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/homenshum/nodebenchai/pipeline-operational-loopnpx skills add HomenShum/NodeBenchAI --skill pipeline-operational-loopgit clone --depth 1 https://github.com/HomenShum/NodeBenchAIWrote 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/homenshum/nodebenchai/pipeline-operational-loop)<a href="https://agentmods.dev/skills/homenshum/nodebenchai/pipeline-operational-loop"><img src="https://agentmods.dev/badge/skills/homenshum/nodebenchai/pipeline-operational-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 | $0.00099 | $0.01035 |
| Opus 5 | $0.00049 | $0.00517 |
| Sonnet 5 | $0.00020 | $0.00207 |
| Haiku 4.5 | $0.00010 | $0.00103 |
Grade A, and why
pipeline-operational-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 4d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pipeline Operational Loop
Standard template for evaluating any change to the real-time chat pipeline or the report generator pipeline.
Canonical spec: docs/architecture/PIPELINE_OPERATIONAL_STANDARD.md
Rule mirror: .claude/rules/pipeline_operational_standard.md
Step 0 — Diagnose first (analyst mindset)
Before writing code, answer:
- Which pipeline layer changed? (args, emit, judge, persistence, UI, dashboard)
- Does it change the contract (EmitProjectionArgs or RunTelemetry)?
- Does it change a judge gate? (if yes → dashboards depend on
GATE_ORDER) - What's the failure mode this change prevents or creates?
Step 1 — Contract
- Update
EmitProjectionArgs(server/pipeline/diligenceProjectionWriter.ts) if needed. - Mirror the shape in
convex/schema.ts→diligenceProjections/diligenceRunTelemetry. - Mirror in Convex args validators (
recordTelemetry,recordVerdict).
Step 2 — Instrument
Every emit goes through emitDiligenceProjectionInstrumented(...):
await emitDiligenceProjectionInstrumented(mutationCaller, args, {
seedTelemetry: { toolCalls, tokensIn, tokensOut, sourceCount },
onTelemetry: async (t, a) => {
const { id } = await convex.mutation(api.domains.product.diligenceRunTelemetry.recordTelemetry, { ...mapTelemetry(a, t) });
const verdict = judgeDiligenceRun({ args: a, telemetry: t });
await convex.mutation(api.domains.product.diligenceJudge.recordVerdict, {
telemetryId: id,
...verdict,
gatesJson: JSON.stringify(verdict.gates),
});
},
});
Step 3 — Judge
judgeDiligenceRun is a pure deterministic function. Don't call LLMs here.
Don't read wall-clock. Don't randomize.
Add a new gate only by:
- Extending the
GateNameunion inserver/pipeline/diligenceJudge.ts. - Appending to
GATE_ORDER(never reshuffle — dashboards depend on it). - Adding the evaluator fn.
- Adding a row to §3 of
PIPELINE_OPERATIONAL_STANDARD.md. - Adding scenario tests in
diligenceJudge.test.tscovering pass, fail, and skip.
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.
- 4d ago First seen · 107 lines · 99 tokens per session scan A 637e28d6343a
pipeline-operational-loop is a skill published in the GitHub repository HomenShum/NodeBenchAI (14 stars, last pushed 20d ago), licensed MIT. It adds 99 tokens to every session and 1,035 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 2026-08-30.
Other skills, from other repositories
convex-create-component
Designs and builds Convex components with isolated tables, clear boundaries, and app-facing wrappers. Use this skill when creating a new Convex component, extracting reusable backend logic into a component, building a third-party integration that owns its own tables, packaging Convex functionality for reuse, or when…
convex-quickstart
Initializes a new Convex project from scratch or adds Convex to an existing app. Use this skill when starting a new project with Convex, scaffolding with npm create convex@latest, adding Convex to an existing React, Next.js, Vue, Svelte, or other frontend, wiring up ConvexProvider, configuring environment variables…
convex-performance-audit
Audits and optimizes Convex application performance across hot-path reads, write contention, subscription cost, and function limits. Use this skill when a Convex feature is slow or expensive, npx convex insights shows high bytes or documents read, OCC conflict errors or mutation retries appear, subscriptions or UI…
convex-setup-auth
Sets up Convex authentication with user management, identity mapping, and access control. Use this skill when adding login or signup to a Convex app, configuring Convex Auth, Clerk, WorkOS AuthKit, Auth0, or custom JWT providers, wiring auth.config.ts, protecting queries and mutations with ctx.auth.getUserIdentity()…
convex-migration-helper
Plans and executes safe Convex schema and data migrations using the widen-migrate-narrow workflow and the @convex-dev/migrations component. Use this skill when a deployment fails schema validation, existing documents need backfilling, fields need adding or removing or changing type, tables need splitting or merging…
robel-auth
Integrate and maintain Robelest Convex Auth in apps by always checking upstream before implementation. Use when adding auth setup, updating auth wiring, migrating between upstream patterns, or troubleshooting @robelest/convex-auth behavior across projects.