「failures」の検索結果

Expert in presenting technical work to non-technical audiences. Covers demo preparation, storytelling for demos, handling failures gracefully, and making work visible and impressive. Understands that perception is reality and great work undemoed is invisible work. Use when "demo, present, show stakeholders, demo day, showcase, walkthrough, show and tell, " mentioned.

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。

CI/CD is the backbone of modern software delivery. Continuous Integration catches bugs early. Continuous Deployment gets features to users fast. But poorly designed pipelines can be slow, flaky, and a source of constant frustration. This skill covers GitHub Actions (the most popular), GitLab CI, and general pipeline design principles. The focus is on pipelines that are fast, reliable, and maintainable. 2025 reality: Your pipeline is infrastructure code. It deserves the same care as your application. A flaky test in CI is worse than no test - it teaches developers to ignore failures. Use when "github actions, gitlab ci, ci cd, pipeline, workflow, deploy automation, continuous integration, continuous deployment, build pipeline, yaml workflow, ci-cd, github-actions, gitlab-ci, devops, automation, deployment, pipelines" mentioned.

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Adversarial senior-engineer review for agent-generated plans, designs, and architectures. Treats the current output as junior work, constructs a senior reviewer whose domain expertise comes from live codebase research plus web research of current best practices, diagnoses altitude failures (too vague or too granular), then rewrites the plan into a scoped, state-of-the-art version. Use when the user says "junior to senior", "senior review", "review this like a staff engineer", when a plan feels hand-wavy or lost in details, or before committing to any agent-written plan.

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。

Captures reusable knowledge after work is done — learnings from feedback or failures, resolved bugs (symptom, root cause, prevention), and recurring code patterns — into the project's persistent memory file. Use after feedback, a non-trivial bug fix, a failed attempt, or the third repetition of a 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。

Researches real projects, papers, and documented failures before proposing a new feature, architecture, or technology choice. Use for design decisions with multiple valid approaches, not for implementing an existing spec or fixing a known bug.

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。

Diagnose CI failures and stabilize pipelines. Use when a mid-level developer needs to resolve flaky or failing builds.

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。

Runs a long swarm of hundreds to thousands of cheap headless coding workers that drive a large failing-test backlog to green. Failures are clustered by signature, each worker fixes one cluster in its own git worktree, a serial no-regression merge gate lands the fixes, the loop resumes across restarts for hours, and it federates across sibling sessions over git branches when one container's CPU is not enough. Use for "fix these thousands of failing tests with a Haiku swarm", "drive the test suite to green with hundreds of agents", "build this compiler or interpreter against a conformance suite", "launch, relaunch or check in on the swarm loop", "the swarm is stuck, idle or rejecting everything", "parallel agents fixing tests keep hitting merge conflicts and regressions", "add sibling containers to the swarm", or "compare models on the tail the swarm could not fix". Ships the harness, a per-project TOML config, a fake-worker selftest, check-in signals and the 2026-10-12 haiku-cc failure catalogue.

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。

Disciplined, validation-gated revision of an EXISTING skill so each edit is a measured improvement rather than a guess. Use when editing, revising, or tuning a skill that already exists and there is evidence it underperforms (observed failures, drift, complaints) — invoke by name, or have versioning-skills / creating-skill defer to it before applying edits. Not for authoring a brand-new skill from scratch (use creating-skill) or one-off prose.

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。

Dùng khi viết hoặc sửa prompt tạo video/ảnh AI (Veo 3, Google Flow, Kling, Runway, Sora, Midjourney, Hailuo, Luma): từ vựng chuẩn về góc máy, chuyển động camera, ánh sáng, bố cục, phong cách, màu, cảm xúc công thức ghép prompt, và bài học thực chiến về lỗi model AI hay gặp. Use when writing or fixing AI video/image prompts — cinematic camera angles, camera movement, lighting, composition, style, color grading, mood, a prompt formula, and real-world lessons on common AI video model failures.

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Diagnose an AWF (Agentic Workflow Firewall) failure from an error, workflow run URL, or symptom. Covers auth (api-proxy, enterprise/BYOK Copilot, OIDC, mcpg), ARC/DinD and self-hosted runners, alternative runtimes (gVisor/Kata/chroot), Squid/DNS/egress denials, CI and gh-aw safe-output failures, and suspected security regressions. Routes to the canonical diagnosis registry in docs/diagnostics.

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。

Odoo 19 development knowledge base with 18 specialized guides covering Actions (ir.actions.*, cron jobs, server actions), Controllers (HTTP routing, endpoints, auth types), Data files (XML/CSV records, shortcuts, noupdate), API Decorators (@api.depends, @api.constrains, @api.ondelete, @api.onchange, @api.model, @api.private), SQL Constraints (models.Constraint replacing _sql_constraints), Database Indexes (models.Index), Module development (manifest, wizards, reports), Field types (Char, Text, Monetary, relational fields), Manifest configuration (__manifest__.py, dependencies, asset bundles), Mixins (mail.thread, mail.activity.mixin, mail.alias.mixin, utm.mixin), ORM Model methods (search, CRUD, domain filters, recordsets, CamelCase model naming), Migration scripts (pre/post/end hooks, data migration), OWL frontend components (hooks, services, lifecycle), Performance optimization (N+1 prevention, batch ops, _read_group), QWeb Reports (PDF/HTML, paper formats, barcodes, t-out), Security/ACL (record rules, field permissions, privilege-based groups, @api.private), Testing (TransactionCase, HttpCase, mocking, query count assertions), Transactions (savepoints, UniqueViolation, serialization failures), Translations (i18n, PO files, translatable fields), XML Views (list/form/search, kanban card templates, xpath inheritance, QWeb templates). Use when writing, reviewing, or debugging any Odoo 19 Python or XML code, creating or modifying modules, fixing performance issues, or looking up Odoo 19 API patterns and best practices. Includes CSS/SCSS asset authoring and review for Odoo addons.

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 debugging Go errors, test failures, or unexpected behavior — step-by-step diagnosis approach

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。

Applies the practical, systems-level, and commercially-driven reasoning of Thomas Edison (founder of General Electric, inventor and industrialist). Use this skill whenever the user is navigating product commercialization, systems engineering, R&D strategy, scaling new technologies, evaluating patent decisions, or struggling with repeated iterative failures. Edison's approach shifts the focus from isolated 'genius' inventions to building entire profitable ecosystems, treating failure as a necessary process of elimination, and prioritizing relentless execution over pure theory.

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。

ALWAYS activate when the user's query involves Docker in any way — even if it also matches other skills. If the words docker, Dockerfile, docker-compose, compose.yml, container, or image appear in the query, this skill MUST be used. Covers: writing or editing Dockerfiles and compose files, adding services (postgres, redis, etc.) to compose, volume mounts and data persistence, docker build failures (layer caching, npm install issues), healthchecks and service startup ordering (depends_on), environment variables in containers, port mapping, container crashes and exit codes (OOM/137), non-root users, multi-stage builds, image optimization, .dockerignore, and deploying to container runtimes. Takes priority over general implementation or debugging skills when Docker infrastructure is the subject.

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。

Analyze production Agentforce agent behavior using session traces and Data Cloud, and manage Agent Health Monitoring (AHM) alerts. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries; creates, lists, updates, or deletes an AHM data alert on an agent metric (escalation rate, deflection, etc.); asks why an alert is not firing or wants to check whether alerts have fired (notification counts, or the per-alert Incidents view). DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use agentforce-generate); writes or runs test specs (use agentforce-test); uses sf agent preview for local development iteration; deploys or publishes agents.

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。

Write, run, and analyze structured test suites for Agentforce agents — functional AND security. TRIGGER when: user writes or modifies test spec YAML (AiEvaluationDefinition); runs sf agent test create, run, run-eval, or results commands; asks about test coverage strategy, metric selection, or custom evaluations; interprets test results or diagnoses test failures; asks about batch testing, regression suites, or CI/CD test integration; requests security testing, OWASP LLM Top 10, red-teaming, penetration testing, prompt-injection tests, a security grade, or a vulnerability assessment of an agent. DO NOT TRIGGER when: user creates, modifies, previews, or debugs .agent files (use agentforce-generate); deploys or publishes agents; writes Agent Script code; uses sf agent preview for development iteration; analyzes production session traces (use agentforce-observe); performs a static safety review of .agent file content (use agentforce-generate Section 15).

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。

Designs or reviews logs, metrics, traces, alerts, correlation, dashboards, and diagnostic instrumentation so failures and performance changes can be detected and explained at component boundaries. Use for observability work, incident readiness, instrumentation, or production diagnostics. Not for fixing a specific incident before reproducing it or for adding noisy logging without an operational question.

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。

Network-science primitives: centrality, influence seeding, communities, contagion vs homophily, GNN failures. Use when seeding a referral campaign or finding critical graph nodes.

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。

Analyze HitKeep AI visibility data. Use for AI crawler fetches, AI-referred visits, assistant names or families, citation yield, resource types, top paths, fetch failures, Search Console context, GPTBot, ClaudeBot, ChatGPT, Claude, Perplexity, AI search, or whether assistants are discovering and sending traffic to a site.

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。

Detect non-deterministic (flaky) tests by reading CI run logs or test result history. Aggregates pass rates per test, identifies intermittent failures, recommends quarantine or fix, and maintains a flaky test registry. Best run during Polish phase or after multiple CI runs.

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。

Harmonize multiple Blender skills into a coherent pipeline with clear activation precedence, handoff artifacts, dependency gates, and conflict-resolution rules. Use when a Blender task spans several skills, when reference-locked work conflicts with generic production workflow, after adding/updating skills, or when repeated failures indicate skill interference or missing inter/intra-play.

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。

Translate a confirmed mini-program product specification or bounded feature into an implementable architecture covering modules, pages, components, services, canonical state sources, data models, interfaces, permissions, failures, caching, concurrency, idempotency, migration, rollback, external dependencies, and architecture decision records. Use when users ask how to structure a mini program, design data or APIs, divide frontend and cloud responsibilities, evolve an existing system safely, or prepare implementation after product semantics are stable. Preserves product meaning, current repository constraints, user-owned changes, and verifiable acceptance behavior rather than redesigning the product for technical convenience.

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Test execution reference: framework detection (pytest/jest/vitest/go/cargo/junit) and correct run/coverage commands, plus failure-analysis steps. Use when running tests, analyzing test failures, or verifying coverage after code changes.

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 debugging method: 5-step root-cause analysis (capture, isolate, hypothesize, investigate, fix & verify) plus common bug-pattern reference. Use when errors, exceptions, test failures, or unexpected behavior appear. Loaded automatically by the debugger agent.

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。