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-relatedgit 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-related)<a href="https://agentmods.dev/skills/datalab-atom/evoany/write-related"><img src="https://agentmods.dev/badge/skills/datalab-atom/evoany/write-related/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-related"><img src="https://agentmods.dev/badge/skills/datalab-atom/evoany/write-related.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 | $0.00023 | $0.00847 |
| Opus 5.5 | $0.00009 | $0.00339 |
| Sonnet 5.5 | $0.00005 | $0.00169 |
| Haiku 4.5 | $0.00002 | $0.00085 |
Grade A, and why
write-related 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/write-related — Related Work Chapter Writing
D3: Related work chapter generation — synthesizes literature from A-layer searches into a structured related work section.
Purpose
Read the literature gathered during the C-layer derivation process (evidence nodes with literature references) and the A-layer search results, then generate a well-structured Related Work section in LaTeX format.
Usage
/write-related <forest_id> [--venue <venue_name>]
Examples:
/write-related exp-2024-run-01 --venue NeurIPS/write-related my-forest --venue ICML
Prerequisites
Before running this skill, ensure:
- A-layer literature searches have been performed during research loop
- Evidence nodes in the forest have
literature_refspopulated research/refs/references.bibcontains the relevant BibTeX entries
Behavior
Step 1: Gather Literature
- Read the forest:
research_get_forest(forest_id) - Extract all evidence nodes with
literature_refs - Collect all unique BibTeX keys from the forest
- Read
research/refs/references.bibfor the full bibliography
Step 2: Organize by Theme
Group related papers into themes/clusters:
- Foundational Work — papers that established the problem area
- Directly Related — papers addressing the same/similar problem (the deep motivation Q)
- Technical Precursors — papers that proposed the mechanisms used in this work
- Alternative Approaches — methods that address similar goals differently
- Evolutionary Computation in ML — other work combining evolution with ML
Step 3: Synthesize the Related Work Chapter
Generate a LaTeX related work chapter with structured discussions:
- Foundations — background and context for the problem
- Prior Work on [Deep Motivation Q] — what's been done on the core problem
- Key Mechanisms in Literature — prior work on specific techniques
- Positioning — where this work fits relative to the literature
Step 4: Write to File
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 · 112 lines · 23 tokens per session scan A 5a36cbb00484
write-related is a skill published in the GitHub repository DataLab-atom/EvoAny (37 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 23 tokens to every session and 847 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-related compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| write-relatedDataLab-atom/EvoAny | Skill | 37 | 6mo ago | Apache-2.0 | A | 23 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |