「vin」の検索結果

Implements and validates the fix for a bug once a failing reproduction test exists. TRIGGER when: a bug has a failing reproduction test and you are ready to make it pass, implementing the root-cause fix, the final step of the bug-fixing pipeline. DO NOT TRIGGER when: no reproduction test exists yet → opsmill-dev-test-driving-bugs; still diagnosing, or asked to fix a bug with no analysis or reproduction test yet → opsmill-dev-analyzing-bugs.

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。

Performs root-cause analysis of a bug — from a GitHub issue, an issue URL, or a free-text description — before any reproduction or fix is written. TRIGGER when: triaging or diagnosing a bug, investigating why something misbehaves, an issue number/URL handed over for analysis, needing the root cause before touching code. DO NOT TRIGGER when: a failing reproduction test already exists and you are ready to fix → opsmill-dev-fixing-bugs; writing that reproduction test → opsmill-dev-test-driving-bugs; capturing a new bug as a ticket → opsmill-dev-creating-issues.

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Mines a pull request's review threads for lessons that generalize beyond that PR, reconstructs each against the actual code before deciding, checks whether it is already codified, and routes the genuinely-new, durable ones into Infrahub's internal documentation — `dev/knowledge/`, `dev/guides/`, `dev/guidelines/`, the `AGENTS.md` files, and `.agents/rules/` — proposing edits first and applying only with the user's approval. TRIGGER when: the user wants to turn PR review feedback into durable conventions, guidelines, or rules; capture recurring reviewer comments as internal documentation; or check whether review lessons are reflected in the knowledge/guides/guidelines/AGENTS.md/rules layer. DO NOT TRIGGER when: sweeping a feature's changes for documentation coverage across all layers → use `audit-docs`; extracting knowledge from completed spec directories → `speckit-opsmill-extract`; only replying to or resolving review threads → normal git/gh flow.

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。

Writes a single failing test that reproduces a bug after its root-cause analysis is complete, before any fix is written. TRIGGER when: a bug has a completed root-cause analysis and you need the failing reproduction test, writing a test that proves a bug exists, the second step of the bug-fixing pipeline. DO NOT TRIGGER when: still triaging or diagnosing the bug → opsmill-dev-analyzing-bugs; implementing the fix once the test exists → opsmill-dev-fixing-bugs; general feature test-first work → superpowers test-driven-development.

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。

Show full session token usage, costs, TLDR savings, and hook activity

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。

Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG). 95% token savings.

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。

Metacognitive check-ins during problem solving - detects when to pivot or persist

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。

Problem-solving with Rudin's Real and Complex Analysis textbook

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Problem-solving strategies for fields in abstract algebra

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Lossless session context compression for token efficiency. Extracts entities, decisions, and state into compact format before context window fills. 10-30x reduction in context size while preserving all actionable information.

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。

Formal theorem proving with research, testing, and verification phases

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Self-evolving skill system. Skills are scored after execution (0-100) on 5 dimensions. Score 90+ over 5 runs = crystallized (locked). Score below 30 = auto-repair attempted. Skills improve themselves through usage feedback.

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。

Unified math capabilities - computation, solving, and explanation. I route to the right tool.

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-layer cognitive stack for machine-verified mathematical problem solving across 14 sub-disciplines.

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 context compression is imminent, when resuming a session, or when preserving critical decisions across long tasks. Provides a durable notepad that survives compaction and lets any agent pick up exactly where the previous context left off.

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。

Knowledge graph integration for token-efficient codebase understanding. Uses codebase-memory MCP for AST indexing, dependency graphs, and smart context selection. 6-71x token savings vs raw file reading.

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Progressive memory recall with 4 scope layers AND 3 depth layers. Scope: identity > project > room > deep. Depth: IDs only > summary > full. 10-50x token savings through fetch-on-confirmation pattern.

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。

Systematic hook debugging workflow. Use when hooks aren't firing, producing wrong output, or behaving unexpectedly.

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。

Post-implementation cleanup that removes AI-generated bloat while preserving functionality. Runs pass-by-pass with test verification after each pass. Activate after kraken/spark complete a feature, or when a codebase needs hygiene 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。

AI art direction system. Claude directs image generation models (Gemini, DALL-E, Flux) via structured prompts. Generate banners, diagrams, logos, screenshots, and social media visuals without leaving the terminal.

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Optimize SQL query performance through EXPLAIN analysis, indexing strategies, and query rewriting for PostgreSQL, MySQL, and SQL Server. Use when debugging slow queries, analyzing execution plans, or improving database performance.

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Design and implement Internal Developer Platforms (IDPs) with self-service capabilities, golden paths, and developer experience optimization. Covers platform strategy, IDP architecture (Backstage, Port), infrastructure orchestration (Crossplane), GitOps (Argo CD), and adoption patterns. Use when building developer platforms, improving DevEx, or establishing platform teams.

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Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.

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。

Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).

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。