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.
Quick Facts
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).
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 8If 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
# 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 — reasonKeep `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
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
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 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
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.