MR
Mayur Rathi
@sickn33
⭐ 47.3k GitHub stars

meteora-dlmm-pool-screening

meteora-dlmm-pool-screening is an code AI skill with a core value of Screen and rank Meteora DLMM pools for LP quality using public Meteora APIs (fee/TVL, bin step, organic score). It helps developers solve real-world problems in the code domain, boosting efficiency, automating repetitive tasks, and optimizing workflows.

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.

Last verified on: 2026-10-06

Quick Facts

Category code
Works With Claude
Source sickn33/antigravity-awesome-skills
Stars ⭐ 47.3k
Last Verified 2026-10-06
Risk Level Low
mkdir -p ./skills/meteora-dlmm-pool-screening && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/meteora-dlmm-pool-screening/SKILL.md -o ./skills/meteora-dlmm-pool-screening/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# 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 **GET**s the public endpoints documented in

[references/meteora-apis.md](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](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.


| Preset | bin_step | TVL USD | min fee/active TVL | min organic | min holders | min volume |

| :--- | :--- | :--- | :--- | :--- | :--- | :--- |

| `volatile` (default) | 80–125 | 10k–150k | 0.05 | 60 | 500 | 500 |

| `stable` | 1–50 | 100k–5m | 0.02 | 70 | 2000 | 5000 |

| `bluechip` | 1–25 | 500k–10m | 0.01 | 80 | 5000 | 10000 |

| `loose` | any | ≥1k | 0 | 0 | 0 | 0 |


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


text
# 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.


Read-only safety


This skill only **GET**s public Meteora JSON. No `.env`, no keystore, no signing, no

`deploy` / `swap` / `claim` / `close`. If the user wants live execution, point them at

[Etemaro](https://etemaro.com) (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

🎯 Best For

  • Claude users
  • Software engineers
  • Development teams
  • Tech leads

💡 Use Cases

  • Code quality improvement
  • Best practice enforcement

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply meteora-dlmm-pool-screening to Your Work

    Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.

  4. 4

    Review and Refine

    Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.

❓ Frequently Asked Questions

Is meteora-dlmm-pool-screening compatible with Cursor and VS Code?

Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.

Do I need specific dependencies for meteora-dlmm-pool-screening?

Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.

How do I install meteora-dlmm-pool-screening?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/meteora-dlmm-pool-screening/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Skipping validation

Always test AI-generated code changes, even for simple refactors.

Missing dependency updates

Check if the skill requires updated dependencies or new packages.

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