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 graniet/kheish --skill dogfoodgit clone --depth 1 https://github.com/graniet/kheishWrote 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/graniet/kheish/dogfood)<a href="https://agentmods.dev/skills/graniet/kheish/dogfood"><img src="https://agentmods.dev/badge/skills/graniet/kheish/dogfood/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/graniet/kheish/dogfood"><img src="https://agentmods.dev/badge/skills/graniet/kheish/dogfood.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 | $0.00019 | $0.01574 |
| Opus 5 | $0.00010 | $0.00787 |
| Sonnet 5 | $0.00004 | $0.00315 |
| Haiku 4.5 | $0.00002 | $0.00157 |
Grade A, and why
dogfood 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 11d 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
86% identical to dogfood — 29 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal=>bashweb_extract=>web_fetch, plusweb_searchwhen discovery is neededsearch_files=>grep_searchandglob_searchbrowser_*tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
Dogfood: Systematic Web Application QA Testing
Overview
This skill guides you through systematic exploratory QA testing of web applications using the browser toolset. You will navigate the application, interact with elements, capture evidence of issues, and produce a structured bug report.
Prerequisites
- Browser toolset must be available (
browser_navigate,browser_snapshot,browser_click,browser_type,browser_vision,browser_console,browser_scroll,browser_back,browser_press) - A target URL and testing scope from the user
Inputs
The user provides:
- Target URL — the entry point for testing
- Scope — what areas/features to focus on (or "full site" for comprehensive testing)
- Output directory (optional) — where to save screenshots and the report (default:
./dogfood-output)
Workflow
Follow this 5-phase systematic workflow:
Phase 1: Plan
- Create the output directory structure:
{output_dir}/ ├── screenshots/ # Evidence screenshots └── report.md # Final report (generated in Phase 5) - Identify the testing scope based on user input.
- Build a rough sitemap by planning which pages and features to test:
- Landing/home page
- Navigation links (header, footer, sidebar)
- Key user flows (sign up, login, search, checkout, etc.)
- Forms and interactive elements
- Edge cases (empty states, error pages, 404s)
What ships with it
2 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.
- 11d ago First seen · 180 lines · 19 tokens per session scan A 1688e24ddd2d
dogfood is a skill published in the GitHub repository graniet/kheish (227 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 19 tokens to every session and 1,574 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to dogfood, differing in 29 lines, and is treated as a copy.
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