automate-me

name	automate-me
description	Use for "automate me", "/automate-me", "create/update my -mode skill", or "turn my preferences into a skill". Mines the current project's transcripts plus direct questions, then drafts a personal <handle>-mode skill.

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. ryan-mode).

This skill orchestrates others: an inline mining pass (step 1), writing-skills plus addskill (authoring and repo install), and unslop (prose discipline). It sequences them; it doesn't replace them.

Flow

0. Check for an existing skill

Look for .claude/skills/*-mode/SKILL.md in the repo and ~/.claude/skills/*-mode/ globally, matching the user's handle. If one exists, confirm intent (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 current project's transcripts before fanning out. Locally that is ~/.claude/projects/<munged-project-path>/ (the directory whose name encodes the project's absolute path); the project's auto-memory MEMORY.md under the same root is also evidence. Use only the current project's scope. Don't glob across other projects' directories: that crosses workspace boundaries and reads private chats from unrelated work. In a sandbox with no transcript access, say so and skip to step 2; questions plus CLAUDE.md and memory carry the draft.

Survey recent conversations for recurring patterns. Run parallel subagents across slices of history (e.g. last 2-4 weeks split into 3 slices so each has enough material). Each slice miner reads transcripts from the scoped path the parent provides, hunts the signals below, and returns a short structured list of patterns with evidence pointers. Default signals:

• 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)

Have each miner return preference atoms, not summaries: trigger, decision rule, quality bar, stop condition, evidence pointer, confidence. Rate confidence per atom: strong (explicit user preference, workflow-changing correction, repeated pattern, or direct request to encode behavior), medium (accepted workflow or repeated tool/validation preference), weak (agent-chosen behavior with no user feedback, or a likely task-specific correction), contradicted (evidence points in incompatible directions — ask the user before writing anything based on it).

Cross-check across slices before elevating a signal. Patterns seen in 2+ slices are high-confidence; lone signals are weak and usually get dropped. Contradicted atoms never get codified silently.

2. Ask the user directly

Mining misses intent that hasn't come up yet. Ask structured multi-choice questions 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, multi-select 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 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; tool use.
• Understand first: which skills to reach for when scoping or investigating.
• 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 fable-mode skill shows the output shape and granularity. Don't copy its content; the user's rules are not fable-mode's.

4. Draft the skill

Author via writing-skills; install via addskill (repo commit, Codex adapter, compatibility matrix).

• Path: .claude/skills/<handle>-mode/SKILL.md in the repo (required for cloud sessions), plus a global symlink if the user wants it machine-wide.
• Handle: the user's first name or chosen identifier.
• Frontmatter description: trigger on their name plus /<handle>-mode plus "work in their style" — not generic keywords like "write code" or "review PR". Mode skills are heavy and opinionated; they should fire on explicit invocation, not auto-trigger on loose description matching.

5. Iterate on prose

Apply unslop and writing-skills' guidelines to every line. 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 on a branch off freshly-fetched main. Commit and open a PR so the user can review it. Don't push to main directly.

Guardrails

• Don't overfit to one conversation. A preference stated once and contradicted another time is noise. Require multiple instances before codifying.
• Don't be clever. Restating other skills' contents, inventing metaphors, or writing "poetic" prose for an agent reader is cost without benefit. Keep it operational.
• Reference, don't inline. Other skills the user relies on appear as path references, not pasted excerpts.
• Keep sections minimal. Only add a section if the user has a specific, non-default rule there. "Communicate clearly" is not a section. "Short paragraphs. Tables when comparing options." is.
• Name conventions generic. Use "the user" in imperatives, not the author's first name. Others may read or adopt the skill.
• Don't force symmetry. No process rules worth writing down means no Process section. Sparse is fine; bloated is not.

Evaluation

A -mode skill is subjective output; a benchmark loop isn't useful here. Vibe-check with the user: does it read like them? Did it miss anything? Then ship. Run a description-optimization loop only if trigger accuracy proves a problem in practice.

When not to use

• User wants a task-specific skill (not working conventions): writing-skills plus addskill alone, no mining.
• User wants one narrow workflow captured ("how I write commit messages"): a regular skill, not a mode skill.

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Adapted from the automate-me skill in cursor/plugins pstack (MIT, by poteto); preference atoms and confidence scale from cursor-team-kit's workflow-from-chats (MIT).