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learn skill

by pedrohcgs·pedrohcgs/claude-code-my-workflow·1.6k stars·MIT

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.

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Is the learn skill safe?

Clean: nothing in its files matched our rules. We read 1 file in the folder on 2026-09-28.

No findings.

Install the learn skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/pedrohcgs/claude-code-my-workflow.git /tmp/claude-code-my-workflow
mkdir -p ~/.claude/skills
cp -r /tmp/claude-code-my-workflow/.claude/skills/learn ~/.claude/skills/learn
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

/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:

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

More skills from pedrohcgs/claude-code-my-workflow

  • Aadjudicate-reviewTurn an incoming set of findings — from an AI reviewer, a referee report, a code review, a linter, or a second model — into verified fixes, without letting a confident misread damage correct work. Every finding is a CANDIDATE until checked against the actual source. Use whenever you receive review comments, audit findings, or a critique you did not write yourself, especially when the reviewer is a model or when the volume is too large to check by feel.
  • Aaudit-reproducibilityEnforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs. Report PASS/FAIL per claim against tolerance thresholds. Use before submission and before releasing a replication package.
  • Ablast-radiusBefore and after changing anything shared — a function's return value, a signature, a schema, a label set, a config default, a constant, a file format — find every consumer and actually run them. Catches the change that looks purely additive but silently breaks a contract in a file you never opened. Use when editing shared code, adding a field/column/return element, renaming, changing units or defaults, or touching a pipeline that produces reported numbers.
  • Acapture-environmentSnapshot the computational environment for a replication package — detects the analysis stack (R / Stata / Python) and emits the right lockfiles (renv.lock + sessionInfo.txt, requirements.txt / environment.yml / uv.lock, Stata version + ado package list), records seeds and RNG kind, optionally writes a pinning Dockerfile, and produces a paste-ready "Computational requirements" block. Use when user says "capture the environment", "snapshot my dependencies", "pin the versions", "make a renv.lock / requirements.txt", "make this byte-reproducible", or before releasing a replication package to openICPSR / the AEA Data Editor.
  • AchallengeStress-test a finding against the choices you did not make. Enumerates the discrete forks a competent analyst could have taken (measure definition, sample filter, control set, clustering level, weighting, functional form), runs the specification grid, and reports the distribution rather than a point estimate — then attacks the identifying assumption with named, computable sensitivity statistics. Use when the user says "is this robust", "challenge this result", "specification curve", "multiverse", "how sensitive is this", "what if I'd used a different measure", "stress-test my estimate", or before a result becomes a headline claim. NOT a reviewer of prose or code — it challenges the CLAIM.
  • AcheckpointSave a structured state snapshot before stopping or handing off. Captures the active plan, recent decisions, file pointers (with line numbers), open questions, and the next 1–3 actions into a checkpoint file under `quality_reports/checkpoints/`. Optionally proposes `[LEARN]` entries to add to MEMORY.md. Use when user says "checkpoint", "save state", "snapshot before I stop", "where am I", "wrap up the session for handoff", or before a long break / model switch / collaborator handoff. Companion to (NOT replacement for) the narrative session-log workflow.
  • Acoauthor-briefGenerate a co-author / collaborator handoff brief for a multi-author, multi-machine project — summarizing what changed since the last brief (git delta), the current state of each artifact (manuscript, analysis, slides), open questions, how to reproduce locally, and any restricted-data access steps. Use when user says "coauthor brief", "handoff brief", "bring my coauthor up to speed", "what changed since last week", "onboard a collaborator", "write a handoff for [name]", or before sending a co-author the repo. NOT a commit or a checkpoint — it is the cross-machine, cross-person summary `meta-governance.md` only partially covers.
  • AcommitCommit the current work — runs the quality, consistency and passport gates, branches off main if needed, stages specific files, and writes a commit whose subject states what is now true. Pushes and opens a pull request only with --pr or when the user asks; never merges — a merge happens only when the user explicitly says to merge. Use ONLY on explicit commit intent — user says "commit", "let's commit this", "open a PR", or prefixes with `/commit`. Do NOT auto-invoke on vague end-of-task phrases ("we're done", "wrap up") — those require explicit confirmation first. Never force-pushes or skips hooks.
  • Acompile-latexCompile a Beamer LaTeX slide deck with XeLaTeX (3 passes + bibtex). Use when user says "compile", "build the slides", "rebuild the PDF", "run latex", "render the tex", or asks why a `.tex` file isn't producing a PDF. Operates on `Slides/*.tex`.
  • Acompress-sessionDistill the current conversation into a structured note (decisions made, open questions, file pointers with line numbers, next 1–3 actions) and save to `quality_reports/session_logs/` before auto-compression. Differs from `/checkpoint` (explicit stop-point snapshot) and from auto-compaction (which truncates rather than distills). Use when context is approaching auto-compact threshold, when a long pipeline has accumulated many decisions, or when the user says "compress", "distil this session", "before we hit auto-compact", "structured handoff before context resets".
  • Acontext-statusShow current context status and session health. Use to check how much context has been used, whether auto-compact is approaching, and what state will be preserved.
  • Acreate-lectureCreate a new Beamer lecture `.tex` from source papers and materials, with notation consistency checks and the project's preamble wired in. Use when user says "create a lecture on X", "new lecture from these papers", "start a deck on topic Y", "scaffold a new Beamer file", "build me a lecture from these PDFs". Scaffolds the full deck — NOT for compiling existing `.tex` (use `/compile-latex`).

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