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 poemswe/co-researcher --skill academic-writinggit clone --depth 1 https://github.com/poemswe/co-researcherWrote 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/poemswe/co-researcher/academic-writing)<a href="https://agentmods.dev/skills/poemswe/co-researcher/academic-writing"><img src="https://agentmods.dev/badge/skills/poemswe/co-researcher/academic-writing/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/poemswe/co-researcher/academic-writing"><img src="https://agentmods.dev/badge/skills/poemswe/co-researcher/academic-writing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00039 | $0.02371 |
| Opus 5.5 | $0.00016 | $0.00948 |
| Sonnet 5.5 | $0.00008 | $0.00474 |
| Haiku 4.5 | $0.00004 | $0.00237 |
Grade A, and why
academic-writing 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 16d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
1. Anti-Pattern Detection
Five diagnostic patterns that mark AI-generated research prose.
Hedging Soup
Stacking uncertainty markers drains every sentence of meaning.
| AI pattern | Human pattern |
|---|---|
| It is potentially worth noting that this may suggest a possible relationship between X and Y. | X correlates with Y (r = 0.43, p < .01), though the cross-sectional design limits causal inference. |
| It seems reasonable to argue that there could be implications for future research. | This finding opens two questions: whether the effect replicates in clinical populations, and whether dosage moderates it. |
Diagnostic: count hedging words (potentially, possibly, may, might, seems, could, arguably, perhaps) per paragraph. More than two in a single paragraph signals hedging soup. Replace stacked hedges with one precise statement of what is known and one precise statement of what limits that knowledge.
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.
- 16d ago Changed · +1 lines 781f48b69eab
- 1mo ago First seen · 125 lines · 39 tokens per session scan A 054b937555d0
academic-writing is a skill published in the GitHub repository poemswe/co-researcher (135 stars, last pushed 3d ago), licensed MIT. It adds 39 tokens to every session and 2,371 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
refactor-research-code
Clean research code for public release while preserving reported results. Uses releaseaudit.py to separate safe mechanical cleanup from ask-first behavior risks and identity leaks. Use for release-ready repos, dead experiment branches, config extraction, documented entrypoints, and double-blind code cleanup.
prepare-artifacts
Prepares a reproducibility artifact (code/data) for submission and badging. Use when a researcher says "artifact evaluation", "artifact appendix", "reproducibility", "badge", "Artifacts Available/Evaluated/Functional/Reusable", "Results Reproduced/Replicated", "ACM badging", "USENIX/OSDI/SOSP AE", "SIGMOD ARI"…
simulate-reviewers
Venue-calibrated peer-review simulation. Use when a researcher says "simulate reviewers", "mock review", "review my paper like a NeurIPS reviewer", "what would Reviewer 2 say", "red-team my paper", "find weaknesses before I submit", "peer review my paper", "strengths and weaknesses / pros and cons of my paper", "what…
verify-results
Check whether reported paper metrics are consistent with outputs from the local code artifact. Use for result reproduction checks, artifact evaluation, reproducibility checklists, code-versus-paper audits, table or claim metric comparisons, and badge-readiness review.
anonymize-paper
Deep anonymization sweep for double-blind paper submissions, plus clean de-anonymization for camera-ready. Use when a researcher says "anonymize my paper", "double-blind check", "blind this submission", "remove author names", "did I leak my identity" — or, after acceptance, "de-anonymize" / "restore authors for…
make-slides
Builds a conference talk deck (Beamer or Marp markdown) from a paper draft - problem-first narrative, one claim per slide, figures reused straight from the paper's LaTeX source, sized to the real slot (12-20 minute full talk vs 5-minute lightning). Use when the user asks to make slides, build a presentation or talk…