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 Boom5426/Nature-Paper-Skills --skill results-section-revisiongit clone --depth 1 https://github.com/Boom5426/Nature-Paper-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/boom5426/nature-paper-skills/results-section-revision)<a href="https://agentmods.dev/skills/boom5426/nature-paper-skills/results-section-revision"><img src="https://agentmods.dev/badge/skills/boom5426/nature-paper-skills/results-section-revision/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/boom5426/nature-paper-skills/results-section-revision"><img src="https://agentmods.dev/badge/skills/boom5426/nature-paper-skills/results-section-revision.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 | $0.00051 | $0.00640 |
| Opus 5 | $0.00026 | $0.00320 |
| Sonnet 5 | $0.00010 | $0.00128 |
| Haiku 4.5 | $0.00005 | $0.00064 |
Grade A, and why
results-section-revision 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 13d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Results Section Revision
Overview
Use this skill for late-stage Results revision when the science is mostly stable but the writing architecture is not. It is narrower than scientific-writing and manuscript-optimizer: the job here is to repair subsection titles, bridge paragraphs, paragraph openings, and local argumentative flow.
Use scientific-writing for general prose drafting or rewriting. Use manuscript-optimizer when the claim hierarchy, evidence chain, or figure logic is still unstable. Use this skill when the section is largely right but still reads like stitched figure captions rather than a controlled argument.
Quick Checks
Before revising any Results subsection, check:
- Is the title selling the conclusion rather than the method?
- Does the first sentence of each paragraph state that paragraph's point?
- Are openings like
we asked,we examined, orto test thisoverused? - Are abstract words masking a more specific noun?
- Is the relationship to the previous paragraph explicit?
- Is the paragraph only reporting a result, or advancing the argument?
- Does the paragraph close by stating what changes in interpretation?
- Does the subsection end in a way that naturally leads into the next one?
Subsection Pattern
Each Results subsection should answer two reader questions in order:
- Why is this analysis needed now?
- What does it establish?
If the second question appears before the first, the subsection will feel abrupt.
The first paragraph should bridge from the previous subsection and state why the next analysis is necessary. Later paragraphs should each do one job: sharpen a finding, separate related findings, show a consequence for evaluation or prioritization, or explain what a comparison actually distinguishes.
Paragraph Openings
Prefer openings that either orient the reader or state a concrete result:
The molecular branch first showed ...The same pattern was also evident in ...A further question was whether ...Cross-representation analyses showed ...
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.
- 13d ago First seen · 70 lines · 51 tokens per session scan A 5f51ebae06f9
results-section-revision is a skill published in the GitHub repository Boom5426/Nature-Paper-Skills (490 stars, last pushed 7d ago), licensed MIT. It adds 51 tokens to every session and 640 once invoked, about $0.0003 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…