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-self-scoringgit 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-self-scoring)<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-self-scoring"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-self-scoring/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-self-scoring"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-self-scoring.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 | $0.00088 | $0.01230 |
| Opus 5 | $0.00044 | $0.00615 |
| Sonnet 5 | $0.00018 | $0.00246 |
| Haiku 4.5 | $0.00009 | $0.00123 |
Grade A, and why
orchestrate-self-scoring 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate Self-Scoring
The single highest-leverage activity in the last few hours of a timeboxed challenge is finding your own weakest dimension before a judge does — because at that point you can still act on it. This skill is a structured, honest self-audit against the four published signals, not a confidence-boosting exercise.
Ground rule: score against evidence, not effort
The instinct under deadline pressure is to score generously because you worked hard on something. Don't. Score against what a reviewer would actually see, the same evidence-anchored discipline the real scoring uses. Time spent is not evidence of quality.
The four-signal self-audit
1. Code zip (30%) — score 0–5 per row, be specific about why
| Check | Evidence required |
|---|---|
| Is there a genuine agent loop, or a decision tree with LLM calls in it? | Point to the actual function. If you can't point to one place where the model decides what happens next, this is a decision tree. |
| Are tools well-named, well-described, individually testable? | Open the tool definitions file. Would a stranger know when to use each tool from its description alone? |
| Are prompts extracted, readable, and deliberate? | Are they in named files/constants, or inline f-strings? |
| Are failure paths (malformed output, tool error, step cap) handled explicitly? | Find the code for each. If it's absent, that's a 0 on this row, not an assumption of graceful degradation. |
| Does the README explain design decisions, not just usage? | Read it as a stranger would. Does it justify choices or only describe commands? |
Run orchestrate-agent-architecture's checklist directly against your actual code, not from memory.
2. Output CSV (30%) — sample and grade, don't eyeball
Pull 5–8 rows at random (not your favorites) and grade each on the Chakra-style 4-point scale:
- 3: specific evidence cited, KB reference is real, reasoning connects evidence to verdict
- 2: right verdict, generic or thin justification
- 1: wrong reasoning, or reasoning that doesn't match the verdict
- 0: no real justification present
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 · 71 lines · 88 tokens per session scan A 29eb4ea90608
orchestrate-self-scoring is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (3 stars, last pushed 29d ago), licensed MIT. It adds 88 tokens to every session and 1,230 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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