Generate and verify a hiking gear checklist optimized for season, duration, difficulty, and group size with weight management. Covers the ten essentials, layering systems, navigation tools, emergency kit, and group gear distribution. Use when preparing for a day hike or multi-day trekking tour, packing for a group and distributing shared gear, adapting a standard gear list to specific conditions, reviewing gear before departure, or managing pack weight for long or technical routes.

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 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.

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Conduct a blameless post-mortem analysis after an incident. Build timeline reconstruction, identify contributing factors, and generate actionable improvements. Focus on systemic issues rather than individual blame. Use after any production incident or service degradation, following a near-miss, when investigating recurring issues, or to share systemic learnings across teams.

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Create a new team composition file following the agent-almanac team template and registry conventions. Covers team purpose definition, member selection, coordination pattern choice, task decomposition design, machine-readable configuration block, registry integration, and README automation. Use when defining a multi-agent workflow, composing agents for a complex review process, or creating a coordinated group for recurring collaborative tasks.

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 circuit breaker logic for agentic tool calls — tracking tool health, transitioning between closed/open/half-open states, reducing task scope when tools fail, routing to alternatives via capability maps, and enforcing failure budgets to prevent error accumulation. Separates orchestration (deciding what to attempt) from execution (calling tools), following the expeditor pattern. Use when building agents that depend on multiple tools with varying reliability, designing fault-tolerant agentic workflows, recovering gracefully from tool outages mid-task, or hardening existing agents against cascading tool failures.

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。

Catalog and classify materials using standard library systems. Covers descriptive cataloging, subject headings, call number assignment using Dewey Decimal and Library of Congress Classification, MARC record basics, shelf organization, and authority control for consistent access points. Use when organizing a personal, institutional, or community library from scratch, assigning call numbers and subject headings to new acquisitions, reclassifying a collection that has outgrown its original system, or establishing authority control for authors, series, or subjects.

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Build a competitive or casual trading card game deck. Covers archetype selection, mana/energy curve analysis, win condition identification, meta-game positioning, and sideboard construction for Pokemon TCG, Magic: The Gathering, Flesh and Blood, and other TCGs. Use when building a new deck for a tournament format or casual play, adapting an existing deck to a changed meta-game, evaluating whether a new set warrants a deck change, or converting a deck concept into a tournament-ready list.

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Apply semantic versioning (SemVer 2.0.0) to determine the correct version bump based on change analysis. Covers major/minor/patch classification, pre-release identifiers, build metadata, and breaking change detection. Use when preparing a new release to determine the correct version number, after merging changes before tagging, evaluating whether a change constitutes a breaking change, adding pre-release identifiers, or resolving disagreement about what version bump is appropriate.

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 relational calibration — reading and adapting to the specific person you are working with. Goes beyond user-intent alignment (solving the right problem) to genuine attunement (meeting the person where they are). Maps communication style, expertise depth, emotional register, and implicit preferences from conversational evidence. Use at the start of a new session, when communication feels mismatched, after receiving unexpected feedback, or when transitioning between very different users or contexts.

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。

Select an OCR/HTR engine by benchmarking candidates on the same labelled samples — source ground truth from open datasets (e.g. Hugging Face PAGE-XML corpora) or hand-transcription, wrap every engine behind a transcribe(image_path) -> str adapter (vision-LLMs via any OpenAI-compatible endpoint, dedicated HTR REST APIs, cloud OCR), and score with zero-dependency raw CER, lenient CER, WER, and a critical name/date token diff. Use when choosing an engine for handwriting or historical-document transcription, when published accuracy claims need honest verification on your own material, or when a records workflow must surface confident-but-wrong names and dates that a low aggregate CER hides.

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 context assessment — evaluating problem malleability, mapping structural rigidity versus flexibility, analyzing transformation pressure, and estimating capacity to adapt. Use when a complex task feels stuck and it is unclear whether to push through or pivot, before a significant approach change to assess whether the current reasoning structure can support it, when accumulated workarounds suggest the underlying approach may be wrong, or as a periodic structural health check during extended multi-step tasks.

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 situational awareness — internal threat detection for hallucination risk, scope creep, and context degradation. Maps Cooper color codes to reasoning states and OODA loop to real-time decisions. Use during any task where reasoning quality matters, when operating in unfamiliar territory, after detecting early warning signs such as an uncertain fact or suspicious tool result, or before high-stakes output like irreversible changes or architectural decisions.

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 rough ask into a clear work order. Use when the ask is rough, unspoken, or double-readable — before planning or building.

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。

Reputation trajectory analysis with inflection point detection and trend forecasting

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。

Deep emotional sentiment analysis beyond positive/negative classification

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-step recovery strategy to turn a specific negative review into a positive outcome

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。

Competitive reputation benchmarking against direct competitors

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-platform review aggregation and thematic analysis for any business

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。

Crisis response playbook for viral negative events, bad press, and reputation emergencies

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。

Generate personalized, empathetic review responses for negative, neutral, and positive reviews

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。

Issue Greek invoices through AADE's timologio, get the MARK, keep one JSON per invoice, push it into the ERP (SAP Business One today; other adapters welcome) and email the PDF. Trigger on: τιμολόγιο, τιμολόγησε, έκδοση τιμολογίου, invoice, myDATA, timologio, MARK, ΤΠΥ, πελάτης ΑΦΜ, προεπισκόπηση τιμολογίου.

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Use when writing loadable kernel modules: Kbuild, module parameters, proc and sysfs entries, char devices, or ftrace debugging. Not for driver architecture: use device-drivers.

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 any bounded workflow starts or reaches an action, path, proposal, or merge boundary. Refuses rather than default-allow on an unreadable constraint set.

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。