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Get guidance on using APM, a package manager for AI agents, to install and share reusable instructions, skills, and server configurations. Covers dependencies, package creation, authentication, policy checks, CLI commands, and troubleshooting.
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Review OpenAPM specification changes through four specialist perspectives covering schemas, registries, dependency resolution, and web architecture. The skill combines findings and mechanical checks into one advisory comment, separating immediate fixes from work deferred to later versions.
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Improve APM CLI commands, help text, setup flows, and error messages. The skill checks consistency with familiar developer tools and guides wording so users understand failures and know what action to take next.
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Guide Python module additions and refactoring in APM CLI. Uses existing architecture patterns to organize shared logic and decision ownership, with regression tests and static checks to protect changes that centralize behavior.
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Investigates distributed application performance using PostHog APM (OpenTelemetry span) data via MCP. Use when the user asks about service traces, slow HTTP/database spans, error spans, trace IDs, or span attributes — not AI observability traces or product logs. Uses posthog:query-apm-spans, posthog:apm-trace-get, posthog:apm-services-list, posthog:apm-attributes-list, and posthog:apm-attribute-values-list.
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Improve APM command-line messages so users can scan results and understand what to do next. It guides warning summaries, consistent colors and symbols, and shared logging code for progress, errors, and detailed output.
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Guide authentication changes in APM CLI for GitHub and Azure DevOps. Centralizes credential lookup through AuthResolver and explains token precedence, Azure CLI fallback, Git credential helper retries, and error messages for failed authentication.
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Review dependency handling, package downloads, credentials, and deployed file cleanup in APM. The skill defines checks for safe paths, centralized credential access, and refusing content when its integrity or signature cannot be verified.
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Review APM positioning, public documentation, release communication, and breaking-change decisions using GitHub evidence. Check that incompatible changes include changelog entries and migration notes, and resolve disagreements among the project's specialist reviewers.
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Build and troubleshoot C#/.NET MCP servers that expose tools and information to AI agents. Guidance targets the official 2.x SDK and covers transport setup, authentication, interactive features, basic clients, and migration issues caused by changed defaults.
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Review an APM pull request through specialist agent perspectives selected for the changed areas. Findings are combined into one advisory comment with follow-up priorities and a shipping recommendation, leaving the final decision to maintainers and authors.
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Prepare an APM release by assessing changes since the last tag, choosing a version increment, cleaning the changelog, updating version files, and running CI-equivalent checks before opening a release pull request.
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Post or update one GitHub comment for an APM autopilot run. The skill assembles public or debug content, adds an AI-generated disclaimer, and supports previewing the assembled comment before posting.
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Check specific documentation pages against source code, one factual claim at a time. Receive verdicts showing supported, partly supported, contradicted, or unsupported statements, with code file and line references and suggested corrections.
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Classify how a pull request affects documentation using its changes and a documentation index. Return a JSON verdict identifying whether updates are unnecessary, already covered, needed within existing pages, or require structural changes.
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Select open pull requests for initial classification and distribute them to dedicated workers with a concurrency limit. Record why each request is included or excluded, without merging changes, assigning owners, or requesting reviewers.
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Draft a pull request description that explains the problem, implementation, trade-offs, and testing. It combines source-backed reasoning, validated Mermaid diagrams, and actual validation output into one Markdown file reviewers can assess on its own.
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Assess how a pull request affects documentation, classify the required updates, and coordinate writing, technical verification, and editorial review. The result is an advisory comment with proposed changes for affected pages or document structure.
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Audit an entire documentation collection against current source code and command output. Check pages for outdated claims, obsolete options, and broken links, apply small corrections, and verify the edits before preparing changes for maintainer review.
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Configure GKE logging, metrics collection, dashboards, and alerts with Cloud Logging, Cloud Monitoring, and managed Prometheus. Diagnose missing measurements, collection failures, permission errors, and alert rule problems to identify appropriate configuration changes.
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Assess one selected GitHub issue from user needs, security, and relevant design or documentation perspectives. Produce an advisory recommendation, a proposed scope brief, and a suggested reply for a human maintainer to consider.
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Turn a list of documentation pages affected by a code change into a precise editing plan. Identify sections and line ranges, record claims to verify, and flag information that must remain consistent across pages.
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Plan documentation structure changes for a pull request, a proposed code change. Produce page outlines and a precise list of additions, moves, and retirements while checking reader learning paths, links, and redirects.
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Assess one selected pull request using its full discussion, reviews, and linked issues. Produce an advisory recommendation about its next step, with optional comments and labels, without reviewing the code diff or approving a merge.
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