v1.0.0 to v1.0.0

---

name: learn

description: |

Extract reusable knowledge from the current session into a persistent skill.

Use when you discover something non-obvious, create a workaround, or develop

a multi-step workflow that future sessions would benefit from.

argument-hint: "[skill-name (kebab-case)]"

allowed-tools: ["Read", "Write", "Bash", "Glob", "Grep"]

disable-model-invocation: true

---

# /learn — Skill Extraction Workflow

Extract non-obvious discoveries into reusable skills that persist across sessions.

## When to Use This Skill

Invoke `/learn` when you encounter:

- **Non-obvious debugging** — Investigation that took significant effort, not in docs

- **Misleading errors** — Error message was wrong, found the real cause

- **Workarounds** — Found a limitation with a creative solution

- **Tool integration** — Undocumented API usage or configuration

- **Trial-and-error** — Multiple attempts before success

- **Repeatable workflows** — Multi-step task you'd do again

- **User-facing automation** — Reports, checks, or processes users will request

## Workflow Phases

### PHASE 1: Evaluate (Self-Assessment)

Before creating a skill, answer these questions:

1. "What did I just learn that wasn't obvious before starting?"

2. "Would future-me benefit from this being documented?"

3. "Was the solution non-obvious from documentation alone?"

4. "Is this a multi-step workflow I'd repeat?"

**Continue only if YES to at least one question.**

### PHASE 2: Check Existing Skills

Search for related skills to avoid duplication:

```bash

# Check project skills

ls .claude/skills/ 2>/dev/null

# Search for keywords

grep -r -i "KEYWORD" .claude/skills/ 2>/dev/null

```

**Outcomes:**

- Nothing related → Create new skill (continue to Phase 3)

- Same trigger & fix → Update existing skill (bump version)

- Partial overlap → Update with new variant

### PHASE 3: Create Skill

Create the skill file at `.claude/skills/[skill-name]/SKILL.md`:

```yaml

---

name: descriptive-kebab-case-name

description: |

[CRITICAL: Include specific triggers in the description]

- What the skill does

- Specific trigger conditions (exact error messages, symptoms)

- When to use it (contexts, scenarios)

author: Claude Code Academic Workflow

version: 1.0.0

argument-hint: "[expected arguments]" # Optional

---

# Skill Name

## Problem

[Clear problem description — what situation triggers this skill]

## Context / Trigger Conditions

[When to use — exact error messages, symptoms, scenarios]

[Be specific enough that you'd recognize it again]

## Solution

[Step-by-step solution]

[Include commands, code snippets, or workflows]

## Verification

[How to verify it worked]

[Expected output or state]

## Example

[Concrete example of the skill in action]

## References

[Documentation links, related files, or prior discussions]

```

### PHASE 4: Quality Gates

Before finalizing, verify:

- [ ] Description has specific trigger conditions (not vague)

- [ ] Solution was verified to work (tested)

- [ ] Content is specific enough to be actionable

- [ ] Content is general enough to be reusable

- [ ] No sensitive information (credentials, personal data)

- [ ] Skill name is descriptive and uses kebab-case

## Output

After creating the skill, report:

```

✓ Skill created: .claude/skills/[name]/SKILL.md

Trigger: [when to use]

Problem: [what it solves]

```

## Example: Creating a Skill

User discovers that a specific R package silently drops observations:

```markdown

---

name: fixest-missing-covariate-handling

description: |

Handle silent observation dropping in fixest when covariates have missing values.

Use when: estimates seem wrong, sample size unexpectedly small, or comparing

results between packages.

author: Claude Code Academic Workflow

version: 1.0.0

---

# fixest Missing Covariate Handling

## Problem

The fixest package silently drops observations when covariates have NA values,

which can produce unexpected results when comparing to other packages.

## Context / Trigger Conditions

- Sample size in fixest is smaller than expected

- Results differ from Stata or other R packages

- Model has covariates with potential missing values

## Solution

1. Check for NA patterns before regression:

```r

summary(complete.cases(data[, covariates]))

```

2. Explicitly handle NA values or use `na.action` parameter

3. Document the expected sample size in comments

## Verification

Compare `nobs(model)` with `nrow(data)` — difference indicates dropped obs.

## References

- fixest documentation on missing values

- [LEARN:r-code] entry in MEMORY.md

```