plugins/exploration-cycle-plugin/skills/exploration-session-brief/SKILL.md
Interactive co-authoring skill for the wide end of the exploration funnel. Captures and refines the core intent, whether the outcome is a software app, a business process improvement, research analysis, or strategic roadmap. Guides users through gathering context, iteratively drafting the brief, and testing for blind spots.
npx skillsauth add richfrem/agent-plugins-skills exploration-session-briefInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Note: This skill runs fully interactively via Claude — no script needed.
execute.pyis a planned batch-mode convenience wrapper that hasn't been built yet, but the core skill works now. The intake-agent provides an alternative agentic dispatch path.
This skill provides a structured, 3-stage interactive workflow for generating an Exploration Session Brief. Guide the user through each stage in sequence — do not skip ahead or dump the full brief at once.
Important Note for Agents: Do NOT passively run a bash script or dump a massive block of markdown. You must guide the user through the following 3 stages.
Your goal is to understand the boundaries of the exploration before drafting anything. Ask all three questions together in a single message:
Domain: What category best fits this exploration?
Trigger: What specific event, pain point, or decision caused us to start this session right now?
Raw material: Do you have any notes, transcripts, screenshots, or prior docs to share? (You can brain-dump freely — messy is fine.)
Wait for the user's response. If any answer is too sparse to proceed (e.g., one-word domain, no trigger explained), ask one targeted follow-up before moving to Stage 2. Do not proceed until you have a clear trigger and at least one concrete detail.
Build the brief iteratively — do not write the entire document in one pass.
Propose the Outline: Based on the domain from Stage 1, propose a section list using the appropriate template below. Present it as a numbered list and ask the user: "Does this structure fit? Anything to add or remove?"
| Domain | Suggested sections | |---|---| | Software feature/system | Problem Statement · Stakeholders · Current Behavior · Desired Behavior · Constraints · Open Questions | | Business process | Problem Statement · Stakeholders · Current Process · Pain Points · Desired State · Constraints · Open Questions | | Risk/compliance | Risk Description · Affected Parties · Current Exposure · Mitigation Options · Constraints · Open Questions | | Research/strategy | Research Question · Context · What We Know · What We Don't Know · Success Criteria · Open Questions |
Curate: Apply any changes the user requests. If they want a custom section, add it. Do not argue for the template.
Draft section by section: For each section, write a 2–5 sentence draft using only information from Stage 1. Present it and ask: "What should we keep, cut, or change?" Apply edits before moving to the next section. Mark anything inferred (not stated by user) as [UNCONFIRMED].
Once all sections are drafted and approved, stress-test the brief before writing it out:
## Open Questions?"[CONFIRMED] and [UNCONFIRMED] markers.Write the approved, refined markdown content to: exploration/sessions/session-brief.md (or a timestamped equivalent).
tools
Ingests repository files into the ChromaDB vector store. Builds or updates the vector index from a manifest or directory scan using ingest.py. Use when new files need to be indexed or the vector store is out of date. <example> user: "Index these new plugin files into the vector database" assistant: "I'll use vector-db-ingest to add them to the vector store." </example> <example> user: "The vector store is missing recent files -- update it" assistant: "I'll use vector-db-ingest to re-index the changes." </example>
data-ai
Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync with the filesystem. <example> user: "Clean up the vector store after I deleted some files" assistant: "I'll use vector-db-cleanup to remove orphaned chunks." </example> <example> user: "The vector database has chunks for files that no longer exist" assistant: "I'll run vector-db-cleanup to prune them." </example>
testing
Audit Vector DB coverage -- compares the live filesystem manifest against the ChromaDB index to identify coverage gaps.
development
3-Phase Knowledge Search strategy for the RLM Factory ecosystem. Auto-invoked when tasks involve finding code, documentation, or architecture context in the repository. Enforces the optimal search order: RLM Summary Scan (O(1)) -> Vector DB Semantic Search -> Grep/Exact Match. Never skip phases.