Agent skill

Nansen Token Screener

by nansen-ai in nansen-ai/nansen-cli

Discover trending tokens — screener, SM holdings, Nansen indicators, and flow intelligence for promising finds.

MITAuto-check passedBackend & APIs

Install Nansen Token Screener

skills CLI
$ npx skills add nansen-ai/nansen-cli --skill nansen-token-screener -a claude-code

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

GitHub CLI
$ gh skill install nansen-ai/nansen-cli nansen-token-screener --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/nansen-ai/nansen-cli.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nansen-token-screener .claude/skills/nansen-token-screener && 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
nansen-token-screener
GitHub stars
139
Token cost
~2.5k tokens
SKILL.md length
1,007 words
Files
1
Skills in repo
32
Repo updated
First seen
Licence
MIT

At a glance

Discover trending tokens — screener, SM holdings, Nansen indicators, and flow intelligence for promising finds.

  • Works in 4 steps: performance_score DESC → market_cap_group priority (largecap →… → risk_score DESC → …
  • Scanning for new tokens
  • SKILL.md covers Authentication and Top tokens — Nansen Score…
  • Needs NANSEN_API_KEY

What it does

Nansen Token Screener is an agent skill from nansen-ai/nansen-cli. Discover trending tokens — screener, SM holdings, Nansen indicators, and flow intelligence for promising finds. Use when scanning for new tokens or screening what's hot.

Its SKILL.md is about 2.5k 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 Backend & APIs. The licence is MIT.

When your agent uses it

  • Scanning for new tokens
  • Screening whats hot

Example prompts

  • “/nansen-token-screener”

Requirements

  • A credential in NANSEN_API_KEY
  • Pre-approved tools (allowed-tools): Bash(nansen:*)

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. performance_score DESC
  2. market_cap_group priority (largecap → midcap → lowcap)
  3. risk_score DESC
  4. 24h volume DESC

What it can do on your machine

Read from SKILL.md and the folder at commit d097942. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(nansen:*)

    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

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • NANSEN_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nansen Token Screener loads about 2.5k tokens when it runs. Until then it costs about 48 tokens; SKILL.md has 1,007 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~48
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

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 nansen-ai/nansen-cli at commit d097942, republished under its MIT licence (© nansen-ai). 1,007 words, ~2,485 tokens.

Download SKILL.mdSave it as .claude/skills/nansen-token-screener/SKILL.md (or your agent's skills folder).
name
nansen-token-screener
description
Discover trending tokens — screener, SM holdings, Nansen indicators, and flow intelligence for promising finds. Use when scanning for new tokens or screening what's hot.
allowed-tools
Bash(nansen:*)

Authentication

Browser login requests nansen:api for API-key-equivalent account API permissions; existing OAuth/MCP nansen:read semantics and separate wallet authorization are unchanged.

Before any research command or loop, require an explicitly selected API key or saved browser session. Run nansen auth status first. Its cached/unverified metadata does not prove credential validity or unlocked storage. Cached access-token expiry alone does not mean the session is unusable: the CLI normally renews a selected session automatically during an already-authorized research task, without another consent request or a separate account check. Stop on anonymous selection, invalid authentication state, blocked or uncertain renewal/cleanup, or an actual authentication failure, including rejected or expired refresh authority. Follow the CLI error guidance; use the free nansen account check when troubleshooting calls for it. Do not unset a failed key, erase a session or switch to anonymous access to retry.

Use nansen login for fresh browser approval within the documented platform scope, or configure a conventional API key. NANSEN_API_KEY overrides the saved session. OpenClaw's optional primaryEnv mapping preserves configured API-key injection; it is not a required-key gate or proof of authentication. Normal credits and entitlements apply. Login does not purchase credits. Browser login is available in CLI 2.0.0 within the macOS arm64 preview scope.

API keys and browser sessions use the same automatic x402 payment behavior: a supported HTTP 402 challenge can spend funds from the configured wallet under existing wallet authorization, payment policy and spending limits. Each call, including calls in loops, can incur a payment. Authentication, authorization and session-renewal failures never trigger payment; login verification and nansen account never pay automatically. Anonymous x402 remains available as a separate paid workflow requiring explicit user intent and payment setup. Do not run this research workflow anonymously or switch to anonymous access after authentication fails.

Token Discovery

Answers: "What tokens are trending and worth a deeper look?"

bash
CHAIN=solana

# Screen top tokens by volume
nansen research token screener --chain $CHAIN --timeframe 24h --limit 20
# → token_symbol, price_usd, price_change, volume, buy_volume, market_cap_usd, fdv, liquidity, token_age_days

# Smart money only
nansen research token screener --chain $CHAIN --timeframe 24h --smart-money --limit 20

# Search within screener results (client-side filter over the fetched candidates).
# Check _meta.search: complete=false means lower-ranked tokens were not searched —
# widen with --limit 1000 or --paginate, or narrow with --filters.
nansen research token screener --chain $CHAIN --search "bonk"

# Smart money holdings — what SM wallets are holding
nansen research smart-money holdings --chain $CHAIN --labels "Smart Trader" --limit 20
# → token_symbol, value_usd, holders_count, balance_24h_percent_change, share_of_holdings_percent

# Nansen indicators for a specific token
TOKEN=<address>
nansen research token indicators --token $TOKEN --chain $CHAIN
# → risk_indicators, reward_indicators (each with score, signal, signal_percentile)

# Flow intelligence — only use for promising tokens from screener/indicators above
nansen research token flow-intelligence --token $TOKEN --chain $CHAIN
# → net_flow_usd per label: smart_trader, whale, exchange, fresh_wallets, public_figure

# Nansen Score Top Tokens — "what should I buy?" (account API endpoint; normal entitlements apply)
# Use this FIRST for discovery, then drill into individual tokens with `indicators` above
nansen research token top-tokens --limit 25
nansen research token top-tokens --market-cap largecap --limit 10
# → chain, token_address, token_symbol, performance_score, risk_score,
#   per-indicator contributions, market_cap_group, latest_date, last_trigger_on

Screener timeframes: 5m, 10m, 1h, 6h, 24h, 7d, 30d

Indicators: score is "bullish"/"bearish"/"neutral". signal_percentile > 70 = historically significant. Some tokens return empty indicators — not an error.

Top tokens — Nansen Score field reference

Results are pre-filtered to performance_score >= 15 server-side and returned sorted by:

  1. performance_score DESC
  2. market_cap_group priority (largecap → midcap → lowcap)
  3. risk_score DESC
  4. 24h volume DESC

So row 0 is always the strongest candidate for the filter you applied — no client-side ranking needed.

Market cap buckets (used in both the sort priority and the --market-cap filter):

  • lowcap: market cap < $100M
  • midcap: market cap $100M – $1B
  • largecap: market cap > $1B

Every contribution is ternary — exactly one of {negative, 0, positive} per field. No partial values. Zero means "indicator didn't apply to this token" (out of scope), not "indicator was neutral".

Performance Score (Alpha — "likely to outperform BTC over 7–30d") Range: -60 to +75 (arithmetic bounds; live max is closer to +45 since no single token hits every positive indicator simultaneously). Buy threshold: >= 15. Sum of the five *_performance fields below.

FieldContributionTriggerWhat the underlying indicator measures
price_momentum_performance+30 / 0upstream score bullish → +30Price momentum, scored against separate thresholds for large-cap vs. low/mid-cap tokens.
chain_fees_performance+30 / 0bullish (30-day fee growth > +1%) → +3030-day spending momentum on network fees (geometric mean of daily returns). Only tracked for a handful of L1 native tokens (e.g. ETH, TRX, AVAX, RON); always 0 for every other token.
trading_range_performance+15 / 0bullish (price breaks above resistance in an uptrend) → +1514-day price trend combined with position vs. nearest support/resistance. In practice fires mostly on established tokens that have well-defined levels — can fire at any market cap, but is rare for new / low-liquidity tokens.
chain_tvl_performance0 / -35bearish (composite TVL growth < 0) → -35TVL momentum composite signal. Only non-zero for chains / L2s whose TVL is tracked. No positive path exists — the field only deducts.
protocol_fees_performance0 / -25bearish (14-day fee growth < -3%) → -2514-day protocol fee momentum. Only non-zero for tokens backed by protocols with measurable fee revenue. No positive path — deduction only.
Show full SKILL.md (361 more words)Show less

Risk Score (Safety — "filters falling knives / dangerous setups") Range: -60 to +80 (arithmetic bounds). Safety threshold: > 0 (positive = safer, negative = riskier). Sum of the four *_risk fields below. For every risk field: upstream score low → positive contribution, high → negative contribution, medium/missing → 0.

FieldContributionWhat the underlying indicator measures
btc_reflexivity_risk+40 / -20Rolling 5-event median ratio of token drop to BTC drop on days BTC falls >3%. Ratio ≤ 1 → low → +40 (token holds up as well as or better than BTC on drawdowns). Ratio > 1 → high → -20 (token drops harder than BTC). Skipped for stablecoins and tokens with <$1M 24h volume.
liquidity_risk+20 / -20Ratio of on-chain liquidity to market cap (total_liquidity_usd / market_cap_usd). Higher ratio → low → +20 (deep books relative to cap). Very thin ratio → high → -20.
concentration_risk+10 / -10Top-10 holder concentration as a fraction of supply. < 0.12 → low → +10 (well-distributed). > 0.55 → high → -10 (whale-concentrated).
inflation_risk+10 / -10EMA of daily token supply inflation rate. Negative / near-zero → low → +10 (stable or deflationary supply). Strongly positive → high → -10 (high dilution). Only evaluated for tokens >= $100M market cap.

Other response fields:

  • market_cap_group: lowcap / midcap / largecap — see thresholds above.
  • latest_date: ISO datetime of the most recent indicator refresh for this token.
  • last_trigger_on: ISO datetime of the most recent trigger across contributing indicators (MAX aggregate — individual indicators may be days-to-months stale even when this looks fresh). Use indicators on a specific token to audit per-indicator ages.

Stablecoins rank high but aren't picks. USDC, USDT, DAI, FDUSD and similar score well on chain_fees + liquidity indicators but aren't what "what should I buy" means. Filter them out of the shortlist using the canonical whitelist at nansen-dbt-ch-tokens/seeds/stablecoins_for_indicator.csv before drilling into indicators.

Typical workflow: start with top-tokens for a shortlist → drop stablecoins → run indicators on the top 3–5 to inspect individual signals and their signal_percentile → flow-intelligence only on the finalists to confirm SM conviction.

Field meanings and contribution mappings above are sourced from nansen-dbt-ch-tokens/models/indicators/api_nansen_score_indicators_all_tokens_latest.sql and per-indicator model yml files. Sign conventions and live value ranges were validated against production ClickHouse data.

Flow intelligence is credit-heavy. Use it to confirm SM conviction on tokens that already look promising from screener + indicators, not as a first pass on every token.

© nansen-ai, 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/nansen-token-screener of nansen-ai/nansen-cli.

Open the folder on GitHubat commit d097942

Compare with similar skills

Nansen Token Screener 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.

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Sub2API AdminWei-Shaw/sub2api44k1 repos~717Automated safety check: PassLGPL-3.0
Firecrawl Build Onboardingfirecrawl/firecrawl190k1 repos~1.4kAutomated safety check: NotesISC
Obsidian BasesAtmosphere/atmosphere3.8k22 repos~3.2kAutomated safety check: PassApache-2.0

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Categories

Questions about Nansen Token Screener

What does Nansen Token Screener do?

Discover trending tokens — screener, SM holdings, Nansen indicators, and flow intelligence for promising finds. Nansen Token Screener is an agent skill from nansen-ai/nansen-cli. Discover trending tokens — screener, SM holdings, Nansen indicators, and flow intelligence for promising finds.

When should I use Nansen Token Screener?

Nansen Token Screener fits situations like: scanning for new tokens; screening whats hot.

How do I install Nansen Token Screener in Claude Code?

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

How do I install Nansen Token Screener in Codex?

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

Can I use Nansen Token Screener 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 nansen-ai/nansen-cli --skill nansen-token-screener -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nansen-token-screener, .gemini/skills/nansen-token-screener, .github/skills/nansen-token-screener and .opencode/skills/nansen-token-screener in your project.

What does Nansen Token Screener need to run?

Going by SKILL.md and its folder, Nansen Token Screener needs credentials named NANSEN_API_KEY. Our summary lists: A credential in NANSEN_API_KEY. Its frontmatter pre-approves these tools: Bash(nansen:*).

Does Nansen Token Screener access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Nansen Token Screener 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 Nansen Token Screener use?

Nansen Token Screener 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 Nansen Token Screener use?

About 2.5k tokens (SKILL.md is roughly 9.9k 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 Nansen Token Screener?

Skills that share tags, products or a category with Nansen Token Screener: Configuring Horizon (coollabsio/coolify, 63k stars), Nestjs Best Practices (rolling-scopes/rsschool-app, 10k stars), Sub2API Admin (Wei-Shaw/sub2api, 44k stars) and Firecrawl Build Onboarding (firecrawl/firecrawl, 190k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nansen Token Screener?

nansen-ai (a GitHub organization) maintains it in nansen-ai/nansen-cli, which has 139 GitHub stars. The repository holds 32 skills in this directory. The repository was last updated on October 9, 2026.

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