agentmods.dev

autoskill

A workflow-analysis tool that studies your recent computer work through Screenpipe, a local service that records screen activity. It finds repeated research patterns and drafts new skills or combinations of existing skills.

In plain words
What is it for?
Use it to review a chosen period of work, group repeated tasks, compare them with existing academic skills, and propose new skills for uncovered workflows.
Why use it?
It helps turn recurring manual workflows into reusable instructions. It also makes clear when the required local recording service or configured language-model connection is unavailable.

Skill for Claude CodeCodex

22 3mo ago B scan 122 tokens copy · 88% MIT
View the source file crazymsn/academic-skills
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/crazymsn/academic-skills/autoskill
Any agent
npx skills add crazymsn/academic-skills --skill autoskill
Clone the repo
git clone --depth 1 https://github.com/crazymsn/academic-skills

Made for: Claude Code, Codex.

Per session 122 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,753 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. Scan, not verified.
Origin 88% 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 $0.00122 $0.02753
Opus 5 $0.00061 $0.01376
Sonnet 5 $0.00024 $0.00551
Haiku 4.5 $0.00012 $0.00275

Measured yesterday against content hash a24cecfb0fa4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 yesterday.

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

88% identical to autoskill — 41 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.

academic-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. yesterday First seen · 219 lines · 122 tokens per session scan B a24cecfb0fa4

Subscribe to this mod's changes

autoskill is a skill published in the GitHub repository crazymsn/academic-skills (22 stars, last pushed 3mo ago), licensed MIT. It adds 122 tokens to every session and 2,753 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 88% identical to autoskill, differing in 41 lines, and is treated as a copy.

Code reviewProductivityResearch
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