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/Lord1Egypt/scientific-agent-toolkitnpx agentmods add skills/lord1egypt/scientific-agent-toolkit/autoskillWrote 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/lord1egypt/scientific-agent-toolkit/autoskill)<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/autoskill"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/autoskill/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/lord1egypt/scientific-agent-toolkit/autoskill"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/autoskill.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 | $0.00123 | $0.02764 |
| Opus 5.5 | $0.00049 | $0.01106 |
| Sonnet 5.5 | $0.00025 | $0.00553 |
| Haiku 4.5 | $0.00012 | $0.00276 |
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 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.
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 This is a copy
89% identical to autoskill — 142 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.
How it starts
The opening of the file, as written. The whole thing — 220 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()raisesScreenpipeUnreachablewith 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.yamlinto 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.pypulls data over localhost HTTP.scripts/cluster.pyreduces the timeline to app/duration/title summaries.scripts/redact.pystrips 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 runningGemma-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 inconfig.yamlfor 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.mdfor the Caddy pattern.
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.
- .gitignore 34 B
- config.yaml 2.0 KB
- references/https-proxy.md 1.5 KB
- references/screenpipe-config.yaml 1.5 KB
- scripts/autoskill.py 1.1 KB runs code
- scripts/backends.py 2.3 KB runs code
- scripts/cluster.py 1.7 KB runs code
- scripts/doctor.py 3.4 KB runs code
- scripts/fetch_window.py 1.1 KB runs code
- scripts/match_skills.py 1.3 KB runs code
- scripts/promote.py 1.5 KB runs code
- scripts/redact.py 1.6 KB runs code
- scripts/run.py 7.2 KB runs code
- scripts/synthesize.py 2.3 KB runs code
- tests/conftest.py 113 B runs code
- tests/smoke_lmstudio.py 2.0 KB runs code
- tests/test_backends.py 4.2 KB runs code
- tests/test_cli.py 1.5 KB runs code
- tests/test_cluster.py 2.7 KB runs code
- tests/test_doctor.py 3.0 KB runs code
- tests/test_e2e.py 12 KB runs code
- tests/test_fetch_window.py 3.7 KB runs code
- tests/test_match_skills.py 2.5 KB runs code
- tests/test_promote.py 3.5 KB runs code
- tests/test_redact.py 4.5 KB runs code
- tests/test_run.py 8.0 KB runs code
- tests/test_synthesize.py 3.1 KB runs code
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 First seen · 220 lines · 123 tokens per session scan B c2245ebd1e1f
autoskill is a skill published in the GitHub repository Lord1Egypt/scientific-agent-toolkit (3 stars, last pushed 4mo ago), licensed MIT. It adds 123 tokens to every session and 2,764 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). It is 89% identical to autoskill, differing in 142 lines, and is treated as a copy.
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