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-escalation-designgit 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-escalation-design)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-escalation-design"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-escalation-design/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-escalation-design"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-escalation-design.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 | $0.00763 |
| Opus 5 | $0.00044 | $0.00381 |
| Sonnet 5 | $0.00018 | $0.00153 |
| Haiku 4.5 | $0.00009 | $0.00076 |
Grade A, and why
orchestrate-escalation-design 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 — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate: Escalation Design
Direct evidence: the support-agent challenge's starter repo states the hard requirement — "Must escalate high-risk, sensitive, or unsupported cases" instead of guessing. HackerRank's own description of the dataset confirms it's built with edge cases, prompt injection attempts, and jailbreaking tests specifically so that escalating everything and replying to everything both result in failure — meaning the graded signal lives almost entirely in a calibrated middle ground, not at either extreme.
Why this can't be an afterthought
If escalation is implemented as "if confidence < threshold, escalate" bolted onto an otherwise-complete classify-and-respond pipeline, the threshold gets tuned by guesswork against however the demo happens to run — not against a considered model of what kinds of cases actually warrant escalation. The published dataset is specifically designed to punish that approach from both directions: too eager to escalate loses points for cases that should have been resolved automatically; too eager to respond loses points (and, in the adversarial cases, potentially demonstrates a real safety failure) for cases that needed a human.
What "escalate deliberately" looks like as a design
Treat escalation as a genuine decision with named categories, not a catch-all:
- High-risk: cases where an incorrect automated response has real consequences (security, financial, legal-adjacent content)
- Sensitive: cases touching topics where automated handling is inappropriate regardless of confidence (this is where prompt-injection and jailbreak attempts should generally land — recognizing an adversarial pattern is itself a valid basis for escalation, not a failure to answer)
- Unsupported: the corpus genuinely doesn't contain grounding for a confident answer — this connects directly to
orchestrate-failure-handling's "mark uncertainty" discipline, applied specifically to knowledge-grounding gaps
Each category should have its own detection logic, not one shared confidence score covering all three — a case can be low-risk-but-unsupported (escalate for one reason) or high-risk-but-well-supported (escalate for a different reason entirely, regardless of confidence).
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 · 35 lines · 88 tokens per session scan A ed08075408ba
orchestrate-escalation-design is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (3 stars, last pushed 1mo ago), licensed MIT. It adds 88 tokens to every session and 763 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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ops
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