Use this skill when bootstrapping scheduled knowledge-base sync for a repo that has no knowledge/.knowledge-sync.yml yet — to run one-time setup that detects the knowledge_root from CLAUDE.md/AGENTS.md, maps doc areas to source globs, records opt-in external sources (Linear/Notion/WebFetch, all disabled by default), captures a baseline last_scanned_sha, sets the per-area update policy, generates or seeds knowledge/CONVENTIONS.md, provisions the L4 memory dir, and offers to register the daily routine. Routes ongoing recurring sync operations to /knowledge-sync.
Use this skill when running the recurring (daily) knowledge-base rescan for a repo that already has knowledge/.knowledge-sync.yml — the main-thread dispatcher that reads the config, computes the git delta since last_scanned_sha, maps changed paths to affected doc areas, early-exits cheaply when nothing changed, then fans out one Agent(content-writer) per affected area, applies the propose/direct update policy, advances the baseline only on success, and writes an L4 run log — all with the G1 untrusted-content choke-point, secret-scan, deny-list, and budget controls woven in. For first-time setup use /knowledge-sync-init.
Use this skill when bootstrapping memory in a freshly cloned repo or when upgrading from a pre-memory plugin version — to initialize the .ai-skills-memory/ skeleton in the current project (directory structure, .gitignore rules, learnings.md template, .committed/ allowlist). Idempotent — safe to re-run on a project that already has memory wired.
Use this skill when reviewing how the plugin behaved across recent runs, after a release to confirm reliability, before filing a plugin bug report, or when planning the next plugin improvement cycle — to collect and analyze past Claude Code session logs for the ai-skills plugin and surface agent, subagent, skill, command, and hook errors, timeouts, unexpected exits, and other anomalies that point at plugin defects. Defaults to the last 7 days of sessions for the current project. Produces an extended Markdown report on disk plus a brief on-screen summary.
Use this skill when bootstrapping a target repository to be ai-skills-aware — on the first run of any ai-skills workflow in a fresh repo, when adopting the ai-skills plugin in an existing repo, or after upgrading to a plugin version that adds new memory paths or templates, including when the user does not say "init" but asks to "set up" or "onboard" the repo — to detect codebase type, create CLAUDE.md + AGENTS.md scaffolding, initialize the .ai-skills-memory/ directory tree from L1 templates, and configure .gitignore. Idempotent — safe to re-run. Accepts `--codebase-type <type>` and `--overwrite`. Not for re-initializing only memory — use `/memory-init` instead.
Use this skill when a local container won't start, a service is unreachable from the host, a local docker-compose stack is misbehaving, or as the Docker-layer diagnosis step of a local bugfix flow — including when the user describes the symptom without naming Docker — to run a Docker-specific local diagnostic that collects container status, logs, networking, and resource usage and diagnoses issues, applying the SRE or DevOps role for investigation. For multi-scope environment analysis (Docker + Kubernetes + CI runner + drift snapshot + optional auto-fix) use `/env-analyze` instead.
Use this skill when investigating a production incident, when an alert fires (latency spike, error rate, pod crashloop), when a customer-reported issue needs prod telemetry, or as the diagnosis step of an incident-response or production-bugfix flow — including when the user describes a prod symptom without asking to "analyze" — to analyze the production environment by collecting Kubernetes pod status, managed database health, logs, metrics, and networking and diagnosing issues, supporting GCP, Azure, and AWS via the `cloud-platforms` skill and applying the SRE or DevOps role.
Use this skill when the user asks to analyze, investigate, assess, evaluate, compare, or research a codebase, architecture, system, product, market, or research topic in a structured way — including requests that need explicit scope, framework-driven reasoning, and separated facts/inference/recommendation but do not use the word "analyze" — to run a deep analysis workflow producing traceable scope, evidence, and conclusions. Not for quick one-off lookups or implementation work — this is an investigation-and-conclusions workflow.