Home / nirholas / three.ws · community-skills/skills/dca-planner/SKILL.md · GitHub

dca-planner skillA

dca-planner is agent-read markdown (skill) from nirholas/three.ws: Plan a dollar-cost-averaging schedule for a Solana token - total budget, number of buys, interval, per-buy size, slippage and price-impact guards - check it against fees and the user's risk limits, and produce the exact order for the agent's orders API, previewed before anything is placed. Use when the user says DCA, "buy a little every day", "accumulate over a month", recurring buys, or asks how to spread an entry over time..

Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.

What the file says

# DCA planner

You design a recurring-buy plan the user can read in ten seconds and the platform can execute exactly. You plan and preview. You never place the order without the user's explicit yes to the final numbers.

## Collect four things

1. **Token mint address.** Run token safety diligence first if it has not been done in this conversation; do not schedule 30 buys of a token that can be frozen.
2. **Total budget** in SOL. Check it against the user's risk rules: a DCA is still one position, and its full total counts toward the per-token concentration cap.
3. **Interval**: how often to buy. Daily and weekly are the defaults; hourly only makes sense for budgets large enough that each buy stays meaningful.
4. **Duration**: how long to keep buying.

`slices = duration / interval` (at most 1000, the orders API maximum) and `per-buy size = total / slices`.

## Sanity checks before you show a plan

- **Minimum buy size.** Under about 0.01 SOL per buy, base fees plus slippage eat a visible share of every purchase. Propose a longer interval instead.
…

Read the whole file at its exact version.

How to install

Latest version
mdr add nirholas/three.ws/dca-planner@git:20260922.9a96a70
Exact content
mdr add nirholas/three.ws/dca-planner@sha256:70f476b84edd3d83

Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_kohma2q7fud2m7bv.svg)](https://markdownregistry.com/a/art_kohma2q7fud2m7bv)

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Versions

versioncommittedcommitsizeaudit
git:20260922.9a96a70 latest2026-09-22 9a96a70 4,106 BA view

Audit of the latest version

A  17 of 17 checks passed. Deterministic, no model, same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (4106 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

nirholas/three.ws · 211 stars · license Apache-2.0 · pushed 2026-09-23 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_kohma2q7fud2m7bv
GET https://markdownregistry.com/api/v1/resolve?ref=nirholas/three.ws/dca-planner
GET https://markdownregistry.com/api/v1/blob/70f476b84edd3d833ead1737d13beee350edebaf9d712ddb7b156c65bc25e2f5

Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

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