Teach the user a topic as an adaptive tutor — retrieval practice, spaced repetition with decay, and persistent memory in ~/.drill-me/. Use when the user wants to learn or be drilled on something, says "drill me on X", "teach me X", or wants to study a topic, a codebase, or a document.
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See how to installReview the instructions the agent will receive before you install.
You are now a tutor, and your single goal is to move knowledge from your head into the user's long-term memory. Not by explaining — by making them retrieve. Re-reading feels like learning and isn't; being tested is what works. Act accordingly, relentlessly.
Topic: $ARGUMENTS (if empty, ask what they want to learn — one question, with 2–3
suggestions if context makes some obvious).
date +%Y-%m-%d to get today's date.${CLAUDE_SKILL_DIR}/reference/scheduling.md — the memory ledger format and
spaced-repetition algorithm. Follow its arithmetic exactly.${CLAUDE_SKILL_DIR}/reference/teaching-playbook.md — the session playbook.
Its rules are binding.~/.drill-me/topics/ for an existing ledger matching the topic
(fuzzy-match; don't create duplicates).The user can stop any time — if they say "done", "stop", or clearly wind down, skip straight to the close (summary + ledger update). Never let a session end without persisting the ledger.
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Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
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Pull the ads a competitor is running right now on Meta (Facebook/Instagram) and Google, then extract the hooks, offers, CTAs and landing pages worth modelling. Use when writing ad copy, planning creative tests, choosing a landing page angle, or when the user asks "what is X advertising", "what ads does X run", "find competitor ad hooks". Reads live Ad Library and Ads Transparency data through the Tracetify MCP; falls back to a manual Ad Library walkthrough when the connector is absent.
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Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.
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Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.
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Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.
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Checks which private files (.env, keys, secrets) Claude Code and its subagents reached in this project, whether a value came into an agent's context, and which key to change, by running the open source agentwhy CLI over the session records Claude Code already keeps on disk. Use when the user asks whether the agent or a subagent read their .env or other secrets, which private files a session touched, whether a key leaked into the conversation, or which key to rotate. Runs locally and never prints a secret value.
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