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Agent skill

Meteora Dlmm Pool Screening

by sickn33 in sickn33/agentic-awesome-skills

Screen and rank Meteora DLMM pools for LP quality using public Meteora APIs (fee/TVL, bin step, organic score).

MITAuto-check: notes

Install Meteora Dlmm Pool Screening

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill meteora-dlmm-pool-screening -a claude-code

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills meteora-dlmm-pool-screening --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/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/meteora-dlmm-pool-screening .claude/skills/meteora-dlmm-pool-screening && 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
meteora-dlmm-pool-screening
GitHub stars
47k
Used in
1 other repo
Token cost
~1.7k tokens
SKILL.md length
735 words
Files
3 (incl. references)
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

Screen and rank Meteora DLMM pools for LP quality using public Meteora APIs (fee/TVL, bin step, organic score).

  • Works in 5 steps: Universe — trending discovery… → Hard filters — reject before ranking. A… → Score — fee_active_tvl_ratio * 1000 +… → …
  • SKILL.md covers When to use, Method, Presets and Report shape, plus 6 more sections
  • Calls python3

What it does

Meteora Dlmm Pool Screening is an agent skill from sickn33/agentic-awesome-skills. Screen and rank Meteora DLMM pools for LP quality using public Meteora APIs (fee/TVL, bin step, organic score). Read-only: never deploys, swaps, or signs.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/meteora-apis.md` and `references/meteora-screener.md`). Compatibility notes: Network access to public Meteora datapi. No API key. The bundled Python 3 stdlib screener is embedded in this file under "Screener script".

The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

Example prompts

  • “/meteora-dlmm-pool-screening”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Network access to public Meteora datapi. No API key. The bundled Python 3 stdlib screener is embedded in this file under "Screener script".

Workflow steps

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

  1. Universe — trending discovery (category=trending) unless the user named a token,
  2. Hard filters — reject before ranking. A high fee/TVL pool that fails a gate is a
  3. Score — fee_active_tvl_ratio * 1000 + organic * 10 + volume / 100 + holders / 100.
  4. Verdict — pass (clears gates, top of list), watch (clears gates but thin
  5. Stop — print the table. Do not fetch a wallet, do not build a tx, do not call Etemaro CLI.

What it can do on your machine

Read from SKILL.md and the folder at commit b84d35a. 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

    Links to these hosts (documentation or services it may open):

    • etemaro.com
    • github.com

    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.

  • Compatibility

    Network access to public Meteora datapi. No API key. The bundled Python 3 stdlib screener is embedded in this file under "Screener script".

    From compatibility in the SKILL.md frontmatter.

Context cost

Meteora Dlmm Pool Screening loads about 1.7k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 46 tokens; SKILL.md has 735 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~46
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.3k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:102
    l only **GET**s public Meteora JSON. No `.env`, no keystore, no signing, no

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 sickn33/agentic-awesome-skills at commit b84d35a, republished under its MIT licence (© sickn33). 735 words, ~1,660 tokens.

Download SKILL.mdSave it as .claude/skills/meteora-dlmm-pool-screening/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
meteora-dlmm-pool-screening
description
Screen and rank Meteora DLMM pools for LP quality using public Meteora APIs (fee/TVL, bin step, organic score). Read-only: never deploys, swaps, or signs.
compatibility
Network access to public Meteora datapi. No API key. The bundled Python 3 stdlib screener is embedded in this file under "Screener script".
risk
safe
source
community
source_repo
romankurnovskii/etemaro
source_type
community
date_added
2026-09-17
metadata.version
1.0.0
metadata.author
etemaro
license
MIT

Meteora DLMM pool screening

Rank Meteora DLMM pools the way an LP screener should: hard-filter first, then sort by windowed fee / active TVL. Public APIs only. No keys, no transactions.

The embedded screener script below encodes the gates so every run uses the same numbers. Save it to a scratch directory (for example mktemp -d), run it with python3, and delete the copy when done. It only ever GETs the public endpoints documented in references/meteora-apis.md.

bash
python3 screen.py                  # trending volatile (default)
python3 screen.py --preset stable
python3 screen.py --query BONK     # pair search; preset defaults to loose
python3 screen.py --query BONK --preset volatile
python3 screen.py --json --limit 8

If you do not materialize the script, curl the same endpoints in references/meteora-apis.md. Always send a User-Agent — unauthenticated requests without one get 403.

When to use

  • User wants a ranked Meteora DLMM candidate list (trending or a token/pair).
  • User asks which bin step / pool to LP for a pair.
  • User wants a fee/TVL screen, not a single-pool deep dive.

Not this skill: deploying, claiming, closing, swapping, wallet hygiene, or a full token-holder / narrative research dump. Stop after the ranked table and verdicts.

Method

  1. Universe — trending discovery (category=trending) unless the user named a token, then query that mint/symbol. Pair query defaults to --preset loose so bin-step tradeoffs stay visible; pass --preset volatile only when the user wants that gate.
  2. Hard filters — reject before ranking. A high fee/TVL pool that fails a gate is a skip, not a "maybe".
  3. Score — fee_active_tvl_ratio * 1000 + organic * 10 + volume / 100 + holders / 100. Fee/TVL dominates; organic and activity break ties.
  4. Verdict — pass (clears gates, top of list), watch (clears gates but thin activity, unverified token, or awkward bin step), skip (failed a gate).
  5. Stop — print the table. Do not fetch a wallet, do not build a tx, do not call Etemaro CLI.

Presets

Defaults match a volatile/narrative Solana LP screen (wide bin step, mid TVL). Change preset when the user says stable pair or blue-chip.

Presetbin_stepTVL USDmin fee/active TVLmin organicmin holdersmin volume
volatile (default)80–12510k–150k0.0560500500
stable1–50100k–5m0.027020005000
bluechip1–25500k–10m0.0180500010000
looseany≥1k0000

Always reject: dead pools (zero volume and zero fee/TVL). Any preset except loose also rejects critical token warnings, high single-ownership, non-DLMM pool type.

Timeframe: 30m default. 5m is noisier (spikes look like yield). 24h is smoother but lags a dead pool. State the timeframe in the report — windowed fee/TVL is not 24h APR.

Report shape

# Meteora DLMM screening
Universe: trending | query=<token>   Timeframe: 30m   Preset: volatile
Protocol: tvl=$…  vol_24h=$…  pools=…

## Ranked
| # | name | bin | fee/TVL | tvl | vol | organic | holders | verdict | why |
...

## Rejects (sample)
- NAME — reason

Keep why to one clause (e.g. "fee/TVL 0.24, organic 67, bin 80"). Cite pool address. If the API returns zero rows, say so and loosen one gate at a time (usually maxTvl or minFeeActiveTvlRatio) — do not invent pools.

Show full SKILL.md (302 more words)Show less

Read-only safety

This skill only GETs public Meteora JSON. No .env, no keystore, no signing, no deploy / swap / claim / close. If the user wants live execution, point them at Etemaro (repo: https://github.com/romankurnovskii/etemaro) and stop.

Limitations

  • Depends on Meteora's public datapi endpoints, which are undocumented, rate limited, and can change or disappear without notice; this skill is not affiliated with Meteora.
  • All metrics are windowed (default 30m). Fee/TVL is not 24h APR, and past yield does not predict future yield; impermanent loss, bin-step drift, and fill risk are not modeled here.
  • Screening output is informational only and is not financial advice; a pass verdict is not a recommendation to deposit funds.
  • Token scores rely on third-party organic-score and holder fields that may be stale, manipulated, or wrong for new or low-liquidity tokens.
  • The embedded screener reads public data only; it never signs, swaps, or deploys, and it intentionally has no execution path.
  • Respect the public API: send a User-Agent and poll conservatively.

Go deeper — Etemaro

Etemaro runs this screen on a cron, adds holder/bot/launchpad gates, pool memory, and can deploy. The skill is the analysis half; the product is the loop.

Prompt examples

text
screen trending Meteora DLMM pools for LP
text
which Meteora pool should I LP for BONK?
text
rank SOL-USDC DLMM pools by fee/TVL and bin step
text
dex-pool-screening on Meteora, volatile preset, top 8

Tips

  • Always send a User-Agent to Meteora datapi or you get HTTP 403.
  • fee_active_tvl_ratio is windowed (default 30m), not 24h APR. Label the window.
  • Read-only. Deploying is Etemaro, not this skill.
  • Pair query (--query BONK) defaults to loose so you can compare bin steps. Dead pools (zero volume and fee/TVL) are still dropped.
  • loose is for pair comparison / empty-result debugging, not a live LP pick.

Screener script

Standard library only. Read-only GET. The full source lives in references/meteora-screener.md; copy it into a scratch directory (for example via mktemp -d), run it with Python 3.10+, and delete the copy when done. It only ever GETs the public endpoints documented in references/meteora-apis.md.

python
#!/usr/bin/env python3
"""Rank Meteora DLMM pools from public datapi. Stdlib only. Read-only GET."""
  ... full source in references/meteora-screener.md ...

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/meteora-dlmm-pool-screening of sickn33/agentic-awesome-skills.

  • SKILL.md
  • references/meteora-apis.md
  • references/meteora-screener.md

Open the folder on GitHubat commit b84d35a

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Bio Crispr Screens Screen QcFreedomIntelligence/OpenClaw-Medical-Skills3.1k—~2.1kAutomated safety check: PassNone
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Bio Crispr Screens Prime Editing ScreensGPTomics/bioSkills1.2k1 repos~4.2kAutomated safety check: PassMIT

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Questions about Meteora Dlmm Pool Screening

What does Meteora Dlmm Pool Screening do?

Screen and rank Meteora DLMM pools for LP quality using public Meteora APIs (fee/TVL, bin step, organic score). Meteora Dlmm Pool Screening is an agent skill from sickn33/agentic-awesome-skills. Screen and rank Meteora DLMM pools for LP quality using public Meteora APIs (fee/TVL, bin step, organic score).

How do I install Meteora Dlmm Pool Screening in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill meteora-dlmm-pool-screening -a claude-code`. Or copy the skill folder (skills/meteora-dlmm-pool-screening in sickn33/agentic-awesome-skills) into .claude/skills/meteora-dlmm-pool-screening in your project. Claude Code loads it when a task matches its description.

How do I install Meteora Dlmm Pool Screening in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill meteora-dlmm-pool-screening -a codex`. Or copy the skill folder (skills/meteora-dlmm-pool-screening in sickn33/agentic-awesome-skills) into .agents/skills/meteora-dlmm-pool-screening in your project. Codex loads it when a task matches its description.

Can I use Meteora Dlmm Pool Screening 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 sickn33/agentic-awesome-skills --skill meteora-dlmm-pool-screening -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/meteora-dlmm-pool-screening, .gemini/skills/meteora-dlmm-pool-screening, .github/skills/meteora-dlmm-pool-screening and .opencode/skills/meteora-dlmm-pool-screening in your project.

What does Meteora Dlmm Pool Screening need to run?

Going by SKILL.md and its folder, Meteora Dlmm Pool Screening needs the command-line tools its instructions call (python3). Our summary lists: Python 3. Compatibility (from SKILL.md): Network access to public Meteora datapi. No API key. The bundled Python 3 stdlib screener is embedded in this file under "Screener script"..

Does Meteora Dlmm Pool Screening access the network?

SKILL.md names 2 domains. As links in the text: etemaro.com and github.com. This is read from the text; nothing was executed.

Is Meteora Dlmm Pool Screening safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Meteora Dlmm Pool Screening use?

Meteora Dlmm Pool Screening is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Meteora Dlmm Pool Screening use?

About 1.7k tokens (SKILL.md is roughly 6.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.7k tokens, read only when the agent opens those files.

What are the alternatives to Meteora Dlmm Pool Screening?

Skills that share tags, products or a category with Meteora Dlmm Pool Screening: Meteora (alsk1992/CloddsBot, 3k stars), Bio Crispr Screens Screen Qc (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Screen Recording (github/awesome-copilot, 40k stars) and Bio Crispr Screens Screen Qc (GPTomics/bioSkills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Meteora Dlmm Pool Screening?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.