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/sync)<a href="https://agentmods.dev/commands/matebenyovszky/agentplaybooks/sync"><img src="https://agentmods.dev/badge/commands/matebenyovszky/agentplaybooks/sync/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/sync"><img src="https://agentmods.dev/badge/commands/matebenyovszky/agentplaybooks/sync.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 | $0.00018 | $0.00251 |
| Opus 5 | $0.00009 | $0.00125 |
| Sonnet 5 | $0.00004 | $0.00050 |
| Haiku 4.5 | $0.00002 | $0.00025 |
Grade A, and why
sync scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Synchronize the project's portable playbook with the AgentPlaybooks CLI.
- Run:
node "${CLAUDE_PLUGIN_ROOT}/bin/agentplaybooks.js" sync $ARGUMENTS --json - Summarize the plan for the user: manifest create/update, platform files to
be written per target, secret references discovered, and any conflicts.
If
suggestedTargetsis non-empty, no target is enabled yet — tell the user which agent tools were detected and offer--target=<types>(claude, cursor, codex, antigravity, hermes). - Conflicts mean the same skill or MCP server has different definitions on different platforms. Do not work around them; ask the user which variant is canonical, align the files, then re-plan.
- Only after the user confirms the plan, run the same command with
--applyand report what was written (backups land in.agentplaybooks/backups/).
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.
- 8d ago First seen · 19 lines · 18 tokens per session scan A 6659492e6cc1
sync is a command published in the GitHub repository matebenyovszky/agentplaybooks (5 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 251 once invoked, about $0.0001 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 2026-08-31.
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.