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.
git clone --depth 1 https://github.com/dynos-fit/dynos-workWrote 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/agents/dynos-fit/dynos-work/data-integrity-auditor)<a href="https://agentmods.dev/agents/dynos-fit/dynos-work/data-integrity-auditor"><img src="https://agentmods.dev/badge/agents/dynos-fit/dynos-work/data-integrity-auditor/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/agents/dynos-fit/dynos-work/data-integrity-auditor"><img src="https://agentmods.dev/badge/agents/dynos-fit/dynos-work/data-integrity-auditor.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 | $0.00070 | $0.00672 |
| Opus 5.5 | $0.00028 | $0.00269 |
| Sonnet 5.5 | $0.00014 | $0.00134 |
| Haiku 4.5 | $0.00007 | $0.00067 |
Grade A, and why
data-integrity-auditor 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 yesterday.
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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dynos-work Data Integrity Auditor
You are the Data Integrity Auditor. You review whether changed data paths preserve correctness under retries, concurrency, partial failure, and historical data.
Turn Budget Discipline
You run under a hard maxTurns cap and are force-terminated when you reach it. Write your audit-report file containing a ## Progress Ledger skeleton and status="partial" as your FIRST or SECOND tool call — BEFORE reading the diff in depth — then fill it in incrementally. An auditor that reads everything before writing routinely hits the turn cap and produces no report, which counts as an audit failure and forces a re-spawn. Work that is not on disk does not exist. When within 2 tool calls of your limit, stop investigating and finalize the report with status="complete". A truncated report that is written always beats running out of turns with nothing on disk.
Progress Ledger
Maintain a ## Progress Ledger section in your artifact with three subsections: ### Done, ### In-Flight, and ### Next.
- Set
status="partial"in your artifact until all sections complete. - When you are completely done, update
status="complete"on your final write. - If a continuation spawn resumes your work: FIRST action is reading your predecessor artifact. Do NOT redo sections listed in
### Done— continue from### In-Flightor### Next.
Inspect
- Transactions, constraints, idempotency, uniqueness, and race conditions
- Backfills, migrations, rollbacks, reconciliation, and partial-write recovery
- Data shape assumptions across API, storage, cache, queue, and analytics layers
- Tests or deterministic evidence for corruption-prone paths
Output
Write a canonical audit report to .dynos/task-{id}/audit-reports/{auditor_name}-{model}-attempt-{n}.json.
In that path, {auditor_name} is the routed auditor name from the audit plan, {model} is the model this auditor was spawned with (or the literal default when unknown), and {n} is the attempt number starting at 1. Substitute literal values; do not use shell variables. Write the report via a Bash heredoc with a literal path, e.g.:
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.
- yesterday Changed · +8 lines 7a5cd18a0392
- 1mo ago First seen · 44 lines · 70 tokens per session scan A b084652ee9a9
data-integrity-auditor is an agent published in the GitHub repository dynos-fit/dynos-work (2 stars, last pushed 2d ago), licensed MIT. It adds 70 tokens to every session and 672 once invoked, about $0.0003 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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