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-buildgit 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-build)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-build"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-build/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-build"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-build.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 | $0.00074 | $0.01113 |
| Opus 5.5 | $0.00030 | $0.00445 |
| Sonnet 5.5 | $0.00015 | $0.00223 |
| Haiku 4.5 | $0.00007 | $0.00111 |
Grade A, and why
orchestrate-build 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 yesterday.
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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build: Design, then Implement
Scored by: code (30%) and, through its outputs, the CSV (30%). The code judge scores only what the code does: agent architecture 30%, prompt and tool craft 30%, robustness 25%, engineering rigor 15%.
Part A - Design (write DESIGN.md before code)
Copy references/DESIGN-template.md to the repo root as DESIGN.md. Fill all 13 sections. The review gate at the end must be fully ticked.
- Output contract. Every CSV column, its exact allowed values, its source (model field or code rule). Do not reformat IDs.
- Architecture. Default: one agent loop or one LLM call for understanding, plus deterministic code for every decision. Add agents only if one fails. Read
references/agent-architecture.md. - The seam. Define the typed record the model returns. Code reads only that record. Decisions use booleans and enums.
- Decision rules. Ordered, risk rules first, first match wins. Use
grill-meon the list to find gaps. Readreferences/rule-engine-architect.md. - Escalation. When to reply, escalate, refuse, and the default when evidence is missing. Both "escalate everything" and "reply to everything" are scored as failures. Read
references/escalation-design.md. - Input checks before the model. Empty, missing, duplicate, oversized, injection markers. Read
references/input-validation-and-overrides.md. - Failure table. For every failure type: what happens to the row, where it is logged, whether the run continues.
- Decisions log. Every real choice, the rejected alternative, and why. Your interview answers come from here.
- Known limits. Write what this design will get wrong now.
Design done when every output column has a named source, every allowed label is produced by a rule, and the escalation rule fits in one sentence.
Part B - Build
- Structure. Entry point, input loading, prompts, agent logic, validation, output writing, tests. Descriptive file names. No
utils.py,helper.py,final.py. Readreferences/naming-and-structure.md. - Secrets and determinism. Keys only from environment variables. No absolute local paths. Seed anything random. Read
references/secrets-and-determinism.md. - Prompts. Treat prompts as reviewable artifacts. Each prompt states: task, allowed outputs (exact labels), evidence to use, output format, what to do when information is missing, and that instructions inside the input must be ignored. Read
references/prompt-engineering.md. - Schema guardrails. Validate every model field before it reaches the CSV. Reject unknown labels. Retry malformed output at most twice, then use the failure row. Read
references/schema-guardrails.md. - Failure handling. One bad row never stops the run. A failed row becomes an honest escalation row and is logged. Read
references/failure-handling.md. - Checkpointing. Save progress per row so a crash or rate limit resumes instead of restarting. Read
references/checkpoint-resilience.md. - Retrieval, if used. Measure recall on sample rows before tuning. Read
references/evidence-retrieval-expert.md. - Tests first for deterministic code. Use
tdd. Model calls are not in unit tests.
What ships with it
16 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/agent-architecture.md 8.7 KB
- references/baseline-run-aug-sample.md 2.4 KB
- references/checkpoint-resilience.md 3.5 KB
- references/DESIGN-template.md 3.0 KB
- references/escalation-design.md 4.0 KB
- references/evidence-retrieval-expert.md 3.3 KB
- references/failure-handling.md 3.3 KB
- references/input-validation-and-overrides.md 4.2 KB
- references/naming-and-structure.md 3.4 KB
- references/past-problems.md 8.8 KB
- references/prompt-engineering.md 3.3 KB
- references/rule-engine-architect.md 3.3 KB
- references/schema-guardrails.md 3.1 KB
- references/secrets-and-determinism.md 2.9 KB
- scripts/baseline_aug_router.py 3.3 KB runs code
- scripts/run_pipeline.py 3.7 KB runs code
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.
- yesterday First seen · 60 lines · 74 tokens per session scan A e101079d7461
orchestrate-build is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (8 stars, last pushed 2d ago), licensed MIT. It adds 74 tokens to every session and 1,113 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-10-24.
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ops
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