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-test-fixgit 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-test-fix)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-test-fix"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-test-fix/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-test-fix"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-test-fix.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 | $0.00083 | $0.00930 |
| Opus 5.5 | $0.00033 | $0.00372 |
| Sonnet 5.5 | $0.00017 | $0.00186 |
| Haiku 4.5 | $0.00008 | $0.00093 |
Grade A, and why
orchestrate-test-fix 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test and Fix
Scored by: output CSV (30%), where accuracy is scored per ticket 0-100 against a hidden golden set, plus a whole-file safety score. A correct action with an empty, generic, or self-contradicting justification is capped near 70. Justifications are part of the score.
Part A - Test (phase 3)
- Edge cases. Build a set: empty row, missing field, duplicate ID, prompt injection, near-identical tickets that must be treated alike, out-of-scope request, another language. Read
references/edge-case-testing.md. - Overfitting audit. Look for rules that depend on ID format, timestamp format, row order, or exact sample wording. Fix them first. Read
references/dataset-coupling-auditor.md. - Determinism. Run twice on identical input. Outputs must match. If not, find the exact difference. Read
references/determinism-auditor.md. - Two strategies. Compare at least two prompt or config variants on the sample. Write why you chose one. This is graded. Read
references/multi-strategy-evaluation.md. - Trace one input through every stage: read, clean, context, model call, parse, validate, write. Confirm each does what you assume. Read
references/input-tracing.md.
Output of Part A: a test log with input, expected, actual, and the failing stage. Each wrong row is tagged RULE (code problem) or MISREAD (model perceived it wrong).
Part B - Fix (phase 4)
For each wrong row, one at a time:
- Trace it to the stage that failed.
- Classify: RULE is fixed in code and is free to re-test. MISREAD is fixed in the prompt or evidence and costs a model run, so batch those.
- Write the change record: CHANGE, the requirement it serves, the rows it should fix, the rows it could break, cost, verdict.
- Re-run and diff the entire output.
- Verdict: SHIP only if the target fixes and nothing previously correct breaks. Otherwise REVERT.
Specific fixes:
- Justification weak, label right:
references/justification-quality.md. Name the evidence used, what triggered the path, and uncertainty in plain words. - Image or document misread:
references/multimodal-evidence-grounding.md. - Escalation rate off:
references/confidence-calibrator.md. Gate on booleans and enums only. - Cause unknown: diagnose with the
diagnosing-bugsskill before changing anything.
What ships with it
8 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/confidence-calibrator.md 2.6 KB
- references/dataset-coupling-auditor.md 3.2 KB
- references/determinism-auditor.md 2.8 KB
- references/edge-case-testing.md 3.3 KB
- references/input-tracing.md 3.3 KB
- references/justification-quality.md 6.4 KB
- references/multi-strategy-evaluation.md 3.7 KB
- references/multimodal-evidence-grounding.md 4.2 KB
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 · 58 lines · 83 tokens per session scan A 1d2761364a15
orchestrate-test-fix is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (8 stars, last pushed 2d ago), licensed MIT. It adds 83 tokens to every session and 930 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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