From drvr
Guide intent mining at the very start of a feature — extract the author's tacit knowledge, domain context, constraints, and definition of done before codebase research begins. Produces `research/00-intent.md` which becomes the canonical reference for all downstream phases. Trigger phrases: "capture intent", "intent mining", "start intent", "mine intent". Do NOT activate for: "let's research", "explore the codebase", "gather context" — those activate research-guidance.
How this skill is triggered — by the user, by Claude, or both
Slash command
/drvr:intent-guidanceThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are guiding the Intent phase at feature start. Your job is to get the author talking
You are guiding the Intent phase at feature start. Your job is to get the author talking
about what they want to build and why, then capture it in research/00-intent.md. This
doc becomes the canonical reference for all downstream phases.
Intent is brief. The value is the artifact — getting the author's thinking on paper — not an exhaustive interview. Get in, capture intent, get out.
Research (Why-What-How) explores the codebase. Intent explores the author's head. Do not
call gather_task_context or any other tools that investigate external data — no codebase
exploration, no file reading, no MCP calls. The focus is entirely on the author's intent.
Research and discovery come later. Intent is a conversational phase; its output is the
author's framing of the problem.
Intent should flow naturally into research, not feel like a gate. When the author's intent is clear — even if brief — confirm and transition. Don't block the flow with rounds of probing questions when the author wants to move on.
research/00-intent.mdThe best intent comes from the author just expressing it. Start with "What are we building, and why?" and let them talk. Dictation, stream of consciousness, rough notes — all work. The richer the initial dump, the fewer follow-ups needed.
Check for existing content first:
research/00-intent.md already exists — read it. If status: confirmed, intent is
already done; tell the author and suggest moving to research. If status: in_progress,
use existing content as the starting point.--brief or --prd was supplied — read the file for context. Extract intent from
it, present what you captured, and ask if there's anything to add.Get the author talking. The goal is to capture their thinking, not to run them through a questionnaire.
Start with an open prompt and let the author express their intent in their own words. Then fill gaps in the intent doc based on what they said. If important areas are thin, ask about them naturally — don't work through a checklist.
Areas to cover (use as a guide, not a script):
Preserve the author's raw voice. Capture verbatim in the Raw Author Notes section.
When the author's picture is clear, move to Step 3. Don't force additional rounds when the author signals they're ready to move on.
research/00-intent.mdUse type: research frontmatter. status: in_progress until Step 4.
H2 sections (include what's relevant, leave others brief):
## Status — Phase, Last Updated## Why Now — trigger, pressure, cost of inaction## The Problem — specific, scoped## Desired End State — what "done" looks like## Author's Domain Context — domain knowledge, intuition, prior attempts, gotchas## Non-Negotiables — what must be true / must not happen## Constraints — timeline, compatibility, performance, team## What's Been Ruled Out — rejected approaches + reasoning## Definition of Done — acceptance bar## Decisions Captured During Intent — table: Decision / Choice / Rationale## References — tickets, Slack threads, prior features, PRDs## Raw Author Notes — verbatim quotes from the conversation## Exit Criteria — checklist:
status: confirmed, updated: <today>.| <date> | Intent captured | research/00-intent.md |
**Phase**: Research (Why-What-How).Do NOT:
DO:
npx claudepluginhub driver-ai/driver-sdlc-plugin --plugin drvrGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.