Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/kopp0510/claude-ddnpx agentmods add skills/kopp0510/claude-dd/extractWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kopp0510/claude-dd/extract)<a href="https://agentmods.dev/skills/kopp0510/claude-dd/extract"><img src="https://agentmods.dev/badge/skills/kopp0510/claude-dd/extract/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kopp0510/claude-dd/extract"><img src="https://agentmods.dev/badge/skills/kopp0510/claude-dd/extract.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00025 | $0.01443 |
| Opus 5.5 | $0.00010 | $0.00577 |
| Sonnet 5.5 | $0.00005 | $0.00289 |
| Haiku 4.5 | $0.00003 | $0.00144 |
Grade A, and why
extract scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/self-improving-agent:extract — Create Skills from Patterns
Transforms a recurring pattern or debugging solution into a standalone, portable skill that can be installed in any project.
Usage
/self-improving-agent:extract <pattern description> # Interactive extraction
/self-improving-agent:extract <pattern> --name docker-m1-fixes # Specify skill name
/self-improving-agent:extract <pattern> --output ./skills/ # Custom output directory
/self-improving-agent:extract <pattern> --dry-run # Preview without creating files
When to Extract
A learning qualifies for skill extraction when ANY of these are true:
| Criterion | Signal |
|---|---|
| Recurring | Same issue across 2+ projects |
| Non-obvious | Required real debugging to discover |
| Broadly applicable | Not tied to one specific codebase |
| Complex solution | Multi-step fix that's easy to forget |
| User-flagged | "Save this as a skill", "I want to reuse this" |
Workflow
Step 1: Identify the pattern
Read the user's description. Search auto-memory for related entries:
# Claude Code replaces EVERY non-alphanumeric character in the resolved absolute cwd
# with a single "-" (dots, spaces and CJK included).
MEMORY_DIR="$HOME/.claude/projects/$(pwd -P | sed 's/[^a-zA-Z0-9]/-/g')/memory"
# Verify first; a missing directory means "wrong path", not "auto-memory has nothing".
# Under LC_ALL=C sed goes per byte, so glob as a fallback:
# [ -d "$MEMORY_DIR" ] || ls -d ~/.claude/projects/*"$(basename "$PWD" | sed 's/[^a-zA-Z0-9]/-/g')"*
grep -rni "<keywords>" "$MEMORY_DIR/"
If found in auto-memory, use those entries as source material. If not, use the user's description directly.
Step 2: Determine skill scope
Ask (max 2 questions):
- "What problem does this solve?" (if not clear)
- "Should this include code examples?" (if applicable)
Step 3: Generate skill name
Rules for naming:
- Lowercase, hyphens between words
- Descriptive but concise (2-4 words)
- Examples:
docker-m1-fixes,api-timeout-patterns,pnpm-workspace-setup
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +10 lines a96cb5459ac4
- 1mo ago First seen · 180 lines · 25 tokens per session scan A a7e7773e79f3
extract is a skill published in the GitHub repository kopp0510/claude-dd (12 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 1,443 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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