agentmods.dev

scholaraio AGENTS.md

Project instructions for ScholarAIO, an all-in-one academic research tool used through a coding agent. They explain the project, preferred workflows, and where its code and guidance live.

In plain words
What is it for?
Use them when developing ScholarAIO, using its command-line interface, choosing project skills, or handling literature, evidence, and research outputs.
Why use it?
They help agents work through the project’s normal tools and preserve research context instead of making isolated changes.

Instructions file for CodexOpenCode

566 yesterday A scan 2,046 tokens original MIT
View the source file ZimoLiao/scholaraio
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 instructions/zimoliao/scholaraio/agents-md
Clone the repo
git clone --depth 1 https://github.com/ZimoLiao/scholaraio

Made for: Codex, OpenCode.

Per session 2,046 This file is loaded in full into every session.
When invoked 2,046 The same file — it is already loaded in full.
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 $0.02046 $0.02046
Opus 5 $0.01023 $0.01023
Sonnet 5 $0.00409 $0.00409
Haiku 4.5 $0.00205 $0.00205

Measured yesterday against content hash 9eb5501516f7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

scholaraio AGENTS.md 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 yesterday.

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.

AGENTS.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ScholarAIO - Agent Entry

This is the repository entry document for coding agents. It is intentionally short:

  • keep durable project facts and hard constraints here
  • move multi-step procedures into skills
  • move deep reference material into the indexed docs/ knowledge base

What ScholarAIO Is

ScholarAIO is an All-in-One academic harness for agents. Researchers work through a coding agent to search literature, inspect evidence, organize persistent project context, produce reviewable research outputs, and use bounded scientific-tool adapters without ScholarAIO replacing the agent's native reasoning or orchestration.

The Python package is scholaraio. Real work should usually happen through the ScholarAIO CLI and project skills, not by bypassing runtime helpers with ad hoc file edits.

How To Work In This Repo

  • Prefer a matching project skill in .claude/skills/ when the user request clearly maps to one.
  • Use the scholaraio CLI to do real work instead of only describing what should be done.
  • Load information progressively. Prefer metadata or abstracts first, then conclusions or full text only when needed.
  • Treat paper conclusions as claims, not facts. Compare evidence, point out limitations, and distinguish supported results from author interpretation.
  • Keep user-facing drafts, reports, exports, and research outputs under workspace/, not the repository root or scholaraio/.
  • Do not casually rewrite or delete runtime data. When touching runtime layout, use Config, migration helpers, and tested accessors.
  • When changing runtime layout, compatibility behavior, agent entry docs, or skill discovery, update tests and aligned docs in the same change.
  • After code or doc changes, run the relevant checks and at least one real smoke path when feasible.

Start Here

Read these in roughly this order:

  1. README.md for the product overview and top-level structure.
  2. STRATEGY.md before changing product scope or adding an external integration.
  3. docs/DESIGN.md for the repository knowledge map.
  4. docs/getting-started/agent-setup.md for repo-open vs plugin or cross-project setup.
  5. docs/guide/cli-reference.md for the current user-facing CLI surface.
  6. docs/guide/agent-reference.md for deeper agent, runtime, and skill organization details.
  7. docs/internal/PLANS.md and docs/internal/exec-plans/completed/scholaraio-upgrade-plan.md before changing runtime layout, migration, or compatibility behavior.

Read the full file on GitHub · 141 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. yesterday First seen · 141 lines · 2,046 tokens per session scan A 9eb5501516f7

Subscribe to this mod's changes

scholaraio AGENTS.md is an instructions file published in the GitHub repository ZimoLiao/scholaraio (566 stars, last pushed yesterday), licensed MIT. It adds 2,046 tokens to every session, about $0.0102 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.

ProductivityResearchScientificagentic-scienceagentic-searchai-agentcomputational-researchresearch-workflowscientific-computing
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