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 agentmods add skills/malloydata/publisher/eval-loopnpx skills add malloydata/publisher --skill eval-loopgit clone --depth 1 https://github.com/malloydata/publisherWrote 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/malloydata/publisher/eval-loop)<a href="https://agentmods.dev/skills/malloydata/publisher/eval-loop"><img src="https://agentmods.dev/badge/skills/malloydata/publisher/eval-loop.svg" alt="Measured on agentmods" 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 | $0.00102 | $0.04315 |
| Opus 5 | $0.00051 | $0.02158 |
| Sonnet 5 | $0.00020 | $0.00863 |
| Haiku 4.5 | $0.00010 | $0.00432 |
Grade A, and why
eval-loop 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 2d 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 — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The Evaluation Loop
You conduct this loop. There is no batch orchestrator to start, no eval API,
and no eval MCP tools. The ledger is plain files in the model package's git
repository (reference/ledger-schema.md in skill:eval-answer defines every
file and event). Scoring is an LLM judge you spawn per case. There is no
scripted scorer, and there will not be one: a script that can pass a wrong
answer is worse than none. The scripts under scripts/ run the loop -- they
answer, re-execute, spawn the judge, compare runs, and write the ledger -- but
none of them decides whether an answer was right.
scrape/run -> eval -> diagnose -> improve -> checkpoint
This skill conducts; it does not restate. Scoring lives in
skill:eval-answer. Components and owners live in skill:eval-diagnose.
Edit rules live in skill:eval-improve.
Do not merge eval into diagnose. A conductor who scores while explaining writes the explanation into the score. Do not skip the acceptance check inside improve. The acceptance check decides whether this edit stays. Checkpoint decides whether a sequence of accepted edits can be undone.
Where the rest of this lives
This file is the procedure. Five things it used to carry inline are files beside it now, because each is needed at one moment rather than every run, and loading all of them for every run is how a skill stops being read.
| When | Read |
|---|---|
| about to run one | reference/running-a-run.md |
| a golden is wrong, doubted, or out of step with the model | reference/golden-side-door.md |
| deciding whether an edit stays | reference/acceptance-check.md |
| about to quote a number, or set the noise band | reference/measurement.md |
| you changed judge doctrine or its inputs | reference/checking-the-judge.md |
Read the file, do not work from the summary here. The acceptance-check rules and the golden side door are both places where acting on a half-memory of the rule produces a confident wrong answer rather than an error.
What ships with it
18 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.
- reference/acceptance-check.md 3.2 KB
- reference/checking-the-judge.md 1.5 KB
- reference/golden-side-door.md 7.0 KB
- reference/measurement.md 6.0 KB
- reference/running-a-run.md 4.4 KB
- scripts/agent_harness.py 18 KB runs code
- scripts/build_run_package.py 24 KB runs code
- scripts/check_judge.py 8.8 KB runs code
- scripts/flip_table.py 14 KB runs code
- scripts/platform_target_test.py 4.2 KB runs code
- scripts/run_baseline.py 75 KB runs code
- scripts/serve.py 5.5 KB runs code
- templates/eval-run-package/eval_run.malloy 30 KB
- templates/eval-run-package/eval_run.malloynb 5.9 KB
- templates/eval-run-package/public/app.js 20 KB runs code
- templates/eval-run-package/public/index.html 12 KB
- templates/eval-run-package/publisher.json 350 B
- templates/eval-run-package/README.md 8.5 KB
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.
- 2d ago First seen · 350 lines · 102 tokens per session scan A 0c9faa416e55
eval-loop is a skill published in the GitHub repository malloydata/publisher (100 stars, last pushed today), licensed MIT. It adds 102 tokens to every session and 4,315 once invoked, about $0.0005 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 2026-09-03.
Other skills, from other repositories
dbt-expert
Expert-level dbt (data build tool), models, tests, documentation, incremental models, macros, and Jinja templating. Use when the user mentions analytics engineering, SQL, data transformation, Jinja, or testing, or when the task involves Project Structure and Configuration, Sources and Staging Models, Intermediate and…
reusable-visualization
Build ONE reusable chart visualization component that receives its data and its settings from the host application instead of fetching them, and declares the fields and config options the host exposes to viewers. Use this whenever a single chart component is reused across many different queries rather than built for…
lightdash-agent-slack-messaging
Use this skill when writing, designing, or generating Slack messages for Lightdash's in-app analytics agent. Triggers when someone asks to create agent update messages, Slack digests, agent notifications, weekly summaries, daily summaries, or any Slack copy for the Lightdash project agent. Also use when asked to vary…
upgrade-preflight
Checks whether a self-hosted Lightdash upgrade is safe to run, and reads the tooling's answer without over-reading it. Use when upgrading a self-hosted instance, planning a maintenance window, answering "is this upgrade safe", or recovering a failed, hung, parked or lock-stuck migration — covers lightdash…
developing-in-lightdash
Use when reading, creating, and editing Lightdash dashboards and charts as JSON, including dashboard layout and chart-type-specific configuration.
developing-data-apps-locally
Use when editing a locally created or downloaded Lightdash data app — how local editing, building, and uploading work, and what is read-only.