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-multi-strategy-evaluationgit 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-multi-strategy-evaluation)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-multi-strategy-evaluation"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-multi-strategy-evaluation/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-multi-strategy-evaluation"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-multi-strategy-evaluation.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 | $0.00730 |
| Opus 5 | $0.00048 | $0.00365 |
| Sonnet 5 | $0.00019 | $0.00146 |
| Haiku 4.5 | $0.00010 | $0.00073 |
Grade A, and why
orchestrate-multi-strategy-evaluation 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 10d 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 — 28 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate: Multi-Strategy Evaluation
Direct evidence: the multi-modal-review (June) challenge's evaluation criteria explicitly require "comparison of ≥2 strategies/prompts/configurations" and "final approach documentation" as mandatory analysis — not optional polish. This is a formalized, graded version of ordinary good engineering practice: don't ship your first idea without checking whether a second one does better.
What this looks like in practice
- Build against the sample/dev dataset, not the golden dataset you don't have. Every Orchestrate challenge ships a
sample_*.csvwith known expected outputs specifically for this purpose. - Implement at least two genuinely different approaches to some meaningful part of the system — not two trivial variations. Examples: a single-call classification prompt vs. a two-step "extract evidence, then classify" pipeline; keyword-based corpus retrieval vs. embedding-based retrieval; a strict rule-based escalation policy vs. a model-judged one.
- Score both against the sample set using the same metric (accuracy against known labels, or a proxy metric if labels are qualitative) and record the numbers, not just an impression.
- Document why you chose what you chose — including what the losing approach got wrong, specifically. "Approach B mis-classified 3 of 20 sample tickets because it conflated
bugandproduct_issuewhen a ticket mentioned an error message without describing a workflow" is evidence. "Approach A seemed to work better" is not.
Why this matters even for challenges that don't explicitly require it
The interview is designed to probe exactly this kind of comparative reasoning — HackerRank's own interview-prep guidance says to be ready to discuss "what you tested and what limitations remain." An answer of "I tried the first thing that came to mind and it worked" is a materially weaker interview answer than "I tried two approaches, here's what the sample data showed about each, here's why I picked the one I did, and here's the specific failure mode the other one had that mine still shares." The second answer demonstrates process — the exact thing HackerRank's stated philosophy says it's now measuring instead of "did you get the right answer."
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.
- 10d ago First seen · 28 lines · 97 tokens per session scan A 34cd5b703d42
orchestrate-multi-strategy-evaluation is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (3 stars, last pushed 29d ago), licensed MIT. It adds 97 tokens to every session and 730 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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