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 DataLab-atom/EvoAny --skill write-introgit clone --depth 1 https://github.com/DataLab-atom/EvoAnyWrote 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/datalab-atom/evoany/write-intro)<a href="https://agentmods.dev/skills/datalab-atom/evoany/write-intro"><img src="https://agentmods.dev/badge/skills/datalab-atom/evoany/write-intro/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/datalab-atom/evoany/write-intro"><img src="https://agentmods.dev/badge/skills/datalab-atom/evoany/write-intro.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 | $0.00031 | $0.00897 |
| Opus 5.5 | $0.00012 | $0.00359 |
| Sonnet 5.5 | $0.00006 | $0.00179 |
| Haiku 4.5 | $0.00003 | $0.00090 |
Grade A, and why
write-intro 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 2mo 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/write-intro — Introduction Chapter Writing
D4: Introduction chapter generation — transforms the deep motivation Q and contribution list into a compelling introduction for an academic paper.
Purpose
Read the C-layer derivation forest's convergence points (deep motivation Q) and contributions, and generate a well-structured Introduction section in LaTeX format.
Usage
/write-intro <forest_id> [--venue <venue_name>]
Examples:
/write-intro exp-2024-run-01 --venue NeurIPS/write-intro my-forest --venue ICML
Prerequisites
Before running this skill, ensure:
- The derivation forest has at least one convergence point
- Contributions have been recorded (primary and auxiliary)
- You know the target conference/journal (for appropriate framing)
Behavior
Step 1: Gather Content
- Read the forest:
research_get_forest(forest_id) - Extract:
- Convergence points — the deep motivation Q(s) driving the research
- Primary contributions — what makes this work novel
- Auxiliary contributions — additional benefits
- Evidence nodes — supporting context
- Identify the gap — what problem exists that this work addresses
Step 2: Structure the Introduction
Follow the standard academic introduction structure:
- Opening / Hook — Start with the broader problem area and its importance
- Problem Statement — Describe the specific challenge or limitation
- Existing Approaches and Their Limitations — What's been tried and why it falls short
- Proposed Approach — How this work addresses the gap (from deep motivation Q)
- Key Contributions — Numbered list of primary contributions
- Additional Benefits — Auxiliary contributions
- Broader Impact (optional) — Wider implications of the work
- Paper Structure — Brief roadmap of the remaining sections
Step 3: Generate LaTeX Content
Draft the introduction in academic prose:
- Use formal academic language
- Build a logical narrative from problem to solution
- Clearly articulate what makes this work novel
- Be specific about contributions (not generic claims)
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.
- 2mo ago First seen · 122 lines · 31 tokens per session scan A 87e2f7e5b563
write-intro is a skill published in the GitHub repository DataLab-atom/EvoAny (37 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 31 tokens to every session and 897 once invoked, about $0.0001 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.
write-intro compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| write-introDataLab-atom/EvoAny | Skill | 37 | 6mo ago | Apache-2.0 | A | 31 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 3d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |