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-prompt-engineeringgit 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-prompt-engineering)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-prompt-engineering"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-prompt-engineering/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-prompt-engineering"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-prompt-engineering.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 | $0.00088 | $0.00674 |
| Opus 5 | $0.00044 | $0.00337 |
| Sonnet 5 | $0.00018 | $0.00135 |
| Haiku 4.5 | $0.00009 | $0.00067 |
Grade A, and why
orchestrate-prompt-engineering 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 — 29 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate: Prompt Engineering as a Reviewed Artifact
Direct evidence: HackerRank's guidance states this directly — "Write prompts with same care as code—specify allowed outputs, required evidence, format requirements." This sits under "Code Quality," meaning prompts are implicitly graded as part of that 30%, not treated as separate from "real" engineering.
What "same care as code" concretely means
A prompt written with code-level rigor has the same properties good code has:
- Explicit allowed outputs. Not "classify the ticket" but "classify as exactly one of:
product_issue,feature_request,bug,invalid— no other values." This is the prompt-side half oforchestrate-schema-guardrails's validation-side guardrail; the two should agree exactly. - Required evidence, stated as a constraint, not a hope. "Ground your response only in the provided corpus documents; if no relevant document exists, say so explicitly rather than answering from general knowledge" — directly enforcing the "must use only the provided support corpus" hard constraint and preventing hallucinated policy citations, which the starter repo names as something to avoid.
- Format requirements stated precisely. If you need JSON, specify the exact keys and types, not "respond in JSON." If you need a justification under a certain length, say so — vague format instructions produce vague, inconsistently-parseable output.
Treat prompts as versioned, readable files — not inline strings
Per orchestrate-naming-and-structure, prompts belong in their own module (prompts.py, prompts/*.txt), not embedded as multi-line strings buried inside the agent loop's control flow. A reviewer — human or interviewer — should be able to open one file and read every prompt the system uses, the same way they'd read your validation logic.
The review test
Read your own prompt back as if you were a stranger with no context. Could you, from the prompt text alone, write the validator that checks its output? If the prompt says "classify appropriately" and your validator separately enforces a strict enum, there's a mismatch — the prompt is under-specifying what the validator over-specifies, and the gap is exactly where the model will produce output your guardrails have to catch (and sometimes won't).
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 · 29 lines · 88 tokens per session scan A f8a34c3e4b78
orchestrate-prompt-engineering is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (3 stars, last pushed 29d ago), licensed MIT. It adds 88 tokens to every session and 674 once invoked, about $0.0004 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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