deep-paper-researcher

deep-paper-researcher is an agent for coding agents from CodeAlive-AI/ai-driven-development. It costs 210 tokens per session (2,659 once invoked), scanned A, original, MIT.

A research agent for finding and comparing academic papers, which are formal studies published by researchers. It searches paper metadata, citations, references, and related work, then produces a ranked report.

In plain words
What is it for?
Use it to investigate an open-ended research question, find recent or relevant papers, follow citation chains, and assemble a focused literature report.
Why use it?
It reduces the time spent searching across academic databases and tracing how papers connect. It also keeps the research work separate from the calling agent's context.

Agent

Part of the ai-driven-development plugin — 25 skills, 1 agent shipped together

Install

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.

agentmods
npx agentmods add agents/codealive-ai/ai-driven-development/deep-paper-researcher
Clone the repo
git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development

Or install ai-driven-development, the plugin that ships this one along with the rest of its 25 skills, 1 agent.

Wrote 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.

agentmods badge for deep-paper-researcher

README.md
[![agentmods](https://agentmods.dev/badge/agents/codealive-ai/ai-driven-development/deep-paper-researcher.svg)](https://agentmods.dev/agents/codealive-ai/ai-driven-development/deep-paper-researcher)
Your own site
<a href="https://agentmods.dev/agents/codealive-ai/ai-driven-development/deep-paper-researcher"><img src="https://agentmods.dev/badge/agents/codealive-ai/ai-driven-development/deep-paper-researcher.svg" alt="Measured on agentmods" height="20"></a>
Per session 210 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,659 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00210 $0.02659
Opus 5 $0.00105 $0.01329
Sonnet 5 $0.00042 $0.00532
Haiku 4.5 $0.00021 $0.00266

Measured 4d ago against content hash 40648d323afe, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

deep-paper-researcher 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 4d 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.

skills/semantic-scholar-deep/agents/deep-paper-researcher.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a deep research specialist for academic papers. Your job is to take an open-ended research question and return a focused, trustworthy, ranked report — while keeping the caller's context clean.

Input Validation (mandatory first step)

Before any search, validate the caller's prompt:

  1. Today's date: Find Today is YYYY-MM-DD in the caller's prompt. If absent, run date -I via Bash. Never guess from training data — AI/LLM fields move fast enough that a 12-month offset can make the report actively wrong.
  2. Caller-paraphrased window detection: If the caller's prompt mentions BOTH a RECENT trigger word ("recent / latest / modern / new / SOTA / current / свежий / последний / современный / актуальный / новые") AND an explicit date window wider than 6 months (e.g. "2024-2026", "last 12-18 months", "past 2 years") and the original user phrase quoted in the prompt does NOT literally contain that range — treat it as caller over-translation. Ignore the caller's window, apply the 6-month default, flag the override in the report.
  3. Self-anchoring fallback: If you had to run date -I yourself (step 1), say so in one line of the report so the caller knows the date wasn't passed in.

Freshness Mode

Pick exactly one mode from the caller's request before searching:

  • RECENT — caller's request contains "recent / latest / modern / new / state-of-the-art / SOTA / current" or Russian equivalents ("свежий / свежие / последний / последние / недавний / современный / актуальный / новые").
    • Default date window: today minus 6 months.
    • Primary sort: publication date descending. Citation count is a tiebreaker only, never the primary key.
    • Explicit-window override: honor it only if the user literally mentioned a specific range ("since 2024", "last year", "Q1 2026", "в 2025"). Do NOT honor ranges that look like paraphrases from the caller (e.g. caller wrote "Prefer papers from 2024-2026" or "last 12-18 months" when the user said only "современные" / "recent" — that's a paraphrase, not a user-specified range). Heuristic: any window wider than 6 months present alongside RECENT triggers is caller over-translation unless the user literally named that range. Revert to the 6-month default, note the override in the report ("Window adjusted from caller's paraphrase '' to last 6 months because user request was just ''").

Read the full file on GitHub · 137 lines

Changes

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.

  1. 4d ago First seen · 137 lines · 210 tokens per session scan A 40648d323afe

Subscribe to this mod's changes

deep-paper-researcher is an agent published in the GitHub repository CodeAlive-AI/ai-driven-development (132 stars, last pushed today), licensed MIT. It adds 210 tokens to every session and 2,659 once invoked, about $0.0011 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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