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 withoneai/one-agent-plugin --skill dovetailgit clone --depth 1 https://github.com/withoneai/one-agent-pluginWrote 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/withoneai/one-agent-plugin/dovetail)<a href="https://agentmods.dev/skills/withoneai/one-agent-plugin/dovetail"><img src="https://agentmods.dev/badge/skills/withoneai/one-agent-plugin/dovetail/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/withoneai/one-agent-plugin/dovetail"><img src="https://agentmods.dev/badge/skills/withoneai/one-agent-plugin/dovetail.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 | $0.00116 | $0.03884 |
| Opus 5 | $0.00058 | $0.01942 |
| Sonnet 5 | $0.00023 | $0.00777 |
| Haiku 4.5 | $0.00012 | $0.00388 |
Grade A, and why
dovetail 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 5d 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.
This is a copy
77% identical to 2-chat — 265 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dovetail through One
Dovetail is an AI-native customer intelligence platform that centralises and synthesises feedback from interviews, support tickets, reviews and research data—helping teams uncover themes, drive product decisions and act on voice-of-customer in one unified workspace.
One exposes Dovetail through four MCP tools. The table below carries real action ids from One's knowledge base, so for a common operation you can skip search and go straight to reading the action's parameters.
How to run an action
- Find the action in the table below, or call
search_one_platform_actionswith platformdovetailif it is not listed. - Call
get_one_action_knowledgewith the action id. Do this every time, including for actions in this table. The table gives you the id, not the parameters. - Call
execute_one_actionwith parameters copied from that knowledge.
Never guess a parameter name, a body field, or an enum value. The knowledge has the real schema, and a guessed field is either a 400 or a silent write of the wrong thing.
Before you start
Call list_one_integrations once and confirm Dovetail is connected. If it is missing, the user has not connected it: say so and point them at https://app.withone.ai rather than reaching for raw HTTP.
Each connection carries an access field. If it reports {"policy": "methods", "methods": ["GET"]} the agent is read-only here, so plan a read-only answer instead of attempting a write that will be refused.
Before a write
Creates, updates, deletes and sends land on a real Dovetail account and cannot be recalled. State the action and the specific target in one line before the first write in a task, and let the user stop you. Reads need no confirmation.
Actions
Docs
| Action | Method | Path | Action id |
|---|---|---|---|
| Export a Doc (HTML or Markdown) | GET | /v1/docs/{{docId}}/export/{{type}} |
conn_mod_def::GJ2X-BjqX6o::Df6CK7_XQtmG4jPLcbMtMA |
| Get a Doc by ID | GET | /v1/docs/{{docId}} |
conn_mod_def::GJ2X-I0CIp0::Rrp4TaWJRzqq9YgmRuWXsw |
| List a User's Personal Docs | GET | /v1/docs/user/{{userId}} |
conn_mod_def::GJ2X-mmSI1Q::C7YLm9aHSYOF9NJuQbNruA |
| List Docs | GET | /docs |
conn_mod_def::GJ2X-YoQSEQ::eywrqa5ATUSjsFFl-HGDhw |
| Create a Doc | POST | /docs |
conn_mod_def::GJ2X9sUVdsg::g6rk5k9vSsCIbWlk-zUt6Q |
| Delete a Doc | DELETE | /docs/{{docId}} |
conn_mod_def::GJ2X95tPBnY::FJiqOkCsRCacKn5iJ-zvog |
| Import File to Doc | POST | /docs/import/file |
conn_mod_def::GJ2X-QgTttM::Kktx9UemTmW67RdUr-gonQ |
| Update a Doc | PATCH | /v1/docs/{{docId}} |
conn_mod_def::GJ2X-vPJyIE::r_BmWggkSt65Y30-k_uBfA |
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.
- 5d ago First seen · 188 lines · 0 tokens per session scan A 3643fee3197a
dovetail is a skill published in the GitHub repository withoneai/one-agent-plugin (1 stars, last pushed 19d ago), licensed MIT. It adds 116 tokens to every session and 3,884 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 77% identical to 2-chat, differing in 265 lines, and is treated as a copy.
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