Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add matebenyovszky/agentplaybooks/plugin install agentplaybooksWrote 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/commands/matebenyovszky/agentplaybooks/login)<a href="https://agentmods.dev/commands/matebenyovszky/agentplaybooks/login"><img src="https://agentmods.dev/badge/commands/matebenyovszky/agentplaybooks/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/commands/matebenyovszky/agentplaybooks/login"><img src="https://agentmods.dev/badge/commands/matebenyovszky/agentplaybooks/login.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 | $0.00013 | $0.00260 |
| Opus 5.5 | $0.00005 | $0.00104 |
| Sonnet 5.5 | $0.00003 | $0.00052 |
| Haiku 4.5 | $0.00001 | $0.00026 |
Grade A, and why
login scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Authenticate the local AgentPlaybooks CLI for account-level operations.
- Run
node "${CLAUDE_PLUGIN_ROOT}/bin/agentplaybooks.js" login $ARGUMENTSin an interactive terminal. The command readsAGENTPLAYBOOKS_API_KEYfirst or prompts securely, verifies the key with the server, then stores it in the user's private AgentPlaybooks credential store. - Never ask the user to paste the key into chat and never print or inspect the credential store.
- Explain that CLI login enables
playbooks,pull, andpush. A generated MCP connection still reads the named environment variable at agent startup; the CLI never copies a secret into an MCP config. - After login, run
playbooks --jsonto verify account access and report only playbook names/identifiers, never credentials.
For a published ChatGPT/Codex account connector, use standards-compliant OAuth linking rather than distributing a user API key. API-key login remains useful for local clients, CI, and Hermes Agent.
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 · 23 lines · 13 tokens per session scan A b04356a71fab
login is a command published in the GitHub repository matebenyovszky/agentplaybooks (9 stars, last pushed 3d ago), licensed MIT. It adds 13 tokens to every session and 260 once invoked, about $0.0001 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 2026-09-05.
Other commands, from other repositories
CHANGELOG
Command "CHANGELOG" from bytedance/UI-TARS-desktop, covering 0.4.0 2024-12-10 - add logging, 1.2.29, 1.2.28, 1.2.26 and 1.2.25.
TODO
The Open-Source Multimodal AI Agent Stack: Connecting Cutting-Edge AI Models and Agent Infra.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.