Map the repository and recommend the next CodeConductor slash command. Read-only.
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See how to installReview the instructions the agent will receive before you install.
Invoke as $cc-explore. The user request follows the skill mention.
Explore request: $ARGUMENTS
Command: explore (fixed for this workflow — do not infer from user text)
npx cc-codeconductor ccep profile explore --output json to get the phases and their roles.npx cc-codeconductor ccep compile --command explore --phase <phase-id> "$ARGUMENTS" --view prompt --output jsonnpx cc-codeconductor ccep evaluate --command explore --input <planner.json> --output json. If stop is true, show questions or risks and wait for human input.prompt for its phase — never forward raw $ARGUMENTS to planners.If graphify-out/graph.json exists, run graphify query "$ARGUMENTS". Invoke repo-explorer. Produce a Repo Map. Do not write code.
Recommend exactly one next slash command ($cc-feature, $cc-fix, $cc-refactor, $cc-review, $cc-backlog, $cc-openspec, $cc-triage, …) with a one-sentence rationale. Do not start that workflow.
Report the Repo Map and the recommended next $cc- command.
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Use when adding monitoring, metrics, logging, or tracing to Spring AI or LLM integration code. Covers token tracking, latency measurement, cost estimation, and prompt/response logging. Use when user mentions AI monitoring, token costs, or LLM observability.
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