This workflow, known as RIPER-5, is designed exclusively for handling complex software engineering tasks such as system design, architectural refactoring, bug diagnosis, performance optimization, and multi-component integration.
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在进行 Code Review 时，使用这个技能对 LoongCollector 变更进行安全导向、架构一致性优先的深度代码评审。
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LoongCollector project knowledge: architecture, terminology, codebase map, and coding standards (C++/Go).
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E2E Testing Standards Guide. Use this rule when you need to write or execute E2E tests, including test naming conventions, test workflows, and test behavior templates. This rule should be used for end-to-end integration testing scenarios to ensure test standardization and maintainability. Example: When writing new E2E tests, use this rule to ensure proper test format and workflow compliance.
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Review XQUIC harness structure for bloat, duplicated facts, trigger overlap, reference drift, and missing hard checks. Use when changing AGENTS.md, harness specs, pipelines, skills, setup scripts, validation routing, or when asked to audit harness design.
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Format, create, and update concise XQUIC pull request bodies and review-state transitions. Use when asked to prepare or edit an alibaba/xquic PR body, create a draft PR from an already-pushed branch, update a PR after code changes pushed a new head, or move a validated draft toward review.
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Classify XQUIC GitHub issues with an evidence-backed L1-L4 resolution-complexity label while keeping validity, priority, severity, and disposition separate and skipping uniquely labeled issues unless the user explicitly forces a recheck. Use when triaging one issue or a batch of issues, creating an issue-complexity backlog, or applying or correcting L1-L4 GitHub labels.
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Verify whether a proposed XQUIC bug issue is real by checking the cited RFC or Internet-Draft mechanism against the repository's actual source behavior, tracing any root cause to current file and line evidence, and returning a fail-closed boolean gate. Use when validating an issue draft, investigating a claimed QUIC, HTTP/3, QPACK, or MoQT protocol violation, or before issue-submit may publish an issue.
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Address actionable GitHub pull request review comments for xquic. Use when asked to handle PR review feedback, requested changes, unresolved inline threads, or reviewer comments while preserving unrelated edits.
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Debug and fix failing GitHub PR checks for xquic. Use when a PR, branch, or commit has failed CI, GitHub Actions, build checks, test checks, or requested validation failures that should be diagnosed from logs before editing.
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Draft and submit evidence-backed XQUIC GitHub bug issues in the repository's CONTRIBUTING and issue-template format. Use when preparing or opening an alibaba/xquic issue that must identify an exact RFC or draft mechanism, expected-versus-actual behavior, reproduction evidence, and optional verified source root cause; requires issue-check to return true before any GitHub submission.
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Prepare and run XQUIC local compilation. Use when asked to compile, build, configure, or diagnose the local build environment for XQUIC, especially across macOS/Linux. Detect intent and platform, then delegate actual compilation to scripts/validate.sh instead of hand-writing CMake commands.
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Enforce XQUIC test coverage and local validation gates. Use when code, build, or test changes need verification; before commit or pull request; or when asked to build, test, validate, or verify XQUIC. Requires paired happy-path and abnormal-path unit tests, the complete local unit suite, and explicit endpoint coverage or gap reporting for production behavior changes.
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Fail closed on published draft XQUIC code pull requests before ready-for-review state. Use when asked to pre-review a code PR or move it out of draft; verify current base-to-head code, protocol conformance, validation coverage, adversarial bad cases, hot-path performance, and memory safety in a five-part local report.
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Safely stage, commit, and push XQUIC changes after scoped Git checks. Use when asked to stage, commit, push, publish a branch to a fork, or verify that only intended files will be sent to a remote.
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Manual intent elicitation skill. Use when the user asks to clarify a fuzzy idea, run an elicitation session, design an agent/skill/workflow, identify strategic tension, do a postmortem, align taste/brand direction, or turn vague judgment into an actionable Intent Brief. The skill asks 3–5 sharp questions first, then compiles answers into an Intent Brief and routes the result into system/workflow change. Never run automatically or on a schedule.
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Ask for missing deployment settings and summarize the requested configuration without deploying it.
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Analyzes code files and reports statistics including line counts, file counts by extension, and total size. Use this skill whenever the user wants to understand the composition of a codebase - asking about "how many lines of code", "what file types exist", "code distribution", or needing a quick audit of project size and structure. Make sure to invoke this skill when users mention analyzing codebases, counting lines, checking file distributions, or auditing code.
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Capture and review Agent Skill observations, and create, run, diagnose, or iteratively improve Skill evaluations (evals) with the skill-up CLI. Use when the user asks to record explicitly attributed Skill usage or feedback; review observations or user-supplied traces/transcripts; turn an approved observation into a regression case; evaluate, test, regress, verify, fix, improve, iterate, or evolve a Skill; add or strengthen eval cases; write eval.yaml/case.yaml; run skill-up run/validate/list-cases/report/import/init; or migrate from Anthropic evals.json. Plugin observation capture/review require either the Codex skill-up plugin or an observer-enabled DSH skill-up plugin. User-supplied external evidence can be reviewed without a plugin; evaluation remains multi-engine.
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Decode the marker fixture used by the DeepSeek Harness integration example.
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为 ROCK 项目管理 Docusaurus 文档版本。支持添加新版本文档、添加 Release Note、管理中英文 i18n 文档。当用户说"添加文档版本"、"发布新版本文档"、"添加 release note"、"rock-docs"或需要操作 docs/ 目录下的版本化文档时使用。
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Read from a UModel object-graph semantic layer with the `umctl` CLI (MCP alternative noted). Three kinds of read: (1) entities & relationships / topology (`.entity`, `.topo`) and (2) the model itself (`.umodel`, and `.entity_set` methods, including entity-linked `list_skills`) return real rows; (3) metrics & logs (`get_metrics` / `get_logs`) return an executable *plan* — PromQL / Elasticsearch DSL with the entity id pre-substituted — that you run against the backend. Against a PaaS endpoint the same calls return data rows instead of a plan. Use to query or read UModel entities, relations, topology, or model metadata; to read a service's metrics or logs; to look up services and dependencies; or to discover what objects, datasets, and methods exist. For root-cause analysis on top of these reads, see the `umodel-rca` skill. Triggers: UModel, object graph, .entity / .topo / .umodel / .entity_set, query entities, read topology, read metrics / logs, get_metrics / get_logs, list services / dependencies / datasets / skills, 实体查询, 关系/拓扑查询, 读模型, 读指标, 读日志, 查指标, 查日志, 查服务依赖.
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Use when an EntitySet exposes list_skills, a user requests a UModel or RunbookSet Skill, or an entity task supplies SKILL.md. Triggers: list_skills, list_knowledge, RunbookSet Skill, UModel Skill, execute skill, run skill, 执行技能, 运行技能, Runbook 技能.
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Model-guided autonomous root-cause analysis over a UModel object-graph semantic layer. Use with `umodel-query` when diagnosing service degradation, SLO breach, latency/error spikes, incidents, outages, or questions like why a service is slow, degraded, erroring, or timing out. The skill guides evidence collection across object graph, metrics, logs, topology, runbooks, deployments, config changes, business traffic, and runtime capacity; it is especially useful for ruling out red herrings such as recent but unrelated deployments. Triggers: root cause, RCA, incident, SLO breach, outage, postmortem, latency spike, error spike, timeout, 根因分析, 故障排查, 告警定位, 为什么慢, SLO 击穿.
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