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-packagegit 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-package)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-package"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-package/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-package"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-package.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 | $0.00090 | $0.01079 |
| Opus 5.5 | $0.00036 | $0.00432 |
| Sonnet 5.5 | $0.00018 | $0.00216 |
| Haiku 4.5 | $0.00009 | $0.00108 |
Grade A, and why
orchestrate-package 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Package the Submission
This skill answers one question: is everything ready for the submission, according to the problem statement? It does not invent requirements. The problem statement is the only authority. If the challenge page disagrees with this file, the challenge page wins.
Step 1 - Read the submission rules (from the problem statement)
Write these down in SUBMISSION-CHECKLIST.md before checking anything. Past rounds required:
| Round | Required files | Extra rules |
|---|---|---|
| May 26 (support triage) | support_tickets/output.csv, code.zip, log.txt chat transcript, README |
5 output columns; AI judge interview |
| Jun 26 (claims review) | output.csv (repo root), code.zip, transcript |
13 output columns in exact order; image_paths separated by ; |
| Aug 26 (message router) | output.csv, code.zip, transcript |
6 columns; confidence between 0 and 1; evidence_message_ids semicolon-separated or none |
| Sep 26 (buy or wait) | output.csv, code.zip (must include evaluation/usage_report.md), transcript |
8 columns; 0 <= amount_safe_to_pay <= requested_amount; strict payment-plan format |
For the current challenge, copy its exact list from the problem statement. Do not trust this table for the current round.
Step 2 - Run the checker
python skills/orchestrate-package/scripts/package_submission.py \
--repo . \
--input dataset/<input-file>.csv \
--key <id-column> \
--columns "<col1>,<col2>,..." \
--allowed "<enum-col>=<v1>,<v2>,..." \
--transcript log.txt \
--out submission
If the installed copy lives in ~/.claude/skills/orchestrate-package/, use that path instead. Run it from the repo root.
- Every line prints PASS or FAIL with the evidence.
- Exit code 0 and
READY TO SUBMITmeans the folder was built. - Exit code 1 and
NOT READYmeans nothing was built. Fix the FAIL lines and run again.
Step 3 - Judge the things the script cannot check
The script checks shape, not quality. Also check by hand:
- Justifications match their status and cite the evidence used (see
orchestrate-test-fix). - Output values are what the code produced on the latest run, not a stale file. Re-run the pipeline from a clean folder first.
- The README lists setup, environment variables, the run command, and the expected input and output paths, and every claim in it is true.
- No
.env, API keys, absolute local paths, or personal data in any file. - Usage and cost numbers in
evaluation/usage_report.mdcome from the final full run, not from a test. - The transcript is complete, not truncated, and shows your own decisions.
What ships with it
1 file 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.
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 · 73 lines · 90 tokens per session scan A 500f9614ae75
orchestrate-package is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (8 stars, last pushed 2d ago), licensed MIT. It adds 90 tokens to every session and 1,079 once invoked, about $0.0004 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.
Other skills, from other repositories
test-result-feedback
Close the acceptance loop for a tested change — consume the apc test TierResult, map EACH acceptance criterion to a pass/fail outcome from structured fields (never chat text), attach an evidence ref, and write it back to the task. A SUT red is a finding, never softened to green.
testgen-flow
Workflow for generating test cases from requirements (Issue Tracker / Wiki sources), exporting to a Test Management System, etc.
lov-atom-feature-dev
An end-to-end workflow for building a small, self-contained feature across its shared core, command-line tool, API, user interface, saved parameter presets, agent tool, and debugging panel. It uses one contract and shared test cases across the pieces.
lov-atom-operations
A checklist and implementation workflow for making a completed feature ready to explain, test, find, authorize, sell, measure, and maintain. It decides which supporting work applies instead of adding every integration by default.
test-router
A testing skill that chooses which checks to run based on the type, scope, and risk of a change.
dev-testing
A testing guide that defines when to use unit, integration, API, and end-to-end tests. Unit tests check small pieces of code, while end-to-end tests check a full user flow.