# Improve Skills

> Improve existing skills based on the current session. Use at the end of a session (or when the user asks) to capture new debugging patterns, data issues, data validation tracks, querying techniques, doc references, or workflow improvements learned during the session. Keeps skills lean and personalized.

- Skill: `dlt-hub/improve-skills` (Agent Skill)
- Install (CLI): `npx skillmds@latest add dlt-hub/improve-skills`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dlt-hub/improve-skills/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: dlt-hub (https://skillmd.com/u/dlt-hub)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/dlt-hub/improve-skills

---


# Improve skills from session

Review the current session and update skills with essential learnings. Skills are the team's institutional memory — keep them lean, specific, and actionable.

## Principles

- **Read the skill first** — always read the current SKILL.md before proposing changes. Don't duplicate what's already there.
- **Lean updates only** — add the minimum needed. A single bullet point or a 3-line code snippet is often enough.
- **Real problems only** — only add things that actually happened in this session or that the user explicitly asked to capture. No hypothetical scenarios.
- **Doc references matter** — if you found a docs page that was essential to solving a problem, add it as an "Essential Reading" link.
- **Tools and commands** — if a specific CLI command or MCP tool was key to diagnosing an issue, add it.
- **Don't restructure** — don't reorganize or rewrite existing skill content. Append to the right section or add a new subsection.

## Process

### 1. Scan the session for learnings

First identify which skills are active in the current toolkit — check the installed skills directory or use the `toolkit_info` MCP tool. Then review the conversation for:

**Errors and debugging**:
- New error types, root causes, and the commands or MCP tools that diagnosed them
- Config settings that helped (e.g., verbosity, timeouts, flags)
- Workarounds for API, source, or destination-specific behaviors

**Data and schema**:
- Unexpected data types or coercions needed
- Schema surprises (nesting, missing columns, naming conventions)
- Processing patterns that worked

**Data access and querying**:
- Library-specific gotchas (e.g., ibis, dlt dataset API)
- Useful query patterns or MCP tool calls

**Source and pipeline configuration**:
- Auth, pagination, or rate-limit quirks
- Config resolution surprises
- Source/resource parameterization patterns

**Workflow**:
- Missing cross-references between skills
- New skills that should be in the workflow
- Steps that are in the wrong order

Map each learning to the most relevant skill in the active toolkit.

### 2. Read the target skills

For each learning, read the relevant SKILL.md. Check:
- Is this already covered?
- Where does it fit best? (which section)
- Would it contradict or duplicate existing content?

### 3. Propose changes to the user

Present a summary of proposed updates:

```
Proposed skill updates:

<skill-name>:
  + [section] Added: <brief description>

<skill-name>:
  (no changes)

<skill-name>:
  + [section] Added: <brief description>
```

Get user approval before editing. The user may want to adjust, skip, or add more.

### 4. Apply changes

Edit the SKILL.md files. For each change:
- Add to the most relevant existing section
- If no section fits, add a new subsection at the end (before "Next steps" if present)
- Include doc links as `**Ref:** <url>` or `**Essential Reading:** <url>`
- Keep code examples minimal — 3-5 lines max

### 5. Update workflow if needed

Check if `workflow.md` cross-references are still accurate after skill changes. Update if new skills were added or handoff points changed.