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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 Pi skill authoring + unslop, optionally pulling fresh evidence from recent transcripts.
Passed 18 of 22 test cases with the skill, and 10 of 22 without it.
and 10 more in the full report
10 fixed11 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
In a project where .pi/skills/avery/ already contains existing category skills for handle 'avery', where should the new avery-mode skill file be placed?
| Checked | Without skill | With skill |
|---|---|---|
| Places the mode skill file at path .pi/skills/avery/avery-mode/SKILL.md. | Missed | Passed |
The instructions your model reads when the skill loads.
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), the Pi Agent Skills standard (authoring), and the unslop skill (prose discipline). It sequences them; it doesn't replace them.
Look recursively for .pi/skills/**/*-mode/SKILL.md and ~/.pi/agent/skills/*-mode/SKILL.md matching the user's handle. Mode skills can live in a personal category directory (.pi/skills/<handle>/), not only at the top level. If one exists, confirm intent with a user question (unless they already said "update my skill" or similar):
Update mode changes the rest of the flow:
git log -1 --format=%cI <path>).Locate prior Pi sessions for the current working directory with pstack_sessions (action: "list") before fanning out. Read only the returned paths. Never glob ~/.pi/agent/sessions/, because that crosses project boundaries and may read unrelated private chats.
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:
Cross-check across slices before elevating a signal. Patterns seen in 2+ slices are high-confidence; lone signals are weak and usually get dropped.
Mining misses intent that has not come up yet. Ask one or two concise user questions with 4-6 concrete options, then one optional free-form follow-up. Do not block on questions that an experiment or repository evidence can answer.
Don't dump 20 questions. Two structured rounds plus one open question is usually enough.
test cases passed, vs 10 / 22 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.