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-naming-and-structuregit 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-naming-and-structure)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-naming-and-structure"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-naming-and-structure/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-naming-and-structure"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-naming-and-structure.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 | $0.00096 | $0.00748 |
| Opus 5 | $0.00048 | $0.00374 |
| Sonnet 5 | $0.00019 | $0.00150 |
| Haiku 4.5 | $0.00010 | $0.00075 |
Grade A, and why
orchestrate-naming-and-structure 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate: Naming and Structure
Direct evidence: HackerRank's guidance is explicit — code "should be runnable and readable. Anyone should be able to open it, find the entry point, understand the main flow." Named mistakes: "Poor naming: Don't use generic filenames like 'helper' or 'utils'—use descriptive names reflecting file purpose." Named recommended practice: "Separate concerns clearly: input loading, prompts, agent logic, validation, evaluation."
The five concerns, kept separate
The organizer's own list is the checklist:
| Concern | What lives here | What doesn't |
|---|---|---|
| Input loading | Reading the corpus/CSVs, parsing tickets/claims into structured objects | Any decision-making logic |
| Prompts | The actual prompt templates, versioned and readable as text | Business logic that decides which prompt to use |
| Agent logic | The loop: decide next action, call tools, interpret results | Prompt text, validation rules |
| Validation | Schema checks, enum checks, the guardrail pattern from orchestrate-schema-guardrails |
Agent decision-making |
| Evaluation | Comparing output against sample_*.csv, computing metrics |
Production agent code |
A codebase where these five are tangled into one 400-line main.py fails this check even if it produces correct output — because "correct output" is only 30-60% of what's being scored (code quality is a separate 30%, and the interview probes architecture directly).
Naming: the concrete test
Open your file tree with no other context. For each file, ask: does the name alone tell you what's inside? helper.py, utils.js, main2.py, test_new.py all fail this test. ticket_classifier.py, corpus_retriever.py, output_validator.py pass it.
This isn't cosmetic. A judge or interviewer navigating your zip under time pressure (the interview is 30 minutes, covering your whole system) reads file names before file contents — bad names cost real evaluation time and read as a proxy for care taken elsewhere.
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 · 51 lines · 96 tokens per session scan A 65eb1ee17564
orchestrate-naming-and-structure is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (3 stars, last pushed 1mo ago), licensed MIT. It adds 96 tokens to every session and 748 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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