/automate-me

name: automate-me
description: "Use for \"automate me\", \"create/update/refresh my -mode skill\", \"turn/capture my preferences or working style into a skill\", or wanting agents to follow how the user works. Drafts or revises a personal -mode skill via create-skill + unslop, optionally pulling fresh evidence from recent transcripts."
disable-model-invocation: true

Automate me

A guided flow for turning the user's working conventions into a skill agents will follow. The output is one `-mode` skill tailored to them (e.g. `jay-mode`, `priya-mode`).

This skill orchestrates three others: an inline mining pass (see step 1), Cursor's built-in `create-skill` (authoring), and the **unslop** skill (prose discipline). It sequences them; it doesn't replace them.

Flow

0. Check for an existing skill

Look recursively for `.cursor/skills/**/*-mode/SKILL.md` and `~/.cursor/skills/*-mode/SKILL.md` matching the user's handle. Mode skills can live in a personal category directory (`.cursor/skills/<handle>/`), not only at the top level. If one exists, confirm intent with `AskQuestion` (unless they already said "update my skill" or similar):

• Update the existing skill (default for repeat runs)
• Start fresh (rare; ask why before doing it)

Update mode changes the rest of the flow:

• Step 1 mines only history since the skill was last edited (`git log -1 --format=%cI <path>`).
• Step 2 asks what's changed or missing, not what to capture from zero.
• Step 4 edits the existing file in place. Preserve sections the user hasn't contradicted; revise ones with new evidence; add new sections only for genuinely new rules.

1. Mine their history

Locate the active workspace's transcripts before fanning out. The system prompt names the workspace's `agent-transcripts/` directory. Use only that path. Don't glob across `~/.cursor/projects/*/`. That crosses workspace boundaries and reads private chats from unrelated projects.

Survey recent agent conversations within that scope for recurring patterns. Run multiple parallel subagents across slices of history (e.g. last 2-4 weeks, split into 3 slices so each has enough material). Each slice mining subagent reads transcripts from the workspace-scoped path the parent provides, looks for the signals below, and returns a short structured list of patterns it saw with evidence pointers. Default signals worth hunting:

• Response preferences (length, tone, format, "dumb it down" corrections)
• Delegation habits (subagents, models, specialized workflows, parallelism)
• Verification posture (what "done" means; unit tests vs live repro; reviewers)
• Code and prose discipline (style, principles cited, lint/format tools)
• Process conventions (worktrees, commits, PRs, review/merge tooling)
• Meta preferences (fixing skills mid-task, proposing new ones)

Cross-check across slices before elevating a signal. Patterns seen in 2+ slices are high-confidence; lone signals are weak and usually get dropped.

2. Ask the user directly

Mining misses intent that hasn't come up yet. Use the `AskQuestion` tool (structured multi-choice) rather than asking the user to type from scratch. Lower cognitive load, higher hit rate.

Shape: one or two questions with 4-6 options each, `allow_multiple: true` for category questions. Start broad ("Which areas matter most?"), then follow up on selected areas with specific options. After the structured rounds, one free-form chat question catches anything the options missed.

Don't dump 20 questions. Two structured rounds plus one open question is usually enough.

3. Cluster findings

Group the combined signals into sections. Common ones (use only what applies):

• **Response style**: length, tone, format.
• **Autonomy**: how much to do without asking; MCP tool use.
• **Understand first**: which skills to reach for when scoping or investigating a change.
• **Subagents**: default, parallelism, model-to-task, specialized workflows.
• **Prose / code discipline**: principles, lint tools, style guides.
• **Review and verify**: repro posture, verification skills, live-testing tools.
• **Process**: git worktrees, commits, PRs, review/merge tooling.
• **Skills**: skill-authoring habits, fix-the-skill-first, proposing new skills.

The **poteto-mode** skill shows the shape. Read it for granularity. Don't copy its content; the user's rules are not the same as poteto-mode's.

4. Draft the skill

Use Cursor's built-in `create-skill` skill to author the skill. Placement:

• Path: preserve an existing mode skill's category. For a new mode, use `.cursor/skills/<handle>/<handle>-mode/SKILL.md` when the repo has an established personal category for that handle; otherwise default to `.cursor/skills/<handle>-mode/SKILL.md` in the project (or `~/.cursor/skills/<handle>-mode/` if the user prefers a personal skill).
• Handle: the user's first name or chosen identifier.
• Frontmatter `description`: trigger on their name + `/<handle>-mode` + "work in their style", not on generic keywords like "write code" or "review PR".
• Frontmatter formatting: follow `create-skill`'s YAML rules. Keep `description` as one YAML scalar; quote it or use `description: >-` with indented continuation lines when punctuation or wrapping requires it.
• Frontmatter `disable-model-invocation: true` by default. Mode skills are heavy and opinionated; they should only apply when the user explicitly invokes them (by name or slash command), not auto-trigger on description matching. Opt out only if the user explicitly wants their mode to apply on every turn.

5. Iterate on prose

Apply the **unslop** skill and `create-skill`'s writing guidelines to every line. Both apply to any agent-read prose, not just skills.

Show the draft to the user and take feedback. Expect multiple iterations. Cut ruthlessly; a mode skill is not a manual.

6. Land it

Work in a worktree off main. Commit and open a PR so the user can review it. Don't push to main directly.

Guardrails

• **Don't ove