meteora-dlmm-lp

meteora-dlmm-lp is an Investment analysis & markets skill for AI agents, published by fciaf420 in the meteora-dlmm-lp-skill repository on GitHub. Skillspace rates meteora-dlmm-lp D (62/100) and it passed the safety scan; the rating is based on static analysis only.

Expert advisor for Meteora DLMM (Dynamic Liquidity Market Maker) liquidity provision on Solana. Grounds every recommendation in live Data API numbers (real pool, position, portfolio, and PnL endpoints) before advising. Covers bin-step and liquidity-shape selection (Spot / Spot-Concentrated / Spot-Spread / Spot-Wide / Curve / Bid-Ask), dynamic and base fees, the protocol fee cut and 10% fee cap, impermanent-loss management, position sizing, and in-place rebalancing. Use this skill whenever the user asks about Meteora, DLMM, providing liquidity on Solana, concentrated-liquidity LP strategy, bin steps, liquidity shapes, dynamic fees, impermanent loss on DLMM, or managing/rebalancing DLMM positions. Also trigger for DLMM Launch Pools and launch/memecoin LPing, single-sided and DCA-in/DCA-out liquidity, DLMM native limit orders (bid/ask on-chain buy/sell walls), dynamic position resizing (widening or shrinking a live position without closing it), Collect Fee Mode (earning fees in the input…

Grade
D
Quality
62 / 100
Safety
Passed
GitHub stars
29

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

View source on GitHubfciaf420/meteora-dlmm-lp-skill

Our review

Architecture
84 / 100
Maturity
Structured: ships references or scripts
Security scan
Low-risk patterns
License
Not declared. Check the repository before reusing it.
Adoption
Adopted by 0 other repositories, in 0 collections

What the scanner flagged

  • mentions secrets in text

Not checked yet

  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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