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-rule-engine-architectgit 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-rule-engine-architect)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-rule-engine-architect"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-rule-engine-architect/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-rule-engine-architect"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-rule-engine-architect.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 | $0.00051 | $0.00746 |
| Opus 5 | $0.00026 | $0.00373 |
| Sonnet 5 | $0.00010 | $0.00149 |
| Haiku 4.5 | $0.00005 | $0.00075 |
Grade A, and why
orchestrate-rule-engine-architect 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate: Rule Engine Architect
Evidence tier: first-hand build (August 2026). Grounded in a completed Orchestrate submission that was audited to destruction — 48 logged defects, 9 measured-and-rejected optimisations, 17 certification scripts. Every number below was measured on that system. Nothing here claims access to HackerRank's internal scoring.
When a rule engine beats an LLM classifier
Three conditions, all measurable before you commit:
- The targets are templated. In the real build, 30 labeled rows contained only 24 distinct reason strings, several repeating verbatim. Ground-truth reasons state which rule fired. A rule engine reproduces that exactly; a generative model approximates it.
- The dataset attacks LLM routers. One labeled row was a prompt-injection attack
whose correct label was
mute/scam. A rule engine is structurally immune — message text never enters a decision-making prompt. - The safety-critical signals are structured data, not prose: verification flags, opt-out state, sender role, dismissal history.
The model layer earns its place only where structured data genuinely cannot reach — reading a poster, hearing a voice note.
Tier ordering is a policy statement
SAFETY ▶ RELATIONSHIP / URGENCY ▶ ENGAGEMENT ▶ DEFAULT
Safety sits above engagement because the spec says risk is muted "regardless of the user's usual engagement." Ordering encodes that sentence. Write the reason in a comment next to the tier.
Two rule-level failures worth knowing
Shadowing. A rule whose condition is implied by an earlier rule's condition can
never fire. Real example: a rule requiring A and B and not C sat below a rule
requiring only B. Strictly unreachable — proven by implication, not sampling. It was
deleted; the output hash was unchanged, which is the proof it was dead.
Contradiction. Two rules encoding opposite policies for the same situation. Real
example: one rule routed a distress message from a known contact to notify, while
another correctly excluded distress-plus-payment-request as a scam pattern. Patching
the first would have made it dead code, so it was deleted.
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 · 67 lines · 51 tokens per session scan A fdefe50daed5
orchestrate-rule-engine-architect is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (3 stars, last pushed 29d ago), licensed MIT. It adds 51 tokens to every session and 746 once invoked, about $0.0003 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.
Other skills, from other repositories
general
Handle everyday conversation, answer questions, manage files, take notes, run scripts, and maintain persistent memory across sessions. Use when the user asks a general question, requests file operations, wants to brainstorm ideas, needs to-do tracking, asks you to remember something, or requests skill search and…
multi-bot
Coordinates responses between multiple GolemBot instances in a shared fleet. Use when the bot operates in a group chat with other bots, needs to decide whether to respond or pass, or must call a peer bot's API to fetch cross-domain data.
kb-guide
Search, read, create, and update knowledge base entries via MCP-connected KB tools. Use when the user asks to look up documentation, find existing articles, check if docs exist on a topic, create a new KB entry, update an existing document, or when domain questions should be answered from the knowledge base first.
ops
Content operations assistant — drafts blog posts, social media copy, and marketing materials, compiles data briefings, and tracks competitor activity. Use when the user asks to write a blog post, draft social media content, create marketing copy, generate a weekly report, compile operational metrics, update the…
escalation
Escalate unresolvable or sensitive requests to a human agent by recording an escalation entry. Use when the user asks to speak to a human, the bot cannot answer confidently, the request involves financial, legal, or security concerns, a safety issue is detected, or the user is frustrated after repeated failures.
code-review
Reviews code changes, pull requests, and diffs for correctness, security, performance, and style. Use when the user submits a PR for review, asks to review a diff or code snippet, or requests a quality check on recent changes.