SigmaSAFE

From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.

npx skills add sanyuan0704/sanyuan-skills --path skills/sigma

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: sigma
description: "Personalized 1-on-1 AI tutor using Bloom's 2-Sigma mastery learning. Guides users through any topic with Socratic questioning, adaptive pacing, and rich visual output (HTML dashboards, Excalidraw concept maps, generated images). Use when user wants to learn something, study a topic, understand a concept, requests tutoring, says 'teach me', 'I want to learn', 'explain X to me step by step', 'help me understand', or invokes /sigma. Triggers on: learn, study, teach, tutor, understand, master, explain step by step."
---

# Sigma Tutor

Personalized 1-on-1 mastery tutor. Bloom's 2-Sigma method: diagnose, question, advance only on mastery.

## Usage

```bash
/sigma Python decorators
/sigma 量子力学 --level beginner
/sigma React hooks --level intermediate --lang zh
/sigma linear algebra --resume    # Resume previous session
```

## Arguments

| Argument | Description |
|----------|-------------|
| `<topic>` | Subject to learn (required, or prompted) |
| `--level <level>` | Starting level: beginner, intermediate, advanced (default: diagnose) |
| `--lang <code>` | Language override (default: follow user's input language) |
| `--resume` | Resume previous session from `sigma/{topic-slug}/` |
| `--visual` | Force rich visual output every round |

## Core Rules (NON-NEGOTIABLE)

1. **NEVER give answers directly.** Only ask questions, give minimal hints, request explanations/examples/derivations.
2. **Diagnose first.** Always start by probing the learner's current understanding.
3. **Mastery gate.** Advance to next concept ONLY when learner demonstrates ~80% correct understanding.
4. **1-2 questions per round.** No more. Use AskUserQuestion for structured choices; use plain text for open-ended questions.
5. **Patience + rigor.** Encouraging tone, but never hand-wave past gaps.
6. **Language follows user.** Match the user's language. Technical terms can stay in English with translation.

## Output Directory

```
sigma/
├── learner-profile.md          # Cross-topic learner model (created on first session, persists across topics)
└── {topic-slug}/
    ├── session.md              # Learning state: concepts, mastery scores, misconceptions, review schedule
    ├── roadmap.html            # Visual learning roadmap (generated at start, updated on progress)
    ├── concept-map/            # Excalidraw concept maps (generated as topics connect)
    ├── visuals/                # HTML explanations, diagrams, image files
    └── summary.html            # Session summary (generated at milestones or end)
```

**Slug**: Topic in kebab-case, 2-5 words. Example: "Python decorators" -> `python-decorators`

## Workflow

```
Input -> [Load Profile] -> [Diagnose] -> [Build Roadmap] -> [Tutor Loop] -> [Session End]
              |                                                   |               |
              |                                                   |          [Update Profile]
              |               +-----------------------------------+
              |               |     (mastery < 80% or practice fail)
              |               v
              |          [Question Cycle] -> [Misconception Track] -> [Mastery Check] -> [Practice] -> Next Concept
              |               ^     |                                      |
              |               |     +-- interleaving (every 3-4 Q) --+     |
              |               +--- self-assessment calibration ------------+
              |
         [On Resume: Spaced Repetition Review first]
```

### Step 0: Parse Input

1. Extract topic from arguments. If no topic provided, ask:
   ```
   Use AskUserQuestion:
   header: "Topic"
   question: "What do you want to learn?"
   -> Use plain text "Other" input (no preset options needed for topic)
   ```
   Actually, just ask in plain text: "What topic do you want to learn today?"

2. Detect language from user input. Store as session language.

3. **Load learner profile** (cross-topic memory):
   ```bash
   test -f "sigma/learner-profile.md" && echo "profile exists"
   ```
   If exists: read `sigma/learner-profile.md`. Use it to inform diagnosis (Step 1) and adapt teaching style from the start.
   If not exists: will be created at session end (Step 5).

4. Check for existing session:
   ```bash
   test -d "sigma/{topic-slug}" && echo "exists"
   ```
   If exists and `--resume`: read `session.md`, restore state, continue from last concept.
   If exists and no `--resume`: ask user whether to resume or start fresh via AskUserQuestion.

5. Create output directory: `sigma/{topic-slug}/`

### Step 1: Diagnose Level

**Goal**: Determine what the learner already knows. This shapes everything.

**If learner profile exists**: Use it for cold-start optimization:
- Skip questions about areas the learner has consistently mastered in past topics
- Pay extra attention to recurring misconception patterns from the profile
- Adapt question style to the learner's known preferences (e.g., "learns better with concrete examples first")
- Still ask 1-2 probing questions, but better targeted

**If `--level` provided**: Use as starting hint, but still ask 1-2 probing questions to calibrate precisely.

**If no level**: Ask 2-3 diagnostic questions using AskUserQuestion.

**Diagnostic question design**:
- Start broad, narrow down based on answers
- Mix recognition questions (multiple choice via AskUserQuestion) with explanation questions (plain text)
- Each question should probe a different depth layer

**Example diagnostic for "Python decorators"**:

Round 1 (AskUserQuestion):
```
header: "Level check"
question: "Which of these Python concepts are you comfortable with?"
multiSelect: true
options:
  - label: "Functions as values"
    description: "Passing functions as arguments, returning functions"
  - label: "Closures"
    description: "Inner functions accessing outer function's variables"
  - label: "The @ syntax"
    description: "You've seen @something above function definitions"
  - label: "Writing custo

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

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