Agent skill

Messari Deep Research

Async deep-research report workflow using Messari AI. An agent skill from moonpay/skills.

Install Messari Deep Research

skills CLI

$ npx skills add moonpay/skills --skill messari-deep-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install moonpay/skills messari-deep-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/moonpay/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/messari-deep-research .claude/skills/messari-deep-research && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "messari-deep-research" agent skill from https://github.com/moonpay/skills/tree/main/skills/messari-deep-research into .claude/skills/messari-deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "messari-deep-research", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/moonpay/skills/tree/main/skills/messari-deep-research

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add moonpay/skills --skill messari-deep-research -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install moonpay/skills messari-deep-research --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/moonpay/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/messari-deep-research .agents/skills/messari-deep-research && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "messari-deep-research" agent skill from https://github.com/moonpay/skills/tree/main/skills/messari-deep-research into .agents/skills/messari-deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "messari-deep-research", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add moonpay/skills --skill messari-deep-research -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install moonpay/skills messari-deep-research --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/moonpay/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/messari-deep-research .cursor/skills/messari-deep-research && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "messari-deep-research" agent skill from https://github.com/moonpay/skills/tree/main/skills/messari-deep-research into .cursor/skills/messari-deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "messari-deep-research", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/moonpay/skills.git --path skills/messari-deep-research

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add moonpay/skills --skill messari-deep-research -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install moonpay/skills messari-deep-research --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/moonpay/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/messari-deep-research .gemini/skills/messari-deep-research && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "messari-deep-research" agent skill from https://github.com/moonpay/skills/tree/main/skills/messari-deep-research into .gemini/skills/messari-deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "messari-deep-research", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install moonpay/skills messari-deep-research

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add moonpay/skills --skill messari-deep-research -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/moonpay/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/messari-deep-research .github/skills/messari-deep-research && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "messari-deep-research" agent skill from https://github.com/moonpay/skills/tree/main/skills/messari-deep-research into .github/skills/messari-deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "messari-deep-research", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add moonpay/skills --skill messari-deep-research -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install moonpay/skills messari-deep-research --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/moonpay/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/messari-deep-research .opencode/skills/messari-deep-research && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "messari-deep-research" agent skill from https://github.com/moonpay/skills/tree/main/skills/messari-deep-research into .opencode/skills/messari-deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "messari-deep-research", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: messari-deep-research
GitHub stars: 113
Token cost: ~1.6k tokens
SKILL.md length: 394 words
Files: 1
Skills in repo: 39
Repo updated: Sep 10, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Async deep-research report workflow using Messari AI. An agent skill from moonpay/skills.

Works in 4 steps: Preflight: check balance → Start deep research job → Poll for completion (free, repeat until… → …
Tasks that involve Deep research
SKILL.md covers Goal, Trigger phrases, Step 0 — Preflight: check… and Step 1 — Start deep research job, plus 9 more sections
Calls python3; reaches api.messari.io

What it does

Messari Deep Research is an agent skill from moonpay/skills. Async deep-research report workflow using Messari AI. Starts a long-form research job, polls until complete, and returns a comprehensive report on any crypto topic, asset, or protocol. Cost varies; typically $0.50–$2.00 USDC.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Deep research. It works with Circle USDC. The repository describes itself as: Skills for AI agents to move money — on-ramps, swaps, wallets, deposits, and more via the MoonPay CLI. The licence is MIT.

When your agent uses it

Tasks that involve Deep research

Example prompts

“/messari-deep-research”

Requirements

Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

Preflight: check balance
Start deep research job
Poll for completion (free, repeat until done)
Retrieve completed report

What it can do on your machine

Read from SKILL.md and the folder at commit aa672ab. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- api.messari.io
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Messari Deep Research loads about 1.6k tokens when it runs. Until then it costs about 62 tokens; SKILL.md has 394 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~62
When it runs · the whole SKILL.md, loaded when a task matches: ~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from moonpay/skills at commit aa672ab, republished under its MIT licence (© moonpay). 394 words, ~1,552 tokens.

Download SKILL.mdSave it as .claude/skills/messari-deep-research/SKILL.md (or your agent's skills folder).

name: messari-deep-research
description: Async deep-research report workflow using Messari AI. Starts a long-form research job, polls until complete, and returns a comprehensive report on any crypto topic, asset, or protocol. Cost varies; typically $0.50–$2.00 USDC.
tags: messari, research, ai, deep-research, x402, workflow

Messari Deep Research Workflow

Goal

Generate a comprehensive, long-form research report on any crypto topic, asset, sector, or question. Uses Messari's async AI deep-research engine which crawls their knowledge graph, on-chain data, and news to produce institutional-quality reports.

Cost per run: ~$0.50–$2.00 USDC on Base (async, takes 5–15 minutes)

Trigger phrases

"Write a deep research report on [topic]"
"Give me a full analysis of [asset/protocol]"
"Research the [DeFi/L2/RWA/etc.] sector"
"Deep dive on [topic]"
"Generate a research report"

Step 0 — Preflight: check balance

bash

mp token balance list --wallet main --chain base --json

Ensure at least $3.00 USDC on Base.

Step 1 — Start deep research job

bash

mp x402 request \
  --method POST \
  --url "https://api.messari.io/ai/v1/deep-research" \
  --body '{
    "query": "<your research question or topic>"
  }' \
  --wallet main \
  --chain base

Example queries:

"What is the investment thesis for Ethereum restaking protocols in 2025?"
"Analyze the competitive landscape of L2 rollups"
"What are the key risks in the RWA tokenization sector?"
"Deep dive on Solana's DeFi ecosystem"

Response: Returns a job id (e.g. dr_abc123) and status pending.

json

{
  "id": "dr_abc123",
  "status": "pending",
  "created_at": "2025-..."
}

Step 2 — Poll for completion (free, repeat until done)

bash

mp x402 request \
  --method GET \
  --url "https://api.messari.io/ai/v1/deep-research/<JOB_ID>" \
  --wallet main \
  --chain base

Status values:

pending — job queued, try again in 60s
processing — actively generating, try again in 60s
completed — report ready, extract result
failed — job failed, check error field

Polling is free (no payment required). Poll every 30–60 seconds.

Step 3 — Retrieve completed report

Once status is completed, the response contains the full report:

json

{
  "id": "dr_abc123",
  "status": "completed",
  "result": {
    "title": "...",
    "summary": "...",
    "sections": [...],
    "sources": [...]
  }
}

Full workflow script (with auto-polling)

bash

#!/bin/bash
# messari-deep-research.sh "<research query>"
# Usage: ./messari-deep-research.sh "Analyze the Solana DeFi ecosystem"

QUERY="${1:-What are the top crypto narratives for 2025?}"
WALLET="main"
CHAIN="base"
BASE="https://api.messari.io"
OUT="$HOME/.config/moonpay/research/deep-$(date -u +%Y%m%d-%H%M%S)"
mkdir -p "$(dirname "$OUT")"

echo "Starting deep research: \"$QUERY\""
echo ""

# Step 1: Start job
RESPONSE=$(mp x402 request --method POST \
  --url "${BASE}/ai/v1/deep-research" \
  --body "{\"query\": \"${QUERY}\"}" \
  --wallet "$WALLET" --chain "$CHAIN")
echo "$RESPONSE" > "${OUT}-job.json"

JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['id'])" 2>/dev/null)

if [ -z "$JOB_ID" ]; then
  echo "ERROR: Could not extract job ID. Response:"
  echo "$RESPONSE"
  exit 1
fi

echo "Job started: $JOB_ID"
echo "Polling for completion (this takes 5–15 minutes)..."

# Step 2: Poll loop
while true; do
  sleep 30
  STATUS_RESPONSE=$(mp x402 request --method GET \
    --url "${BASE}/ai/v1/deep-research/${JOB_ID}" \
    --wallet "$WALLET" --chain "$CHAIN")

  STATUS=$(echo "$STATUS_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin).get('status','unknown'))" 2>/dev/null)
  echo "  Status: $STATUS ($(date +%H:%M:%S))"

  if [ "$STATUS" = "completed" ]; then
    echo "$STATUS_RESPONSE" > "${OUT}-report.json"
    echo ""
    echo "Report complete! Saved to ${OUT}-report.json"
    break
  elif [ "$STATUS" = "failed" ]; then
    echo "ERROR: Research job failed."
    echo "$STATUS_RESPONSE"
    exit 1
  fi
done

Cancel a running job

bash

mp x402 request \
  --method POST \
  --url "https://api.messari.io/ai/v1/deep-research/<JOB_ID>/cancel" \
  --wallet main \
  --chain base

List past research jobs

bash

mp x402 request \
  --method GET \
  --url "https://api.messari.io/ai/v1/deep-research" \
  --wallet main \
  --chain base

Output format

Present the completed report as:

## Deep Research: [Report Title]
**Job ID:** dr_abc123
**Generated:** [timestamp]
**Cost:** ~$X.XX USDC

### Executive Summary
[2-3 paragraph summary]

### [Section 1 Title]
[content]

### [Section 2 Title]
[content]

...

### Sources
- [source 1]
- [source 2]

Show full SKILL.md (172 more words)Show less

Good research queries

Asset deep dives:

"Investment thesis for [TOKEN]: fundamentals, competition, risks, and outlook"
"On-chain health of [TOKEN]: activity, retention, revenue"

Sector analysis:

"Competitive landscape of Ethereum L2 rollups in 2025"
"State of RWA tokenization: market size, key players, regulatory risks"
"DeFi lending protocols: risk comparison across Aave, Compound, and Morpho"

Narrative research:

"What is the AI x crypto narrative and which projects are best positioned?"
"Is the memecoin supercycle over? Evidence and counterarguments"

Macro:

"How does the current macro environment affect crypto market structure?"

Notes

Deep research is async — start the job, then come back; do not busy-wait
Report quality is significantly higher than single-shot AI chat
If polling for >20 minutes with processing status, the job may be stuck — cancel and retry
Payments in USDC on Base (--chain base)
Free polling: Step 2 GET requests do not trigger payment

messari-x402 — Core endpoint reference and quick AI chat (/ai/v2/chat/completions)
messari-token-research — Faster, cheaper token research using multiple data endpoints
messari-alpha-scout — Surface topics worth deep-researching
messari-funding-intel — Complement with funding data

Files

Just SKILL.md in skills/messari-deep-research of moonpay/skills.

Open the folder on GitHubat commit aa672ab

Compare with similar skills

Messari Deep Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Messari Deep Research compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Messari Deep Research this skillmoonpay/skills	113	—	~1.6k	Automated safety check: Pass	MIT	29 days ago
GitHub Deep Researchbytedance/deer-flow	84k	4 repos	~1.3k	Automated safety check: Pass	MIT	today
Deep Research WorkflowTokenRhythm/opensquilla	7.1k	—	~1.3k	Automated safety check: Pass	Apache-2.0	today
Deep Researchsanjay3290/ai-skills	431	9 repos	~683	Automated safety check: Notes	Apache-2.0	28 days ago
Horizontal-Vertical Deep ResearchKKKKhazix/khazix-skills	21k	—	~2.1k	Automated safety check: Pass	MIT	8 days ago
Academic Research PipelineImbad0202/academic-research-skills	51k	—	~15k	Automated safety check: Pass	Custom licence	today

Similar skills

GitHub Deep Research
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
84k GitHub starsUsed in 4 repos~1.3k tokens
Research & ScienceAuto-check passed
Deep Research Workflow
TokenRhythm/opensquilla
Runs multi-round research in three stages with a persisted state file, evidence tracking and a long-form report with per-claim citations.
7.1k GitHub stars~1.3k tokensUpdated today
Research & ScienceAuto-check passed
Deep Research
sanjay3290/ai-skills
Execute autonomous multi-step research using Google Gemini Deep Research Agent.
431 GitHub starsUsed in 9 repos~683 tokens
Research & ScienceAuto-check: notes
Horizontal-Vertical Deep Research
KKKKhazix/khazix-skills
Runs a two-axis deep research method on a product, company, concept or person: its full history over time, compared with peers today, delivered as a typeset PDF report.
21k GitHub stars~2.1k tokensUpdated 8 days ago
Research & ScienceAuto-check passed
Academic Research Pipeline
Imbad0202/academic-research-skills
Orchestrates a ten-stage academic workflow from research to finished manuscript, including integrity checks, two rounds of peer review and revision.
51k GitHub stars~15k tokensUpdated today
Research & ScienceAuto-check passed
Academic Research Suite for Codex
Imbad0202/academic-research-skills-codex
A router skill that sends academic work such as literature reviews, drafting, citation checks, peer review and revision to the right workflow in the ARS suite.
12k GitHub stars~12k tokensUpdated 6 days ago
Research & ScienceAuto-check passed

Questions about Messari Deep Research

What does Messari Deep Research do?

Async deep-research report workflow using Messari AI. An agent skill from moonpay/skills. Messari Deep Research is an agent skill from moonpay/skills. Async deep-research report workflow using Messari AI.

When should I use Messari Deep Research?

Messari Deep Research fits situations like: tasks that involve Deep research.

How do I install Messari Deep Research in Claude Code?

Run `npx skills add moonpay/skills --skill messari-deep-research -a claude-code`. Or copy the skill folder (skills/messari-deep-research in moonpay/skills) into .claude/skills/messari-deep-research in your project. Claude Code loads it when a task matches its description.

How do I install Messari Deep Research in Codex?

Run `npx skills add moonpay/skills --skill messari-deep-research -a codex`. Or copy the skill folder (skills/messari-deep-research in moonpay/skills) into .agents/skills/messari-deep-research in your project. Codex loads it when a task matches its description.

Can I use Messari Deep Research in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add moonpay/skills --skill messari-deep-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/messari-deep-research, .gemini/skills/messari-deep-research, .github/skills/messari-deep-research and .opencode/skills/messari-deep-research in your project.

What does Messari Deep Research need to run?

Going by SKILL.md and its folder, Messari Deep Research needs the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Messari Deep Research access the network?

SKILL.md names 1 domain. In commands or code: api.messari.io; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Messari Deep Research safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Messari Deep Research use?

Messari Deep Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Messari Deep Research use?

About 1.6k tokens (SKILL.md is roughly 6.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Messari Deep Research?

Skills that share tags, products or a category with Messari Deep Research: GitHub Deep Research (bytedance/deer-flow, 84k stars), Deep Research Workflow (TokenRhythm/opensquilla, 7.1k stars), Deep Research (sanjay3290/ai-skills, 431 stars) and Horizontal-Vertical Deep Research (KKKKhazix/khazix-skills, 21k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Messari Deep Research?

moonpay (a GitHub organization) maintains it in moonpay/skills, which has 113 GitHub stars. The repository holds 39 skills in this directory. The repository was last updated on September 10, 2026.

Source: moonpay/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Messari Deep Research

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Messari Deep Research Workflow

Goal

Trigger phrases

Step 0 — Preflight: check balance

Step 1 — Start deep research job

Step 2 — Poll for completion (free, repeat until done)

Step 3 — Retrieve completed report

Full workflow script (with auto-polling)

Cancel a running job

List past research jobs

Output format

Good research queries

Notes

Related skills

Files

Compare with similar skills

Similar skills

More from moonpay/skills

Related

Questions about Messari Deep Research