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autoskill

autoskill is a skill for Claude Code from dralkh/iktinah. It costs 123 tokens per session (2,957 once invoked), scanned B, a copy of autoskill, MIT.

A tool that examines repeated workflows recorded by Screenpipe, a local screen-activity service, and suggests new reusable agent skills or combinations of existing skills.

In plain words
What is it for?
Use it to review recent work, group recurring research tasks, compare them with existing skills, and draft skills for uncovered patterns.
Why use it?
It helps identify routine work that could be turned into an automated workflow. It requires Screenpipe to be running and uses the recorded local activity as its source.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: built for openclaw.

Not installable on its own: it runs a file from its repository that does not travel with it. Clone the repository, or install whatever ships that file. The line is ./target/release/screenpipe doctor # confirm permissions + ffmpeg.

not rated 77 1mo ago B scan 123 tokens copy · 97% MIT
View the source file dralkh/iktinah
Install

Getting it into your agent

There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.

Made for: Claude Code.

Wrote 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.

agentmods badge for autoskill

README.md
[![agentmods](https://agentmods.dev/badge/skills/dralkh/iktinah/autoskill.svg)](https://agentmods.dev/skills/dralkh/iktinah/autoskill)
Your own site
<a href="https://agentmods.dev/skills/dralkh/iktinah/autoskill"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/autoskill.svg" alt="Measured on agentmods" height="20"></a>
Per session 123 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,957 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. Scan, not verified.
Origin 97% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00123 $0.02957
Opus 5 $0.00062 $0.01478
Sonnet 5 $0.00025 $0.00591
Haiku 4.5 $0.00012 $0.00296

Measured 2d ago against content hash 9f2114f66210, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade B, and why

autoskill scanned grade B with 1 finding 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 2d ago.

The scan reads SKILL.md. This mod also ships 23 executable files (scripts/autoskill.py, scripts/backends.py, scripts/cluster.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Asks for rootmediumPrivilege escalation

A mod that escalates privileges can change anything on the machine, not only the project.

# # if xcodebuild plug-ins error: sudo xcodebuild -runFirstLaunch
Origin

This is a copy

97% identical to autoskill — 42 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/autoskill/SKILL.md · 219 lines

How it starts

The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.

autoskill

Requires a running screenpipe daemon. This skill has no alternate data source — it reads exclusively from the local screenpipe HTTP API (default http://localhost:3030). If the daemon isn't running, run() raises ScreenpipeUnreachable with install instructions.

Network access & environment variables. This skill makes authenticated HTTP requests to (a) the user's local screenpipe daemon on loopback, and (b) the user-configured LLM backend — one of http://localhost:1234/v1 (LM Studio, default), https://api.anthropic.com (opt-in Claude), or a user-supplied BYOK Foundry gateway. The skill reads three environment variables — SCREENPIPE_TOKEN, ANTHROPIC_API_KEY, FOUNDRY_API_KEY — and uses each only to authenticate to the single endpoint its name implies. No other network destinations, no telemetry, no data egress to any third party.

Overview

Turn the user's own workflow history — captured passively by the local screenpipe daemon — into new skills. This skill is on-demand: the user invokes it with a time window, it queries screenpipe's local HTTP API, clusters repeated workflow patterns, compares each pattern against the existing skills in this repo, and produces a staged folder of proposals the user can review, edit, and promote.

When to Use This Skill

Invoke this skill when the user asks to:

  • "Analyze my last 4 hours / day / week and propose new skills."
  • "Look at what I've been doing and tell me what's not covered yet."
  • "Draft a skill from my recent workflow."
  • "Find composition recipes for workflows I repeat."

Do not invoke it for one-off questions about screenpipe itself, for real-time screen queries, or without an explicit user request — the skill analyzes sensitive local content and must stay explicitly user-triggered.

Privacy Posture

  • Screenpipe handles app/window filtering at capture time. Install a starter deny-list by copying references/screenpipe-config.yaml into the user's screenpipe config. Sensitive apps (password managers, messaging, banking) are never OCR'd in the first place.
  • Raw OCR never leaves the machine. scripts/fetch_window.py pulls data over localhost HTTP. scripts/cluster.py reduces the timeline to app/duration/title summaries. scripts/redact.py strips emails, API keys, bearer tokens, and phone numbers as defense-in-depth before any cluster summary reaches the LLM.
  • LLM backend defaults to local. The recommended setup is LM Studio running Gemma-4-31B-it — strong reasoning at a size that fits on most workstation GPUs, and no data ever leaves your machine. Cloud backends (claude, foundry) are opt-in and documented in config.yaml for users who explicitly want them. Detection and embeddings always run locally regardless of backend choice.
  • Dry-run mode (--plan) prints the exact timeline that will be analyzed before any LLM call.
  • TLS for localhost (optional, for corporate policy): see references/https-proxy.md for the Caddy pattern.

Read the full file on GitHub · 219 lines

Files

What ships with it

27 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 2d ago First seen · 219 lines · 123 tokens per session scan B 9f2114f66210

Subscribe to this mod's changes

autoskill is a skill published in the GitHub repository dralkh/iktinah (77 stars, last pushed 1mo ago), licensed MIT. It adds 123 tokens to every session and 2,957 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). It is 97% identical to autoskill, differing in 42 lines, and is treated as a copy.

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