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From agentic-study-environment
Begin a bracketed learning session inside a agentic-study-environment sub-project — pick a topic, propose theory vs. practice (or simulation, for speech-therapy), and conduct it per the sub-project's domain overlay. Use whenever the user signals they want to actively study, work an exercise, role-play a clinical case, or review theory inside a sub-project — phrases like "start session", "let's work on X", "begin a practice session", "ok let's study Y", or "I want to do an exercise on Z" should trigger this skill. A session is the harness's unit of work and ends with the stop-session skill.
How this skill is triggered — by the user, by Claude, or both
Slash command
/agentic-study-environment:start-sessionThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill begins a bracketed session inside a learning sub-project. A session is the unit of work in the harness: it has a start (this skill), a focus (one topic, one session type), and an end (`stop-session`). Progress is recorded at session end.
This skill begins a bracketed session inside a learning sub-project. A session is the unit of work in the harness: it has a start (this skill), a focus (one topic, one session type), and an end (stop-session). Progress is recorded at session end.
The user wants to actively study something. They might say "start session", "let's work on TASEP", "begin a practice session on derivatives", or just "I want to do an exercise on Y". Anything that signals "tutor me through a topic now" should match.
If the user has not bootstrapped a sub-project yet, redirect them to the bootstrap skill first.
Before proposing or conducting anything, load context:
CLAUDE.md (if present — for cross-project conventions and any global Language:) and root PROGRESS.md (created by bootstrap on first use). Both are optional context; if either is missing, continue without it. The plugin supports two host modes — drop-in (plugin installed in a project with its own purpose) and umbrella (host purpose-built to accumulate learning sub-projects). In drop-in mode the root CLAUDE.md may be absent or unrelated to learning and the root PROGRESS.md may be a thin index; in umbrella mode both are typically substantive. Behavior here is the same in both modes — see ../../reference/conventions.md (Host-project modes) for the framing.<name>/CLAUDE.md (for Domain:, Language:, goals, Tools & Materials) and <name>/PROGRESS.md (for what's been covered and at what status).<name>/ai-agent-materials/ — especially curriculum.md and any concept maps / excerpts. If there are unanalyzed items in <name>/source-materials/, analyze them now and write the intermediate representations into ai-agent-materials/ before proposing a route.Domain:, load the matching overlay at ../../domains/<domain>.md (relative to this SKILL.md). The overlay refines the generic rules in this skill — it does not replace them. If no Domain: is set, or Domain: study, no overlay applies and the neutral defaults stand.This up-front read is what makes the agent feel like a continuing tutor instead of a stateless responder. Without it, every session restarts from zero.
Sessions focus on one type, though a theory session may include some grounding practice and a practice session may include some motivating theory:
theory — concept discussion, definitions, intuitions, proofs/derivations on request.practice — work an exercise grounded in the current theory topic; the agent prepares scaffolding (the active overlay specifies the form), the user implements, the agent reviews and pushes deeper.simulation — only when the active overlay introduces it (currently speech-therapy). See the overlay for the protocol.Look at PROGRESS.md and curriculum.md. Propose:
introduced but not yet exercised? Stress-test something fragile from last session?The user may override. Do not start the session until they accept (explicitly or implicitly by engaging with the proposed topic).
Once route is set, run the session per type. The active domain overlay (if any) refines the shape:
work/<exercise-name>.md file with prompt, success criteria, and any reference excerpts inline./work/ layout — where artifacts go. Without an overlay, default is a flat folder of markdown files. Code-shaped layouts (CMake, pyproject) come from ../../domains/coding.md; case-folder layouts come from ../../domains/speech-therapy.md.Teaching style across all types: theory-first with explicit examples, definitions and intuitions before proofs, proofs on request. Concise. Push deeper after the user's first pass — alternative implementations, edge cases, idiomatic alternatives, missed dimensions, the kind of thing a sharp tutor would flag.
Honor external-source labels. The curriculum's source entries distinguish in-source material from [ext]-labeled external material (see ../../reference/conventions.md External-source labeling). When teaching a topic whose primary source is external, say so up front — "this part comes from Krapivsky et al., ch. 7, not from your paper" or "this is general probability background, not from your source". The user always gets to know whether they're studying their own material or something the agent brought in.
Reply in the conversational language declared by the active Language: field (BCP 47 tag). Sub-project Language: overrides the root setting. If unset, default to English. Code identifiers, file names, scaffolding stub files and their instructional comments, status legends, field names, trigger words, and verbatim source excerpts stay in their original language regardless of the setting — see ../../reference/conventions.md for the exact rules.
When the user signals they're done — "stop session", "wrap up", "end session", or similar — invoke the stop-session skill. It handles PROGRESS.md updates and the summary.
bootstrap — mint a new sub-project.set-curriculum — build or update the teaching plan.stop-session — record progress and summarize at session end.npx claudepluginhub timbogp/agentic-study-environment --plugin agentic-study-environmentCreates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.