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 BingHanOfUESTC/open_agent_team --skill literature-evidence-mappinggit clone --depth 1 https://github.com/BingHanOfUESTC/open_agent_teamWrote 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/binghanofuestc/open_agent_team/literature-evidence-mapping)<a href="https://agentmods.dev/skills/binghanofuestc/open_agent_team/literature-evidence-mapping"><img src="https://agentmods.dev/badge/skills/binghanofuestc/open_agent_team/literature-evidence-mapping/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/binghanofuestc/open_agent_team/literature-evidence-mapping"><img src="https://agentmods.dev/badge/skills/binghanofuestc/open_agent_team/literature-evidence-mapping.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00035 | $0.00860 |
| Opus 5 | $0.00017 | $0.00430 |
| Sonnet 5 | $0.00007 | $0.00172 |
| Haiku 4.5 | $0.00003 | $0.00086 |
Grade A, and why
literature-evidence-mapping 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 11d 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Evidence Mapping
This skill turns search results into research-grade evidence. It should be used before ideation and again before final paper writing.
1. Search Plan
Write:
research_workspace/literature/search_plan.md
Include:
research question
inclusion criteria
exclusion criteria
search queries
venues and indexes
date window
target benchmark or dataset names
Use recursive search when the field is unclear or the first pass returns shallow coverage:
breadth pass: collect diverse candidate papers
depth pass: follow citations, authors, code links, benchmarks
gap pass: search directly for failure modes and limitations
recency pass: search the latest arXiv/OpenReview/venue/Papers With Code entries
Default depth floor:
screen 30-60 candidate papers
include 15-30 relevant papers
deep-read 8-12 key papers
collect at least 5 recent papers from the last 24 months when available
collect at least 3 limitation/negative-result sources
2. Paper Inventory
Maintain:
research_workspace/literature/paper_inventory.tsv
Columns:
id
canonical_key
title
authors
year
venue
url
code_url
data_url
task
method_family
benchmark
result_claim
evidence_level
source_pass
why_relevant
limitations_signal
status
Status values:
candidate
included
excluded
deep_read
baseline_source
idea_source
citation_only
3. Evidence Card
For every key paper, write a card under:
research_workspace/literature/cards/<paper_id>.md
Template:
# <paper title>
- Citation:
- Source URL:
- Code/Data:
- Problem:
- Method:
- Key assumptions:
- Experiments:
- Reported results:
- Limitations stated by authors:
- Limitations inferred by team:
- Relevance to current research:
- What can be reused:
- What should not be assumed:
- Citation key:
- Paper section/table/figure anchors:
- Related-work bucket:
- Claims allowed in paper:
- Claims not supported:
Deep-read cards must come from full paper text whenever possible. If only abstract or metadata was available, mark the card as abstract_only and do not use it for method details, result comparisons, or limitations beyond the abstract text.
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.
- 11d ago First seen · 183 lines · 35 tokens per session scan A c64b3c1473dd
literature-evidence-mapping is a skill published in the GitHub repository BingHanOfUESTC/open_agent_team (106 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 860 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.
Other skills, from other repositories
company-product-context
Compiles comprehensive company product context from PDF documents, web research, and industry knowledge.
python-lib-analyzer
Analyze any Python library structure, explore modules, classes, and functions with signatures and documentation.
Ability Generator
This skill generates markdown skill templates to be later used.
nexus-configuration
A skill for reading, planning, approving, and checking configuration in Nexus, the current private conversation or room. It covers settings such as agents, rooms, providers, channels, connectors, skills, models, tools, and MCP connections.
agentica
How to answer questions about the agentica product you are running inside — CLI flags, config.yaml profiles, API keys, models, sessions, resume, workspace, AGENTS.md standing rules, skills, logs, upgrade, and selfmanage. Use when asked how agentica works, how to configure or upgrade it, where state lives on disk…
generate-ai-rules
Generate AI assistant configuration files for a repository — CLAUDE.md, AGENTS.md, and Cursor rules (.cursor/rules/.mdc) — from codebase analysis. Use whenever the user wants to create or update CLAUDE.md, AGENTS.md, agent rules, Cursor rules, AI coding assistant configuration, or "onboard AI tools" to a project, even…