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-edge-case-testinggit 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-edge-case-testing)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-edge-case-testing"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-edge-case-testing/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-edge-case-testing"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-edge-case-testing.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 | $0.00654 |
| Opus 5 | $0.00044 | $0.00327 |
| Sonnet 5 | $0.00018 | $0.00131 |
| Haiku 4.5 | $0.00009 | $0.00065 |
Grade A, and why
orchestrate-edge-case-testing 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 9d 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: Edge Case Testing
Direct evidence: HackerRank's guidance names this precisely — "Incomplete testing: Don't only inspect successful cases; examine failures and edge cases" and "Inconsistent handling: Similar cases receiving different treatment without justification." The interview prep guidance separately says to "discuss edge cases explicitly: missing data, conflicting signals, distracting input instructions" — meaning this isn't just a code-quality concern, it's rehearsed material for the 30%-weighted interview too.
Two distinct testing disciplines this asks for
1. Inspect failures, not just successes. After a full run, don't just check "did most rows look plausible" — actively pull the rows where the agent escalated, marked uncertain, or hit a fallback path, and read them individually. These are disproportionately where bugs and bad judgment calls hide, and disproportionately where the golden dataset likely has deliberately hard cases (both challenge descriptions confirm curated edge cases and adversarial inputs are part of the test set).
2. Consistency-check similar cases. Take two tickets/claims that are similar in substance but phrased differently, or that hit the same underlying issue from different angles. Run both through the agent. If they get materially different treatment (one escalates, one doesn't; one cites evidence, one doesn't) with no substantive reason, that's a calibration bug — the kind an interviewer will find by asking "what about a case like X but slightly different?"
A concrete practice: build a consistency test set
Before submission, hand-pick 5-10 pairs of similar-but-not-identical cases from your own understanding of the domain (not from the golden dataset, which you don't have access to). Run the agent on each pair. For every pair that diverges, either:
- Confirm the divergence is justified (the cases actually differ in a way that matters) and note why — this becomes interview material
- Or treat it as a bug and fix the underlying logic
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.
- 9d ago First seen · 29 lines · 88 tokens per session scan A cb8b67478b51
orchestrate-edge-case-testing is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (3 stars, last pushed 28d ago), licensed MIT. It adds 88 tokens to every session and 654 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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