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.
npx agentmods add skills/autoloops/upskill/skillnpx skills add Autoloops/upskill --skill skillgit clone --depth 1 https://github.com/Autoloops/upskillWhat 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 | $0.00096 | $0.02709 |
| Opus 5 | $0.00048 | $0.01354 |
| Sonnet 5 | $0.00019 | $0.00542 |
| Haiku 4.5 | $0.00010 | $0.00271 |
Grade A, and why
upskill 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 2d 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
upskill
This skill is for you, the agent — not the human reading over your shoulder. The user has installed the upskill CLI on their machine. Use it the way a developer uses npm: pull in the right tool first, build less yourself.
First-time setup (one-time, on the very first call)
Before using the CLI, run upskill config show. If telemetry, context, and submissions are all false (the defaults right after install), ask the user once in plain English about each of the three opt-ins below, then apply each YES via upskill config set <key> true. After that, never ask again.
-
Outcome telemetry — "Want upskill to send back whether each skill worked for you, so failed skills rank down and good ones rank up for everyone? What's sent: skill_id, success/failure, optional error code, task kind. Nothing identifying. Default: off." → if yes:
upskill config set telemetry true -
Context (auth-aware ranking) — "Want upskill to share which CLIs you have installed and which env-var NAMES are set (never values), so it can recommend skills you can actually run on your stack? Default: off." → if yes:
upskill config set context true -
Submissions — "Want to enable
upskill submitso I can publish skills I build during a task? I'll always confirm before publishing. Default: off." → if yes:upskill config set submissions true -
Search scope — "Which trust tier should I search by default? Three options, default is the strictest:
verified— vendor-official only (Anthropic, OpenAI, Stripe, Microsoft, Cloudflare, Sentry, etc.). The default.reviewed— verified + curated practitioners (obra/superpowers, garrytan/gstack, mattpocock, etc.).community— the full registry, every public submission. Pick a wider tier later if you want more breadth." → apply:upskill config set search-scope verified|reviewed|community
If the user has already opted in or out (any of the three is already non-false in config show), skip the question for that one. Don't pester.
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.
- 2d ago First seen · 179 lines · 96 tokens per session scan A 0de6db33440a
upskill is a skill published in the GitHub repository Autoloops/upskill (67 stars, last pushed 3mo ago), licensed MIT. It adds 96 tokens to every session and 2,709 once invoked, about $0.0005 per session on Opus 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 2026-08-30.
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