Borrowing it
Nothing to install: this file belongs to zhnnky329/MathModeling-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zhnnky329/MathModeling-skills/main/.claude/skills/result-report-generator/SKILL.mdgit clone --depth 1 https://github.com/zhnnky329/MathModeling-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/zhnnky329/mathmodeling-skills/result-report-generator)<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/result-report-generator"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/result-report-generator/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/zhnnky329/mathmodeling-skills/result-report-generator"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/result-report-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00040 | $0.00574 |
| Opus 5 | $0.00020 | $0.00287 |
| Sonnet 5 | $0.00008 | $0.00115 |
| Haiku 4.5 | $0.00004 | $0.00057 |
Grade A, and why
result-report-generator 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Turn saved experiment artifacts into compact evidence. Do not treat ordinary successful runs as requiring a long report, and do not choose the winning method.
Inputs
run_summary.json- method card and probe summary
- decision ledger
- saved tables, metrics, and figures
- session
rigor_profile
Stop if the run summary claims outputs that do not exist or if main and baseline are not comparable.
Modes
Ordinary lean round
- Validate the run summary and referenced artifacts.
- Return a compact evidence digest in the conversation.
- Do not save a Markdown report unless:
- a fallback trigger fired;
- a material anomaly or contradiction exists;
- the human must make a proceed/adjust/fallback decision.
Decision-point round
Save:
results/Qx/experiments/roundN/qx_decision_report.md
Include only:
- main vs baseline metrics;
- output-degeneracy/concentration evidence;
- assumption or feasibility warnings;
- robustness evidence already available;
- fallback trigger state;
- unresolved trade-offs.
Then invoke decision-prompt-builder. After the human answers, route the answer to modeler-decision-logger.
Final/submission mode
Save:
results/Qx/reports/qx_final_result_analysis.md
Include:
- final main/baseline comparison;
- uncertainty and error;
- concentration/degeneracy interpretation;
- robustness links;
- limitations and applicable scope;
- exact source paths for numerical claims.
Rejection and Fallback
- Archive a method only after a human
result_verdictorfallback_activationdecision. - Move rejected code and outputs to
workspace/archived/<Qx>/<method>_REJECTED_roundN/. - Add one compact history line to
qx_method_card.md; do not create a separate iteration log. - Do not archive from an AI suggestion alone.
Rules
- Do not fabricate metrics, comparisons, or interpretations.
- Separate facts from human verdicts.
- Do not create
result-report-generator_modeler_decision.md. - Do not repeat the full run summary; cite it and extract only decision-relevant evidence.
- Do not call a diagnostic reference a usable baseline.
- Do not generate paper prose.
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 · 86 lines · 40 tokens per session scan A 8c2fe0f78778
result-report-generator is a skill published in the GitHub repository zhnnky329/MathModeling-skills (847 stars, last pushed 17d ago), licensed MIT. It adds 40 tokens to every session and 574 once invoked, about $0.0002 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 2026-08-30.
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