Agent skill

Messari Token Research

by moonpay in moonpay/skills

Full token research workflow using Messari x402 API. An agent skill from moonpay/skills.

MITAuto-check passedResearch & Science

Install Messari Token Research

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

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

GitHub CLI
$ gh skill install moonpay/skills messari-token-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-token-research .claude/skills/messari-token-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/

Facts

Skill name
messari-token-research
GitHub stars
113
Token cost
~1.4k tokens
SKILL.md length
253 words
Files
1
Skills in repo
39
Repo updated
First seen
Licence
MIT

At a glance

Full token research workflow using Messari x402 API. An agent skill from moonpay/skills.

  • Works in 6 steps: Preflight: check balance → Asset fundamentals (~$0.05) → Price timeseries (~$0.18) → …
  • Tasks that involve Deep research
  • SKILL.md covers Goal, Trigger phrases, Step 0 — Preflight: check… and Step 1 — Asset fundamentals…, plus 8 more sections
  • Reaches api.messari.io

What it does

Messari Token Research is an agent skill from moonpay/skills. Full token research workflow using Messari x402 API. Fetches asset fundamentals, price history, sentiment signals, and news, then synthesizes a research brief via Messari AI. Total cost ~$1.00–$1.50 USDC per run.

Its SKILL.md is about 1.4k 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, x402, Bitcoin and Solana. 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-token-research”

Workflow steps

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

  1. Preflight: check balance
  2. Asset fundamentals (~$0.05)
  3. Price timeseries (~$0.18)
  4. Sentiment signals (~$0.35)
  5. Recent news (~$0.55)
  6. AI research synthesis (~$0.25)

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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    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 Token Research loads about 1.4k tokens when it runs. Until then it costs about 59 tokens; SKILL.md has 253 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~59
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

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). 253 words, ~1,407 tokens.

Download SKILL.mdSave it as .claude/skills/messari-token-research/SKILL.md (or your agent's skills folder).
name
messari-token-research
description
Full token research workflow using Messari x402 API. Fetches asset fundamentals, price history, sentiment signals, and news, then synthesizes a research brief via Messari AI. Total cost ~$1.00–$1.50 USDC per run.
tags
messari, research, tokens, signals, x402, workflow

Messari Token Research Workflow

Goal

Given a token slug (e.g. bitcoin, ethereum, solana), run a 5-step research workflow that pulls fundamentals, price action, signals, news, and finishes with an AI-synthesized brief.

Total cost per run: ~$1.00–$1.50 USDC on Base

Trigger phrases

  • "Research [token]"
  • "Give me a brief on [token]"
  • "What's the outlook on [token]?"
  • "Deep dive on [token]"

Step 0 — Preflight: check balance

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

Ensure at least $2.00 USDC on Base before starting. If low:

bash
# Bridge USDC from Ethereum to Base
mp token bridge \
  --wallet main \
  --from-chain ethereum \
  --to-chain base \
  --token usdc \
  --amount 10

Step 1 — Asset fundamentals (~$0.05)

Replace {slug} with the token identifier (e.g. bitcoin, solana):

bash
mp x402 request \
  --method GET \
  --url "https://api.messari.io/v2/assets/details?assets={slug}" \
  --wallet main \
  --chain base

Extract: name, symbol, market cap, circulating supply, max supply, category, description, ATH, current price.


Step 2 — Price timeseries (~$0.18)

bash
mp x402 request \
  --method GET \
  --url "https://api.messari.io/v1/assets/timeseries/{slug}?granularity=daily&start_date=$(date -d "30 days ago" +%Y-%m-%d 2>/dev/null || date -v-30d +%Y-%m-%d)&end_date=$(date +%Y-%m-%d)" \
  --wallet main \
  --chain base

Extract: 30-day price trend, volatility pattern, notable pumps/dumps.


Step 3 — Sentiment signals (~$0.35)

bash
mp x402 request \
  --method GET \
  --url "https://api.messari.io/signal/v1/assets?assetSlug={slug}" \
  --wallet main \
  --chain base

Extract: mindshare score, sentiment direction, social volume trend.


Step 4 — Recent news (~$0.55)

bash
mp x402 request \
  --method GET \
  --url "https://api.messari.io/news/v1/news/feed?assets={slug}&limit=10" \
  --wallet main \
  --chain base

Extract: top 5 headlines, publication dates, sentiment of coverage.


Step 5 — AI research synthesis (~$0.25)

Feed all data from Steps 1–4 into Messari AI for a structured brief:

bash
mp x402 request \
  --method POST \
  --url "https://api.messari.io/ai/v2/chat/completions" \
  --body '{
    "model": "messari",
    "messages": [
      {
        "role": "system",
        "content": "You are a crypto research analyst. Given asset data, price action, signals, and news, produce a structured research brief with: 1) Summary, 2) Key metrics, 3) Bullish/bearish factors, 4) Risk factors, 5) Outlook."
      },
      {
        "role": "user",
        "content": "Research brief for {slug}. Fundamentals: {step1_output}. Price trend (30d): {step2_summary}. Signals: {step3_output}. News: {step4_headlines}"
      }
    ]
  }' \
  --wallet main \
  --chain base

Full workflow script

bash
#!/bin/bash
# messari-research.sh <slug>
# Usage: ./messari-research.sh bitcoin

SLUG="${1:-bitcoin}"
WALLET="main"
CHAIN="base"
BASE="https://api.messari.io"
OUT="$HOME/.config/moonpay/research/messari-${SLUG}-$(date -u +%Y%m%d-%H%M%S)"
mkdir -p "$(dirname "$OUT")"

echo "=== [1/4] Asset Fundamentals ==="
FUNDAMENTALS=$(mp x402 request --method GET \
  --url "${BASE}/v2/assets/details?assets=${SLUG}" \
  --wallet "$WALLET" --chain "$CHAIN")
echo "$FUNDAMENTALS" > "${OUT}-fundamentals.json"

echo "=== [2/4] Price Timeseries (30d) ==="
TIMESERIES=$(mp x402 request --method GET \
  --url "${BASE}/v1/assets/timeseries/${SLUG}?granularity=daily" \
  --wallet "$WALLET" --chain "$CHAIN")
echo "$TIMESERIES" > "${OUT}-timeseries.json"

echo "=== [3/4] Signals ==="
SIGNALS=$(mp x402 request --method GET \
  --url "${BASE}/signal/v1/assets?assetSlug=${SLUG}" \
  --wallet "$WALLET" --chain "$CHAIN")
echo "$SIGNALS" > "${OUT}-signals.json"

echo "=== [4/4] News ==="
NEWS=$(mp x402 request --method GET \
  --url "${BASE}/news/v1/news/feed?assets=${SLUG}&limit=10" \
  --wallet "$WALLET" --chain "$CHAIN")
echo "$NEWS" > "${OUT}-news.json"

echo ""
echo "Research data saved to ${OUT}-*.json"
echo "Total cost: ~\$1.13 USDC"
echo ""
echo "Next: pass this data to Messari AI for synthesis (Step 5)"

Output format

Present the final brief to the user as:

## [TOKEN] Research Brief
**Date:** [today]
**Cost:** ~$1.13 USDC

### Summary
[2-3 sentence overview]

### Key Metrics
- Price: $X (ATH: $Y, -Z% from ATH)
- Market Cap: $X (rank #N)
- 30d Performance: +/-X%
- Mindshare Score: X (trend: ↑/↓)

### Bullish Factors
- [factor 1]
- [factor 2]

### Bearish / Risk Factors
- [factor 1]
- [factor 2]

### Outlook
[1-2 sentence assessment]

Notes

  • Slug format: lowercase, hyphenated — bitcoin, ethereum, solana, chainlink
  • For very new tokens, Steps 2–3 may return empty — skip gracefully
  • AI synthesis (Step 5) can use all saved JSON files if running interactively
  • Payments are in USDC on Base — ensure ETH on Base for gas
  • messari-x402 — Core endpoint reference
  • messari-alpha-scout — Find trending tokens before researching them
  • messari-deep-research — Deeper async report (10–15 min, more comprehensive)
  • moonpay-swap-tokens — Act on research by swapping tokens
  • moonpay-check-wallet — Verify USDC balance before running

© moonpay, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

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

Open the folder on GitHubat commit aa672ab

Compare with similar skills

Messari Token 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 Token Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Messari Token Research this skillmoonpay/skills113—~1.4kAutomated safety check: PassMIT
Minara Crypto Trading and WalletMinara-AI/minara-skills362—~5.7kAutomated safety check: PassNone
BlockrunBlockRunAI/blockrun-mcp391—~2.7kAutomated safety check: PassMIT
Solana Payments Wallets Tradingnpc-live/clawfirm1561 repos~4.7kAutomated safety check: PassMIT
Payram Crypto PaymentsPayRam/payram-mcp158—~2kAutomated safety check: NotesNone
Metengine Data Agentinternet-court/internet-court-skill6.5k1 repos~19kAutomated safety check: PassApache-2.0

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Questions about Messari Token Research

What does Messari Token Research do?

Full token research workflow using Messari x402 API. An agent skill from moonpay/skills. Messari Token Research is an agent skill from moonpay/skills. Full token research workflow using Messari x402 API.

When should I use Messari Token Research?

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

How do I install Messari Token Research in Claude Code?

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

How do I install Messari Token Research in Codex?

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

Can I use Messari Token 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-token-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-token-research, .gemini/skills/messari-token-research, .github/skills/messari-token-research and .opencode/skills/messari-token-research in your project.

What does Messari Token Research need to run?

SKILL.md names no scripts, command-line tools or credentials: Messari Token Research is instructions for the agent only.

Does Messari Token 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 Token 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 Token Research use?

Messari Token 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 Token Research use?

About 1.4k tokens (SKILL.md is roughly 5.6k 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 Token Research?

Skills that share tags, products or a category with Messari Token Research: Minara Crypto Trading and Wallet (Minara-AI/minara-skills, 362 stars), Blockrun (BlockRunAI/blockrun-mcp, 391 stars), Solana Payments Wallets Trading (npc-live/clawfirm, 156 stars) and Payram Crypto Payments (PayRam/payram-mcp, 158 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Messari Token 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.