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 conn-castle/agent-layer --skill dogfoodgit clone --depth 1 https://github.com/conn-castle/agent-layerWrote 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/conn-castle/agent-layer/dogfood)<a href="https://agentmods.dev/skills/conn-castle/agent-layer/dogfood"><img src="https://agentmods.dev/badge/skills/conn-castle/agent-layer/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/conn-castle/agent-layer/dogfood"><img src="https://agentmods.dev/badge/skills/conn-castle/agent-layer/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 | $0.00045 | $0.01502 |
| Opus 5.5 | $0.00018 | $0.00601 |
| Sonnet 5.5 | $0.00009 | $0.00300 |
| Haiku 4.5 | $0.00005 | $0.00150 |
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 today.
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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dogfood Agent Layer
Exercise the product through its public interfaces and inspect the resulting
state. Pursue useful real tasks, invent workflows, combine features, and follow
surprises. Look for confusing or missing capabilities as well as errors.
Use the current request to determine discovery, fix, and shipping scope.
Create disposable projects outside existing Git/Agent Layer roots, normally under
/var/tmp/; use nested projects only for deliberate discovery cases. Keep the
batch binary, harnesses, and evidence outside every cleanup/deletion target in a
unique retained directory under .agent-layer/tmp/ or /var/tmp/.
When selecting coverage, read open
GitHub issues and relevant
docs/agent-layer/DECISIONS.md entries. Use the scenario menu
for ideas and known fragile boundaries; choose experiments beyond it too.
Consult the source campaign only when tracing a
lesson's provenance; its logs and historical approvals are not execution inputs.
Candidate and workspace
- Build one fixed source snapshot per discovery batch and copy the binary into the retained batch directory so later development builds cannot replace it. Record its commit, uncommitted changes, build command, binary path/SHA-256, embedded version/commit, platform, and relevant provider versions. Give implementation workers separate checkouts.
- Invoke the candidate by absolute path and verify nested launchers and MCP
servers use it. Follow
docs/DEVELOPMENT.mdfor scratch source selection: setAL_DEV_BYPASS_VERSION_DISPATCH=1,AL_DEV_EXECUTABLEto the retained candidate, andAL_DEV_SOURCE_ROOTto each scratch project's own configuration root. Pins and PATH lookup can otherwise select an older release. Leave development overrides unset when testing real version dispatch. - Reconnect owned test MCP servers after rebuilding: a running server retains its loaded code. Check the chat's orchestration-server identity separately before attributing its failures to the candidate.
- For cases without real-provider inference, use local bare Git remotes, dummy
secrets, fake providers/local MCP servers, and child-only HOME/XDG/TMPDIR/Git
configuration. Preserve unrelated files and processes with sentinels. Use
ordinary disk storage for persistence cases; tmpfs misses fsync/publication
behavior.
AL_NO_NETWORK=1controls relevant Agent Layer downloads, not arbitrary network access. - Run authorized native-provider calls separately in disposable projects with existing provider homes/sign-ins; do not copy credentials into fixtures. Record effective provider/model/effort and overrides. A substituted provider covers only that provider.
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.
- today First seen · 132 lines · 45 tokens per session scan A e80fcfd3e8ca
dogfood is a skill published in the GitHub repository conn-castle/agent-layer (10 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 1,502 once invoked, about $0.0002 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 2026-10-21.
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