QM is a shared work environment for coding agents that gives each employee and conversation an isolated workspace while allowing collaboration in Slack and on the web. Startups use it to run agents for individuals, teams, and company operations, with selectable harnesses and models. The catalogue entries extend QM with skills, instructions, and settings.
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 skills add yc-software/qm --skill interactive-logingit clone --depth 1 https://github.com/yc-software/qmWrote 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/yc-software/qm/interactive-login)<a href="https://agentmods.dev/skills/yc-software/qm/interactive-login"><img src="https://agentmods.dev/badge/skills/yc-software/qm/interactive-login/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/yc-software/qm/interactive-login"><img src="https://agentmods.dev/badge/skills/yc-software/qm/interactive-login.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00046 | $0.00673 |
| Opus 5.5 | $0.00018 | $0.00269 |
| Sonnet 5.5 | $0.00009 | $0.00135 |
| Haiku 4.5 | $0.00005 | $0.00067 |
Grade A, and why
interactive-login 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 3d 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.
What it actually says
Interactive login
Nothing captures logins for you. Every login runs in a fresh temporary home and is saved
to the keychain with the script shipped with this skill. Never log in against the real
$HOME, and never treat a token or config already in $HOME as a usable login: only a
keychain handle is. Use the same explicit sandbox_id for the skill read, login,
and save-login commands so the temporary home and save script stay on one computer.
-
Make a fresh directory and start the login with
execute(background:true)with every config location pointed into it. Copy in any config the CLI needs first (for AWS, the[profile …]/[sso-session …]blocks in.aws/config); everything under that directory becomes the saved login.d=$(mktemp -d) && echo "$d" in_d() { env HOME="$d" XDG_CONFIG_HOME="$d/.config" XDG_DATA_HOME="$d/.local/share" XDG_STATE_HOME="$d/.local/state" XDG_CACHE_HOME="$d/.cache" "$@"; } in_d aws sso login --profile work --use-device-code --no-browser in_d gh auth login --hostname github.com --git-protocol https --web in_d glab auth login --hostname gitlab.com --web in_d gcloud auth login --no-launch-browserUse the CLI's device-code mode (AWS needs
--use-device-code); a browser flow only completes on the same machine. -
Relay the verification URL and code from the job output. Answer any prompt with
backgroundactionsend_input. Use itswatchorpollaction with the returnedprocess_id; never stop the job while the person is approving. -
When it exits successfully, save the directory with the script under the absolute directory the skill read reported:
node <skill dir>/scripts/save-login.mjs aws "$d" --label "work (123456789012)"The script uploads only the files under that directory, removes the directory, and prints the credential handle. It also removes a directory that holds no usable login. Never remove the directory yourself; if the save fails, run the script again. Saving the same service again replaces that entry in place: its id, handle, owner and grants stay the same.
-
Use the login only by naming the handle in
credentialsonexecute(includingbackground:true), including to check that it works. The files are staged in a private home for that command or job, and refreshes the CLI writes are saved back when it ends.
Never print token files or paste their contents into the conversation. If the login fails or expires, start again from step 1 with a new directory.
What ships with it
1 file 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.
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.
- 3d ago Changed · +2 lines e0c3a4a077d3
- 4d ago Changed · -1 lines 1fa8a8f79aef
- 7d ago Changed · -33 lines f8efcda9f216
- 1mo ago First seen · 85 lines · 46 tokens per session scan A 4adc90342ddc
interactive-login is a skill published in the GitHub repository yc-software/qm (15,434 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 673 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
browser-automation
Use when controlling web pages with the OpenClaw browser tool, especially multi-step flows, login checks, tab management, or recovery from stale refs/timeouts.
protected-vercel-deployments
Access and test Vercel deployments protected by Vercel Authentication, SSO, or Deployment Protection with agent-browser. Use when a preview or production URL redirects to a Vercel login page, returns a protection 401 or 403, or needs short-lived Trusted Sources OIDC authentication instead of a static bypass secret or…
vercel-sandbox
Run agent-browser + Chrome inside Vercel Sandbox microVMs for browser automation from any Vercel-deployed app. Use when the user needs browser automation in a Vercel app (Next.js, SvelteKit, Nuxt, Remix, Astro, etc.), wants to run headless Chrome without binary size limits, needs persistent browser sessions across…
agentcore
Run agent-browser on AWS Bedrock AgentCore cloud browsers. Use when the user wants to use AgentCore, run browser automation on AWS, use a cloud browser with AWS credentials, or needs a managed browser session backed by AWS infrastructure. Triggers include "use agentcore", "run on AWS", "cloud browser with AWS"…
crabbox-apps
Use when asked to open, run, test, or show an app in Crabbox, including native desktops, web previews, and computer use on a temporary machine.
cloud-image-bake
Bake, select, prove, and safely retire a Cloud Worker image with crabbox and config one-liners.