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 ShaishavMaisuria/research-paper-lifecycle-skills --skill verify-resultsgit clone --depth 1 https://github.com/ShaishavMaisuria/research-paper-lifecycle-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/shaishavmaisuria/research-paper-lifecycle-skills/verify-results)<a href="https://agentmods.dev/skills/shaishavmaisuria/research-paper-lifecycle-skills/verify-results"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/verify-results/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/shaishavmaisuria/research-paper-lifecycle-skills/verify-results"><img src="https://agentmods.dev/badge/skills/shaishavmaisuria/research-paper-lifecycle-skills/verify-results.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 | $0.00050 | $0.02800 |
| Opus 5.5 | $0.00020 | $0.01120 |
| Sonnet 5.5 | $0.00010 | $0.00560 |
| Haiku 4.5 | $0.00005 | $0.00280 |
Grade A, and why
verify-results 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 1mo 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify Results
Close the loop between what the paper claims and what the artifact produces. This skill helps the author confirm their reported numbers reproduce: it locates the experiment code, helps stand up a clean/sandboxed run, runs the artifact's own tests, and does a consistency audit — comparing the metrics the run produces against the paper's tables and claims, within a tolerance that does not change the paper's conclusions. It reports mismatches (paper says X, code produces Y) and missing reproduction steps, and audits the artifact against current reproducibility-badge expectations.
It is a copilot: it sets up and guides, and the author runs anything heavy (training, long evals) in their own environment. It never fabricates a number, never executes destructive commands, and never claims a result was independently reproduced — a clean audit means consistent, not reproduced.
When to use
- "Do my results reproduce?" / "Does my code match the paper's tables?"
- "Check my reproducibility" / "verify my experiments" / "reproduce my numbers".
- Prepping an artifact for an evaluation track (ACM AE, USENIX, OSDI, SOSP, SIGMOD ARI, ETAPS, NeurIPS/ICML/ACL reproducibility).
- Filling a reproducibility checklist (NeurIPS Paper Checklist, ACL Responsible NLP, ML Code Completeness) and wanting an honest read on each item.
- After a results table changes and you need to confirm the code still produces it.
Inputs
- The artifact / experiment code (a directory; a repo URL the author has cloned locally — this skill reads local files, it does not clone for you).
- The paper
.texwhose tables/claims are being checked (or the specific\inputfile that holds the results table). - The target venue's artifact track, if any — its current Call for Artifacts decides which badges exist and what hosting they require.
- Optionally, a metrics file from a prior run (JSON/CSV) to compare without re-running.
Process
This skill follows plan → set up → run (author) → audit, with the verification step grounded in external, measurable signals (test pass/fail, a numeric diff against a file the run produced) — never the model's own judgment that the numbers "look right".
What ships with it
6 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.
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.
- 1mo ago First seen · 206 lines · 50 tokens per session scan A b3f4ad903b4b
verify-results is a skill published in the GitHub repository ShaishavMaisuria/research-paper-lifecycle-skills (55 stars, last pushed 3d ago), licensed Apache-2.0. It adds 50 tokens to every session and 2,800 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
academic-writing
You must use this when producing any research prose — literature reviews, syntheses, analyses, methodology descriptions, discussion sections, abstracts, or any written output intended for an academic audience.
systematic-review
You must use this when conducting PRISMA-standard systematic reviews, protocol development, or Risk of Bias assessment.
hypothesis-testing
You must use this when formulating testable hypotheses, designing experimental controls, or defining falsification criteria.
quantitative-analysis
You must use this when selecting statistical tests, interpreting effect sizes, or conducting power analysis.
research-synthesis
You must use this when merging findings from multiple studies into a coherent narrative with grounded evidence.
literature-review
You must use this when synthesizing existing knowledge, identifying research gaps, or tracing the evolution of scientific ideas.