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-interviewgit 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-interview)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-interview"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-interview/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-interview"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-interview.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 | $0.00061 | $0.01093 |
| Opus 5.5 | $0.00024 | $0.00437 |
| Sonnet 5.5 | $0.00012 | $0.00219 |
| Haiku 4.5 | $0.00006 | $0.00109 |
Grade A, and why
orchestrate-interview 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview Preparation
Scored by: technical depth and ownership 40%, problem understanding and judgment 25%, communication clarity 20%, honesty and self-awareness 15%.
The judge's past criticism was about owning details: "knowing your own constants." It did not challenge architecture, methodology, AI workflow, or honesty. Depth of ownership is the bar.
Step 1 - Constants drill (do first)
Fill references/CONSTANTS-template.md for every number in the code: thresholds, timeouts, regex bounds, retrieval parameters, confidence floor and ceiling, resize sizes, evidence counts. Label each MEASURED, SPEC, STANDARD, BOUND, or JUDGEMENT.
Rules:
- Never write "chosen after evaluation" for a value you did not sweep.
- For MEASURED values, have the losing alternative and its number ready.
- For STANDARD values, say plainly that you did not tune them, and why that is a deliberate choice.
Step 2 - Nine topics, one prepared answer each
Structure each answer as: claim, evidence or mechanism, the alternative you rejected, the limit.
- Architecture. One-sentence shape of the system, then where the model is called and where code decides.
- Deterministic vs LLM reasoning. Which decisions are code and why. Example mechanism: the model can only escalate a high-risk case; it can never downgrade one, because the deterministic gate runs first.
- Arbitration. How conflicting signals are resolved. Name the order of rules.
- End-to-end flow. One input traced from load to CSV row. Use the trace you wrote in
orchestrate-test-fix. - Retrieval. What you used and why, with one case where it mattered and one failure mode.
- Confidence calibration. What confidence means in your system, its source, and how you checked it.
- AI-assisted workflow. Where AI helped, what you rejected, what you checked yourself. Say "I decided" more than "the AI created".
- Trade-offs. One decision you would reverse, and why.
- Honesty. Three specific limits, what you did not test, and what you would improve next.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 67 lines · 61 tokens per session scan A 055827c9580d
orchestrate-interview is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (8 stars, last pushed 2d ago), licensed MIT. It adds 61 tokens to every session and 1,093 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-24.
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