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-checkpoint-resiliencegit 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-checkpoint-resilience)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-checkpoint-resilience"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-checkpoint-resilience/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-checkpoint-resilience"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-checkpoint-resilience.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 | $0.00082 | $0.00753 |
| Opus 5 | $0.00041 | $0.00377 |
| Sonnet 5 | $0.00016 | $0.00151 |
| Haiku 4.5 | $0.00008 | $0.00075 |
Grade A, and why
orchestrate-checkpoint-resilience 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 11d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate: Checkpoint-and-Resume Resilience
Source: first-hand participant case study (Medium, "How I went from 122 to 1 in 24 hours"). After hitting API rate limits mid-run — which forced an expensive full reprocessing pass — the author built deterministic checkpointing after each processed claim, so the next invocation skips already-completed work and resumes exactly where it left off. This is a single, specific, credited technique from a #1-ranked submission, not general HackerRank guidance — cited and labeled as such.
Why this matters specifically for a 24-hour hackathon
Both Orchestrate challenges process a batch dataset (support tickets or damage claims) through model calls, sometimes involving expensive multi-image vision calls. A rate limit hit at row 400 of 500, with no checkpointing, means either: waiting out the rate limit and reprocessing all 500 rows again (burning time and API budget you don't have much of), or submitting a partial, incomplete output.csv. Neither is acceptable with a fixed 24-hour clock and a hard requirement that every input row have an output row (see orchestrate-failure-handling).
The pattern
- Persist progress after every row, not at the end. Write a small state file (or append to a results file directly) recording which input IDs have been fully processed, immediately after each one completes — not batched, not buffered until the end of the run.
- On startup, check for existing progress and skip completed work. The main loop's first action is: load the checkpoint state, filter the input dataset down to unprocessed rows only, and continue from there.
- Make the resume deterministic. The same input, reprocessed, should be identical to what it would have been the first time (this connects to
orchestrate-secrets-and-determinism's seeded-randomness requirement) — a checkpoint system built on top of nondeterministic processing produces inconsistent results depending on exactly where a run happened to stop. - Treat a rate-limit error as a distinct, expected failure mode, not a generic exception. Catch it specifically, back off, and let checkpointing handle the "what's already done" bookkeeping rather than crashing the whole run.
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.
- 11d ago First seen · 40 lines · 82 tokens per session scan A 2ae4d07d5dd1
orchestrate-checkpoint-resilience is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (3 stars, last pushed 1mo ago), licensed MIT. It adds 82 tokens to every session and 753 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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