Dograh is an open-source, self-hostable platform for building voice AI agents that combine speech recognition, language models, and speech synthesis. Teams use its visual workflow builder and telephony integrations to create and operate voice agents while choosing their own providers and infrastructure.
Borrowing it
Nothing to install: this file belongs to dograh-hq/dograh. 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/dograh-hq/dograh/main/.agents/skills/review-agents-md/SKILL.mdgit clone --depth 1 https://github.com/dograh-hq/dograhWrote 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/dograh-hq/dograh/review-agents-md)<a href="https://agentmods.dev/skills/dograh-hq/dograh/review-agents-md"><img src="https://agentmods.dev/badge/skills/dograh-hq/dograh/review-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/skills/dograh-hq/dograh/review-agents-md"><img src="https://agentmods.dev/badge/skills/dograh-hq/dograh/review-agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00082 | $0.02170 |
| Opus 5 | $0.00041 | $0.01085 |
| Sonnet 5 | $0.00016 | $0.00434 |
| Haiku 4.5 | $0.00008 | $0.00217 |
Grade A, and why
review-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 12d 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 — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review AGENTS.md
Audit first. Report drift, missing coverage, and wrong ownership boundaries before editing docs unless the user explicitly asks for patches.
Freshness Rule
Treat the repo as source of truth.
- Trust current code and current directory layout over any
AGENTS.md,README.md, or this skill's references. - If prose and code disagree, report the prose as stale.
- If a reference file in this skill disagrees with the repo, trust the repo and mention the drift.
Workflow
0. Refresh the seam reference before using it
If subagents are available, refresh references/dograh-seams.md before relying on it.
- Spawn exactly one subagent for this maintenance pass.
- Tell the subagent to inspect the live repo.
- Limit its patch set to
.agents/skills/review-agents-md/references/dograh-seams.md. - Also allow
.agents/skills/review-agents-md/scripts/inventory_agents_md.py, but only if the helper itself needs a repo-specific fix. - Tell the subagent not to recurse into this same seam-refresh workflow. This is a one-level maintenance pass, not an infinite self-audit loop.
- Tell the subagent not to review or patch any repo
AGENTS.mdfiles yet. Its job is only to refresh the seam reference and helper. - After the subagent returns, review its diff quickly before using
dograh-seams.mdin the main audit.
Use a prompt shaped like:
Review and refresh .agents/skills/review-agents-md/references/dograh-seams.md against the live Dograh repo. Patch only that file, and patch .agents/skills/review-agents-md/scripts/inventory_agents_md.py only if needed. Do not recurse into another seam-refresh pass. Do not review or edit any AGENTS.md files yet.
If subagents are not available, do the same seam refresh locally before continuing.
1. Inventory the current hierarchy
Run the helper first from the repo root:
python .agents/skills/review-agents-md/scripts/inventory_agents_md.py
This prints:
- every discovered
AGENTS.md - child
AGENTS.mdownership boundaries - immediate child directories for each scope
- large uncovered subtrees that may deserve their own
AGENTS.md
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 207 lines · 82 tokens per session scan A 5f75fadca3f3
review-agents-md is a skill published in the GitHub repository dograh-hq/dograh (5,621 stars, last pushed yesterday), licensed BSD-2-Clause. It adds 82 tokens to every session and 2,170 once invoked, about $0.0004 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 2026-08-30.
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