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 agents/homenshum/nodebenchai/agent_run_verdict_workflowgit clone --depth 1 https://github.com/HomenShum/NodeBenchAIWhat 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 | $0.00000 | $0.01525 |
| Opus 5 | $0.00000 | $0.00763 |
| Sonnet 5 | $0.00000 | $0.00305 |
| Haiku 4.5 | $0.00000 | $0.00153 |
Grade A, and why
AGENT_RUN_VERDICT_WORKFLOW 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Run Verdict Workflow
Goal
Make every in-app agent run capable of producing a final operator-facing verdict with open-source citations, trace-backed evidence, and explicit next actions, all surfaced through the existing UI.
This is the standard workflow for any agent-facing slice that touches:
- contract + data model wiring
- backend issue context enrichment
- frontend live workflow panels
- verdict exactness UI surfacing
- tests + verification
This extends the existing NodeBench harness. Do not build a second orchestration stack, second trace system, or parallel review UI.
Operating stance
- Direct yourself as if you were the product owner and reliability lead.
- Do not stop after a backend patch if the UI cannot expose the result.
- Do not stop after UI polish if the verdict is not defensible from stored evidence.
- Do not stop after a “looks good” pass if deterministic tests and verification are missing.
Workflow contract
1. Contract + data model wiring
Start at the contract layer.
Define or confirm:
- the user-facing job contract
- the stored data contract
- the verdict contract
- the UI contract
For agent-task substrate work, prefer the existing tables and query surfaces:
agentTaskSessionsagentTaskTracesagentTaskSpanstoolApprovals- existing
sourceRefs,goalId,visionSnapshot,successCriteria,crossCheckStatus,deltaFromVision
If the feature needs new session output, first ask:
- Can this be derived from existing session or trace metadata?
- Can the UI query synthesize it without adding new persistence?
- If persistence is required, is the new field part of the canonical session contract?
Rules:
- Prefer derived proof packs over duplicate storage.
- Keep verdict enums explicit and bounded.
- Keep source references structured, not embedded in prose only.
- Use exact validators and exact return contracts in Convex.
Minimum verdict shape:
verdictverdictLabelsummaryconfidenceevidenceCountcitationCountverificationCountsapprovalCountskeyFindingsopenIssuesnextActionstopSourceRefs
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 · 205 lines · 0 tokens per session scan A ee73b446d3b6
AGENT_RUN_VERDICT_WORKFLOW is an agent published in the GitHub repository HomenShum/NodeBenchAI (14 stars, last pushed 19d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,525 tokens. 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 2026-08-30.
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