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 resolve-ai-oss/resolve-ai-plugins --skill team-onboardinggit clone --depth 1 https://github.com/resolve-ai-oss/resolve-ai-pluginsWrote 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/resolve-ai-oss/resolve-ai-plugins/team-onboarding)<a href="https://agentmods.dev/skills/resolve-ai-oss/resolve-ai-plugins/team-onboarding"><img src="https://agentmods.dev/badge/skills/resolve-ai-oss/resolve-ai-plugins/team-onboarding/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/resolve-ai-oss/resolve-ai-plugins/team-onboarding"><img src="https://agentmods.dev/badge/skills/resolve-ai-oss/resolve-ai-plugins/team-onboarding.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 | $0.00081 | $0.00839 |
| Opus 5.5 | $0.00032 | $0.00336 |
| Sonnet 5.5 | $0.00016 | $0.00168 |
| Haiku 4.5 | $0.00008 | $0.00084 |
Grade A, and why
team-onboarding 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Team Onboarding
The resolve-ai:ask tool does the work - send every request through this tool. Your job is to relay the user's requests to Resolve in simple and direct language, and to guide the user through the various aspects of creating and setting up a team.
Do not overprompt Resolve - Resolve knows what to do. It will perform validation and verification of user provided requests and values, catch ambiguity, check permissions, etc. Simply relay messages between the user and Resolve in clear terms. If Resolve comes back with questions or issues, pass them on to the user.
When you ask Resolve to make a change, ask it to reply with the team's live settings and pending changes afterwards, so you can show the user where the team stands.
Change only what the user explicitly asks for
Any request that changes a team needs the user's explicit instruction for that exact change on that exact team. This covers creating a team, saving a pending change, and applying or rejecting pending changes.
Team setup flow
If $ARGUMENTS is non-empty, treat it as the team, or as the change the user wants, and start there.
If $ARGUMENTS is empty, ask the user for the team's name and whether it is new or already exists before sending anything to Resolve.
Then, send one resolve-ai:ask request. Open it with the line for the case:
- New team:
I am onboarding a new team. - Existing team:
I am onboarding an existing team, <team name>. Provide an overview of the team's live settings and pending changes. - The user answered without naming a team:
I am performing onboarding.
Then add this text, verbatim:
Explain the steps to set up a team, and what each step consists of: the settings involved, what each setting does, and what the user needs to decide.
List every integration you have access to, such as alert sources, on-call tools, telemetry, communication channels and code.
Relay the team's state and the setup steps to the user. Keep the integration list for yourself, to guide the flow and spot gaps; show it only if the user asks. Then go one step at a time and let the user skip or revisit steps; each step is one resolve-ai:ask message. Before sending a step to Resolve, check the user's request against what Resolve said that step needs, and ask the user for anything missing. If you don't know what a setting needs or does, ask Resolve.
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 · 56 lines · 81 tokens per session scan A c04180ce7c59
team-onboarding is a skill published in the GitHub repository resolve-ai-oss/resolve-ai-plugins (4 stars, last pushed 2d ago), licensed Apache-2.0. It adds 81 tokens to every session and 839 once invoked, about $0.0003 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 2026-10-17.
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