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 agentmods add skills/datalab-atom/evoany/ask-litnpx skills add DataLab-atom/EvoAny --skill ask-litgit 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/ask-lit)<a href="https://agentmods.dev/skills/datalab-atom/evoany/ask-lit"><img src="https://agentmods.dev/badge/skills/datalab-atom/evoany/ask-lit.svg" alt="Measured on agentmods" 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 | $0.00041 | $0.00473 |
| Opus 5 | $0.00020 | $0.00236 |
| Sonnet 5 | $0.00008 | $0.00095 |
| Haiku 4.5 | $0.00004 | $0.00047 |
Grade A, and why
ask-lit 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 5d 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.
What it actually says
/ask-lit — Literature Question Answering
A4: Unified literature interface — local-first, online-supplement, auto-ingest.
Purpose
Answer a research question using academic literature. Prioritizes the local vector database, supplements with online search when needed, and automatically ingests new papers for future queries.
Usage
/ask-lit <question or topic>
Examples:
/ask-lit attention mechanism for long-tailed recognition/ask-lit why does mixup regularization improve calibration/ask-lit SOTA methods for CIFAR-100-LT
Behavior
-
Local search first — call
lit_search_localwith the query- If >= 3 relevant results (score > 0.1): proceed to answer
-
Online supplement (if local results < 3)
- Invoke
/search-litto find papers online - New papers are automatically ingested into the local DB via
lit_ingest - Merge online results with local results
- Invoke
-
Synthesize answer
- Read the abstracts / content of the top results
- Generate a concise answer to the question
- Cite papers using BibTeX keys:
[authorYYYYword]
-
Return
- The synthesized answer
- A list of cited papers with full BibTeX entries
- Call
bib_appendto add new citations toresearch/refs/references.bib
Output Format
## Answer
<synthesized answer with [citations]>
## References
<BibTeX entries>
Tool Usage
| Tool | Purpose |
|---|---|
lit_search_local |
Search local vector DB |
/search-lit |
Online literature search (when local is insufficient) |
lit_ingest |
Auto-ingest new papers |
bib_append |
Persist citations to references.bib |
code_qa |
If question involves code from the evolution |
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.
- 5d ago First seen · 68 lines · 41 tokens per session scan A 281266a1da96
ask-lit is a skill published in the GitHub repository DataLab-atom/EvoAny (37 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 41 tokens to every session and 473 once invoked, about $0.0002 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.
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