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learn

learn

github.com/pedrohcgsAgent workflows1.7k47 installsUpdated 5w ago
Repository

About learn

learn is a workflow skill for recording non-obvious discoveries as reusable skills, aimed at agents that need to retain solutions across sessions. It evaluates whether a finding merits documentation, checks for overlap, and guides creation and review.

Key features

  1. 01Assess whether a discovery is reusable
  2. 02Search for related existing skills
  3. 03Create skills in .claude/skills/
  4. 04Check specificity, testing, and sensitivity

Use cases

  1. 01Record a debugging fix that took investigation
  2. 02Document an undocumented integration workaround
  3. 03Preserve a repeatable multi-step workflow

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/pedrohcgs/claude-code-my-workflow --skill learn

After installing, type/learnin Claude Code to confirm it loaded.

Details

From the project README

/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:

# 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:

---
name: descriptive-kebab-case-name
description: |
  [What the skill does, then the situations it applies to — the key use first.
   Include an exact error string only when that string is the trigger.]
  - What the skill does
  - Trigger conditions (symptoms; an exact error message when that is the trigger)
  - When to use it (contexts, scenarios)
metadata:
  author: [you]
  version: "1.0"
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:

---
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.
metadata:
  author: [you]
  version: "1.0"
---

# 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]))
  1. Explicitly handle NA values or use na.action parameter
  2. 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

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