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/allisoneer/agentic_auxilaryWrote 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/commands/allisoneer/agentic_auxilary/decide_findings_openai)<a href="https://agentmods.dev/commands/allisoneer/agentic_auxilary/decide_findings_openai"><img src="https://agentmods.dev/badge/commands/allisoneer/agentic_auxilary/decide_findings_openai/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/commands/allisoneer/agentic_auxilary/decide_findings_openai"><img src="https://agentmods.dev/badge/commands/allisoneer/agentic_auxilary/decide_findings_openai.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 | $0.03339 |
| Opus 5 | $0.00010 | $0.01670 |
| Sonnet 5 | $0.00004 | $0.00668 |
| Haiku 4.5 | $0.00002 | $0.00334 |
Grade A, and why
decide_findings_openai 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 — 368 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This command keeps judgment and routing at the orchestrator level. Child sessions do bounded work only: research disputed areas, apply bounded cleanups, execute planning pipelines, or verify state.
The goal is to move every in-scope finding into one of these terminal states:
- resolved by bounded cleanup (verified, committed, pushed)
- resolved via plan + implementation (verified, committed, pushed)
- accepted as context (no action needed, rationale documented)
- explicitly deferred with rationale
- explicitly declined with rationale
- research-pending (investigation spawned, awaiting reclassification)
<workflow_contract>
- Follow all 9 steps in order.
- Use
todowriteand keep exactly one taskin_progressat a time. - Always normalize findings into an internal corpus before routing.
- Cluster related findings before choosing routes.
- Prefer
researchwhen evidence is weak, disputed, or blast radius is unclear. - Prefer
plan_and_implementwhen multi-file changes, design decisions, or test harness work are involved. - Any cleanup claiming a fix must happen only after verification (
just check+just test), commit, and push. - Update the source artifact with a backlink to the decision document after writing it.
- Refresh findings state after material progress before declaring completion.
- Do not use session-centric findings commands; this workflow replaces that pattern with orchestrator-driven resolution. </workflow_contract>
<step_1>
Step 1: Interpret Intent and Establish Autonomy Bounds
- Extract the source artifact path from
<userMessage>. If no path is provided, ask for one and stop. - Infer the requested autonomy level from
<userMessage>. - Support common modifiers such as:
cleanup only— only executecleanup_nowroutes, defer everything elseplanning focus— bias toward identifying work that needs planning rather than quick cleanupresearch first— spawn research for any finding with confidence gaps before routingdry run— route and document decisions but do not execute changeshigh severity only— process only high-severity findingsinclude low— include low-severity findings (normally skipped)- category filters like
only testing,only security, etc.
- If the user does not narrow scope, assume:
- all Medium+ severity findings are in scope
- autonomous routing is allowed
- code changes and commits are allowed
- Ask a clarification question only if you cannot determine the source artifact or if the user gave mutually exclusive instructions.
- Record the explicit limits for this run, especially whether code changes and commits are allowed.
</step_1>
<step_2>
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 First seen · 368 lines · 19 tokens per session scan A 343673067dcc
decide_findings_openai is a command published in the GitHub repository allisoneer/agentic_auxilary (77 stars, last pushed 2d ago), licensed MIT. It adds 19 tokens to every session and 3,339 once invoked, about $0.0001 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 2026-09-08.
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