By TimboGP
A Claude Code learning harness: structured tutor sessions over your own source materials, in any domain.
Adjust the difficulty level of a learning sub-project's curriculum — simplify a too-advanced curriculum into a gentler on-ramp, or deepen a too-introductory one into something with real teeth. Use whenever the user signals the curriculum's level doesn't match their starting point — phrases like "this paper is too hard, can we build up to it?", "simplify the curriculum", "make it easier", "go more advanced", "I want a more rigorous version", "this is too basic, push deeper", "I need an intro version of this", or "create a simpler/harder curriculum from this material". The skill is allowed to pull in external materials and training knowledge to fill the gap between the source's natural level and the target level, but every external addition is explicitly labeled — the user always knows what came from their own source materials and what came from elsewhere.
Scaffold a new learning sub-project under the agentic-study-environment harness — create the directory, generate per-project CLAUDE.md and PROGRESS.md from templates, and register the new project in the root PROGRESS.md. Use this skill whenever the user wants to start studying a new topic, paper, book, language, or domain with the tutor harness — phrases like "bootstrap a new project to learn X", "start a new sub-project for Y", "set up a learning project on Z", or "create a sub-project to study W" should all trigger this skill. Do not use for general project scaffolding outside the agentic-study-environment harness.
Build or update the teaching curriculum for a learning sub-project from its source materials. Use whenever the user wants to plan, structure, or refresh the learning path for a sub-project — phrases like "set curriculum for X", "update the curriculum", "plan the teaching path", "build a curriculum from these materials", or general intent to organize source material into an ordered teaching plan. This skill reads the sub-project's source materials and writes ai-agent-materials/curriculum.md. It does not start a session and does not update PROGRESS.md.
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.
End the current learning session — update the sub-project's PROGRESS.md (topics + journal), mirror status changes into the root PROGRESS.md, and produce a concise summary of what was covered. Use whenever the user signals the session is over — phrases like "stop session", "let's wrap up", "end session", "we're done for today", or any out-of-character debrief signal (for speech-therapy simulations — "debrief", "end simulation"). Do not invoke this skill proactively at the end of a long conversation unless the user signals it.
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A Claude Code learning harness — structured tutor sessions over your own source materials, in any domain.
You bring a topic and the source material (a paper, a book, a syllabus, a clinical case load, anything). The harness turns Claude Code into a tutor that brackets the work into focused sessions, keeps a per-project curriculum and progress log, and adapts its teaching shape per the domain you're studying via swappable overlays.
It's a harness, not a course. There's no curated content here — you provide the materials, and the agent works them with you.
Two short transcripts. The fluff is trimmed; everything bracketed […] is paraphrasing what the agent does silently between turns.
> let's work on tasep
[reads PROGRESS.md, tasep/CLAUDE.md, tasep/ai-agent-materials/curriculum.md,
loads domains/coding.md (Domain: coding)]
Last session you exercised T0 (env + smoke tests) and got T1 (TASEP model)
and T2 (master equation) introduced. T3 (mean-field closure) is the next
unlocked topic.
Proposal: practice session on T3 — implement a mean-field density-profile
solver and compare against your existing notebook results. ~45 min.
Override?
> sounds good
I'll write a stub at tasep/work/src/tasep/mean_field.py with the signature,
the boundary-condition cases, and inline TODOs for each block. Tests get
a corresponding stub at tests/test_mean_field.py.
[writes the two stub files]
Three things to think about as you implement:
1. The α < ½ (low-density) and α > ½ (high-density) cases collapse
differently in the bulk — your loop should branch cleanly there.
2. The closure assumes ⟨n_i n_{i+1}⟩ ≈ ⟨n_i⟩⟨n_{i+1}⟩; flag where in code
you're committing to that.
3. Watch numpy broadcasting on the boundary terms — easy off-by-one.
Ping when you have a first pass.
> start session on aphasia-cases
[reads PROGRESS.md, aphasia-cases/CLAUDE.md (Domain: speech-therapy),
source-materials/patients/maria-58.md, loads domains/speech-therapy.md]
Three profiles available: maria-58 (Broca's aphasia, post-stroke,
week 4), tomas-67 (anomic, mild), liam-7 (developmental language delay).
Curriculum says you're due an initial-anamnesis simulation. Maria is the
most complex of the three. Start there, or warm up on the milder cases?
> let's do maria
Loading profile: Broca's aphasia, stroke 4 weeks ago, former teacher,
lives with her daughter, full receptive comprehension, frustrated by
the gap between what she wants to say and what comes out. Knows she
had a stroke; doesn't know the prognosis specifics. Speech: agrammatic,
2–3 word utterances, intact intonation, articulation slow.
Entering character. You're meeting her for the first time.
---
[in character]
hello... I... Maria... yes.
> Hello Maria, I'm so glad you could come in today. How are you feeling?
(pause) ...tired. um. (gestures toward her head) ...words. no.
> Can you tell me a little about what brought you here?
(frowns) ...hospital. before. (long pause) speak... not. daughter — she
say... come.
The session ends with stop session; the agent steps out of character (for simulations), updates the sub-project's PROGRESS.md and the root one, and summarizes what moved.
The harness ships as a Claude Code plugin at plugin/agentic-study-environment/. The plugin exposes four lifecycle skills:
| Skill | Trigger | What it does |
|---|---|---|
agentic-study-environment:bootstrap | "bootstrap a project for X" | Mint a new sub-project (<name>/CLAUDE.md, <name>/PROGRESS.md, <name>/source-materials/); register in root PROGRESS.md |
agentic-study-environment:set-curriculum | "set curriculum for X" | Build or update <name>/ai-agent-materials/curriculum.md from source materials |
agentic-study-environment:start-session | "start session", "let's work on X" | Begin a bracketed learning session (theory / practice / domain-specific types); load the active domain overlay |
agentic-study-environment:stop-session | "stop session", "wrap up" | Update sub-project + root PROGRESS.md, summarize what was covered |
agentic-study-environment:adjust-level | "simplify the curriculum", "make it harder", "build up to this paper" | Rewrite the curriculum at a different level — simpler or harder — pulling in external material with strict labels |
Each sub-project lives in its own <name>/ directory and declares Domain: (and optionally Language:) in its own CLAUDE.md. The matching overlay at plugin/agentic-study-environment/domains/<domain>.md specifies the practice/review shape, scaffolding form, and /work/ layout for that domain. Two overlays ship in the box:
npx claudepluginhub timbogp/agentic-study-environment --plugin agentic-study-environmentUltra-compressed communication mode. Cuts ~75% of tokens while keeping full technical accuracy by speaking like a caveman.
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