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 skills add NITISH-R-G/hackerrank-orchestrate-skills --skill orchestrate-cost-and-ops-metricsgit clone --depth 1 https://github.com/NITISH-R-G/hackerrank-orchestrate-skillsWrote 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/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-cost-and-ops-metrics)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-cost-and-ops-metrics"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-cost-and-ops-metrics/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/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-cost-and-ops-metrics"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-cost-and-ops-metrics.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 | $0.00097 | $0.00679 |
| Opus 5 | $0.00048 | $0.00340 |
| Sonnet 5 | $0.00019 | $0.00136 |
| Haiku 4.5 | $0.00010 | $0.00068 |
Grade A, and why
orchestrate-cost-and-ops-metrics 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 12d 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 — 30 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate: Cost and Operational Metrics
Direct evidence: the multi-modal-review (June) challenge's evaluation criteria explicitly list "operational metrics: model calls, token usage, image usage, cost estimates, runtime, and TPM/RPM considerations" as mandatory analysis. This reflects a broader signal from HackerRank's stated philosophy — evaluating whether a candidate/agent-builder thinks like someone who has to actually operate the system, not just get it to work once.
What to instrument, concretely
- Model call count: total calls made across the full run, broken down by purpose (classification calls vs. retrieval calls vs. validation-retry calls) if your architecture has distinct stages.
- Token usage: input and output tokens, ideally per-call-type, summed for the full dataset run.
- Image usage (multi-modal challenge specifically): how many images were sent to a vision model, at what resolution/size, since this is often the dominant cost driver in multi-modal pipelines.
- Cost estimate: token/image counts × the provider's published per-unit pricing, presented as a real dollar figure for the full run — not just "it uses tokens."
- Runtime: wall-clock time for the full dataset, and whether that's dominated by model latency, retrieval, or something else.
- TPM/RPM considerations: whether your call pattern would hit a provider's tokens-per-minute or requests-per-minute limit at production scale, and what you'd do about it (batching, backoff, a different model tier).
Why this belongs in a hackathon submission, not just a production system
This is the clearest evidence in the entire published record that HackerRank is explicitly grading production-mindedness, not just "does it work in the demo." A submission that produces a correct output.csv with zero visibility into what it cost to produce demonstrates exactly the gap the organizers' broader philosophy pieces describe — treating AI output as a black box to be accepted rather than something to evaluate and reason about operationally.
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.
- 12d ago First seen · 30 lines · 97 tokens per session scan A 3dfe9640d594
orchestrate-cost-and-ops-metrics is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (3 stars, last pushed 1mo ago), licensed MIT. It adds 97 tokens to every session and 679 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 2026-08-31.
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