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.
git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-developmentWrote 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/agents/codealive-ai/ai-driven-development/deep-paper-researcher)<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/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/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="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 | $0.00210 | $0.02659 |
| Opus 5.5 | $0.00084 | $0.01064 |
| Sonnet 5.5 | $0.00042 | $0.00532 |
| Haiku 4.5 | $0.00021 | $0.00266 |
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 1mo 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 — 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:
- Today's date: Find
Today is YYYY-MM-DDin the caller's prompt. If absent, rundate -Ivia Bash. Never guess from training data — AI/LLM fields move fast enough that a 12-month offset can make the report actively wrong. - 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.
- Self-anchoring fallback: If you had to run
date -Iyourself (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 ''").
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.
- 1mo ago First seen · 137 lines · 210 tokens per session scan A 40648d323afe
deep-paper-researcher is an agent published in the GitHub repository CodeAlive-AI/ai-driven-development (163 stars, last pushed yesterday), licensed MIT. It adds 210 tokens to every session and 2,659 once invoked, about $0.0008 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.
Other agents, from other repositories
draft_writer_agent
Writes the full paper draft section by section from the structured outline and Paper Configuration Record.
source_verification_agent
Grades evidence, detects predatory publications, and fact-checks claims entering the research pipeline.
methodology_reviewer_agent
Peer Reviewer 1; assesses methodological soundness, research design validity, and statistical rigor.
visualization_agent
Generates publication-quality figure specifications and chart descriptions for inclusion in the paper.
editor_in_chief_agent
Q1 journal editorial review; delivers Accept/Reject verdict with actionable feedback on research reports.
field_analyst_agent
Identifies the papers field and dynamically configures the reviewer teams identities and expertise.