Borrowing it
Nothing to install: this file belongs to dbhq-uk/dovetail-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dbhq-uk/dovetail-skill/main/AGENTS.mdgit clone --depth 1 https://github.com/dbhq-uk/dovetail-skillWrote 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/instructions/dbhq-uk/dovetail-skill/agents-md)<a href="https://agentmods.dev/instructions/dbhq-uk/dovetail-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/dbhq-uk/dovetail-skill/agents-md/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/instructions/dbhq-uk/dovetail-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/dbhq-uk/dovetail-skill/agents-md.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 | $0.02009 | $0.02009 |
| Opus 5.5 | $0.00804 | $0.00804 |
| Sonnet 5.5 | $0.00402 | $0.00402 |
| Haiku 4.5 | $0.00201 | $0.00201 |
Grade A, and why
dovetail-skill AGENTS.md 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.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Guidance for AI agents (and people) working in this repository.
What this is
dovetail - a repository coherence checker for AI coding agents. It follows the Agent Skills layout (skills/<name>/SKILL.md) and ships as a Claude Code plugin.
Layout
.claude-plugin/plugin.json # plugin manifest
skills/dovetail/SKILL.md # the skill: the conversation rules and the verbs it calls
skills/dovetail/scripts/ # python, standard library only; dovetail.py drives a run
skills/dovetail/references/ # the finding contract and one rubric per reviewer
skills/dovetail/ci/ # workflow templates users copy into their own repo
skills/dovetail/tests/ # offline, no model calls
install.sh / install-codex.sh # local symlink installers (Claude / Codex)
docs/design-notes.md # why the tool is shaped this way
The three layers
Layer 1, deterministic - scan.py runs
the graph queries, the exact checks, the convention checks, the git-history
signals and any repo-local plugins. Seconds, no model, no network.
Layer 2, judgement - reviewer.py
holds the roster, tiering and the shared validator;
claimscan.py narrows contradiction
candidates into clusters. Reviewers run as in-session subagents interactively,
one per shard, or through ci_dispatch.py
for the scheduled job. Both paths take their shards from ci_dispatch.plan_shards,
so they cannot shard the same repository differently. Rubrics live in skills/dovetail/references/reviewers/, loaded by name
at dispatch time.
Layer 3, triage - the conversation is in SKILL.md;
the state is in dovetail.py. No Python
TUI: findings are rendered into the session as markdown, which makes the loop a
conversation that can be interrupted and questioned rather than a modal
application. But the agent never reads the scan's JSON or the clusters: on a
repository of about 900 files they fill most of its context before the first
question. dovetail.py keeps the run on disk and each verb (scan, next,
decide, check, rescan, prepare-review, wave, import-review) prints
only what the next step needs. Every value reaches it as an argument, never
pasted into code.
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 Changed · +16 lines · +632 tokens per session 7e44c14ff811
- 2mo ago First seen · 87 lines · 1,377 tokens per session scan A 94a9b69303a4
dovetail-skill AGENTS.md is an instructions file published in the GitHub repository dbhq-uk/dovetail-skill (3 stars, last pushed 4d ago), licensed MIT. It adds 2,009 tokens to every session, about $0.0080 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 2026-08-31.
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