Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Axect/magi-researchers/plugin install magi-researchersWrote 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/axect/magi-researchers/research-explain)<a href="https://agentmods.dev/skills/axect/magi-researchers/research-explain"><img src="https://agentmods.dev/badge/skills/axect/magi-researchers/research-explain/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/axect/magi-researchers/research-explain"><img src="https://agentmods.dev/badge/skills/axect/magi-researchers/research-explain.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 | $0.00000 | $0.06593 |
| Opus 5 | $0.00000 | $0.03297 |
| Sonnet 5 | $0.00000 | $0.01319 |
| Haiku 4.5 | $0.00000 | $0.00659 |
Grade A, and why
research-explain 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 10d 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 — 427 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Explain Skill
Description
Generates high-quality explanations of concepts using Gemini and Codex in parallel (Phase 1: MAGI strategy exploration), then synthesizes a single-voice explanation with Claude (Phase 2: convergent generation).
Usage
/research-explain "concept" [--domain physics|ai_ml|statistics|mathematics|paper] [--audience general-public|high-school|undergraduate|phd-student|researcher|expert|"free text"] [--weights '{"clarity":0.2,"accuracy":0.2}'] [--depth low|medium|high|max] [--personas N] [--claude-only] [--substitute "Gemini -> Opus"]
Arguments
$ARGUMENTS— The concept to explain and optional flags:--domain— Knowledge domain (physics, ai_ml, statistics, mathematics, paper). Auto-inferred if omitted.--audience— Target audience (default:phd-student):general-public— No assumed technical backgroundhigh-school— Basic math/science literacyundergraduate— Introductory college-level knowledge in the domainphd-student— Graduate-level domain knowledge (default)researcher— Active researcher familiar with the fieldexpert— Deep specialist in the exact sub-field"free text"— Any custom audience description (e.g.,"medical doctors learning ML")
--weights— JSON object of scoring weights for explanation quality ranking. Keys:clarity,accuracy,depth,accessibility,completeness,engagement. Values must sum to 1.0. If omitted, Claude analyzes the prompt and audience to recommend adaptive weights for user confirmation (see Step 0a).--depth— Controls explanation pipeline depth (default:medium):low— Skip Phase 1 entirely; Claude generates explanation directlymedium— Full MAGI (parallel brainstorm + cross-review) → explanationhigh— MAGI + adversarial debate → explanation with misconceptions sectionmax— Hierarchical MAGI-in-MAGI: N persona subagents → meta-review + debate → multi-perspective deep dive
--personas N|auto— Number of explanation-specialist subagents for--depth max(default:auto, range: 2-4). Whenauto, Claude analyzes the concept to determine the optimal persona count. Ignored for other depth levels.--claude-only— Replace all Gemini/Codex MCP calls with Claude Agent subagents. Use when external model endpoints are unavailable or for a Claude-only workflow. Two subagents with distinct cognitive styles (Creative-Divergent and Analytical-Convergent) ensure perspective diversity.--substitute "Agent -> Opus"— Replace a specific MAGI agent with Claude (Opus). Accepted:"Gemini -> Opus","Codex -> Opus". Can be specified multiple times. If both substituted, equivalent to--claude-only.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 427 lines · 0 tokens per session scan A 251e6345733f
research-explain is a skill published in the GitHub repository Axect/magi-researchers (13 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 6,593 tokens. 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
slides-polish
Per-page Codex review + targeted python-pptx / Beamer fixes for academic talk slides. Use AFTER /paper-slides (or any externally generated PPTX/Beamer) when the deck looks 'mostly OK' but the user wants a final pass that aligns visual weight with a reference, bumps PPTX fonts to projector-readable size, kills italic…
semantic-scholar
Search published venue papers (IEEE, ACM, Springer, etc.) via Semantic Scholar API. Complements /arxiv (preprints) with citation counts, venue metadata, and TLDR. Use when user says "search semantic scholar", "find IEEE papers", "find journal papers", "venue papers", "citation search", or wants published literature…
elixir-idioms
OTP/BEAM patterns and Elixir idioms — GenServer, Supervisor, Task, Registry, pattern matching, with chains, pipes. Use when designing processes or debugging BEAM issues.
learning-and-development
Builds capability — skills gaps, career frameworks, training that transfers to the job, and internal mobility. Use this to design a career ladder, close a capability gap, decide whether to build or hire a skill, structure onboarding into a role, or work out why training keeps failing to change anything.
video-perception
Use when the user mentions a video file (.mp4, .mov, .avi, .mkv, .webm), a YouTube URL, asks to watch/analyze/review a video, or references video content in conversation.
engineering-onboarding
Create an onboarding guide for an engineer joining a team that consumes the design system. Trigger when someone says: onboard new engineer, developer getting started guide, new engineer guide, engineering onboarding, first day for developers, frontend onboarding, or anything about helping an engineer new to the team…