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 gioviat/research-toolkit --skill scientific-prosegit clone --depth 1 https://github.com/gioviat/research-toolkitWrote 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/gioviat/research-toolkit/scientific-prose)<a href="https://agentmods.dev/skills/gioviat/research-toolkit/scientific-prose"><img src="https://agentmods.dev/badge/skills/gioviat/research-toolkit/scientific-prose/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/gioviat/research-toolkit/scientific-prose"><img src="https://agentmods.dev/badge/skills/gioviat/research-toolkit/scientific-prose.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 | $0.00053 | $0.01207 |
| Opus 5 | $0.00026 | $0.00603 |
| Sonnet 5 | $0.00011 | $0.00241 |
| Haiku 4.5 | $0.00005 | $0.00121 |
Grade A, and why
scientific-prose 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 8d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scientific prose
Banned patterns
- Em dashes (—) and en dashes (–): em dashes are clear signs of AI writing, so remove them and replace them with more natural alternatives, like periods or commas. If not possible, restructure the sentence. This is a hard constraint.
- Throat-clearing openers: "It is important to note that...", "It's worth mentioning..."
- Unearned transitions: "Moreover", "Furthermore", "Additionally" used as connective filler rather than because the next sentence needs that specific logical relation
- Preview sentences: "In this section, we will discuss...". State the content directly instead.
- Inflated vocabulary where a plain word means the same thing: "delve", "leverage", "robust", "multifaceted", "testament", "tapestry", "comprehensive" (as a filler adjective), "utilize" (use "use"), "load-bearing", "align with", "crucial", "emphasizing", "highlight" (verb). Additional AI-frequent terms to eliminate: "actually", "interplay", "intricacies", "landscape", "fostering", and "garner".
- Manufactured rule-of-three lists and uniform sentence length: vary structure the way a written derivation or argument naturally does
- Hedge words used to avoid a claim rather than to state genuine uncertainty: "may", "could potentially", "generally speaking": either state the result or state precisely what is uncertain and why
- Generic conclusions: "This shows the importance of X" — end on the specific finding, not a platitude
- Unnecessary comparisons: "We do X rather than Y", "It is X, not Y", "What it does, what it does not". If there is no need for a direct comparison, avoid it: just state plainly the central idea.
- Undue emphasis on significance and legacy: avoid framing standard methods or incremental results as a "pivotal moment" or "vital role". Avoid saying a development "marks a shift" or is "setting the stage for".
- Superficial analyses with "-ing" endings: avoid tacking present participle phrases onto sentences to add fake depth. An example of this is writing "achieving SOTA", "underscoring its efficiency".
- Promotional and advertisement-like language: avoid "groundbreaking", "renowned", or "boasts a" when describing architectures or experimental results.
- Vague attributions and weasel words: do not use "Experts argue", "Observers note", or "Industry reports". Ensure you attribute opinions or prior findings with specific sources.
- Outline-like boilerplate sections: avoid formulaic "Challenges and Future Prospects" structural framing in the discussion.
- Copula avoidance: do not substitute elaborate constructions like "serves as", "represents a", or "offers a" for simple verbs like "is" or "are".
- Negative parallelisms and tailing negations: avoid overused "Not only... but..." constructions. Do not use tacked-on fragments like "no guessing" in place of complete clauses.
- Elegant variation (synonym cycling): do not unnecessarily swap technical terms (e.g., cycling randomly through "model", "network", and "framework") just to avoid repetition.
- False ranges: avoid "from X to Y" constructions where X and Y are not placed on a meaningful scale.
- Signposting and announcements: omit meta-commentary like "Let's break this down" or "Here's what you need to know".
- Diff-anchored writing: describe the methodology or architecture as it currently is. Avoid narrating the process of changing it from a previous version unless explicitly writing a changelog.
- Aphorism formulas: avoid reducing rigorous technical claims into pseudo-profound formulas like "X is the Y of Z".
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.
- 8d ago First seen · 49 lines · 53 tokens per session scan A ce1d08457e64
scientific-prose is a skill published in the GitHub repository gioviat/research-toolkit (2 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 1,207 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 2026-08-31.
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