Agent skill

Recipe Playground Dca

by krakenfx in krakenfx/kraken-cli

Example driver: drive a time-based dollar-cost-averaging hypothesis into a recorded session.

MITAuto-check passedBusiness, Finance & HR

Install Recipe Playground Dca

skills CLI
$ npx skills add krakenfx/kraken-cli --skill recipe-playground-dca -a claude-code

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

GitHub CLI
$ gh skill install krakenfx/kraken-cli recipe-playground-dca --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/krakenfx/kraken-cli.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/recipe-playground-dca .claude/skills/recipe-playground-dca && 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
recipe-playground-dca
GitHub stars
751
Token cost
~2.2k tokens
SKILL.md length
896 words
Files
1
Skills in repo
59
Repo updated
First seen
Licence
MIT

At a glance

Example driver: drive a time-based dollar-cost-averaging hypothesis into a recorded session.

  • Business, Finance & HR work in your project
  • SKILL.md covers Important, Params, Quick Start and Start the Session, plus 4 more sections
  • Calls jq

What it does

Recipe Playground Dca is an agent skill from krakenfx/kraken-cli. Example driver: drive a time-based dollar-cost-averaging hypothesis into a recorded session.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Business, Finance & HR. The repository describes itself as: The first AI-native CLI for trading crypto, stocks, forex, and derivatives. The licence is MIT.

When your agent uses it

  • Business, Finance & HR work in your project

Example prompts

  • “/recipe-playground-dca”

What it can do on your machine

Read from SKILL.md and the folder at commit aa56e59. 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:

    • jq

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    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.

Context cost

Recipe Playground Dca loads about 2.2k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 896 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~29
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

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 passed

The automated check found no risky patterns in SKILL.md.

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 krakenfx/kraken-cli at commit aa56e59, republished under its MIT licence (© krakenfx). 896 words, ~2,206 tokens.

Download SKILL.mdSave it as .claude/skills/recipe-playground-dca/SKILL.md (or your agent's skills folder).
name
recipe-playground-dca
description
Example driver: drive a time-based dollar-cost-averaging hypothesis into a recorded session.
version
1.0.0

Playground: DCA (Time-Gated)

PREREQUISITE: Load kraken-playground and kraken-dca-strategy to run this recipe. This recipe is an example, not a boundary. Adapt the steps to your hypothesis, or write your own driver (see kraken-playground → Driving Your Own Hypothesis).

Test a dollar-cost-averaging hypothesis on live prices with no real money. Buy on a fixed cadence, optionally only when price dips below a short-term average, and record every buy, skip, and average cost into one session for later replay and P&L.

Use this skill for:

  • running a time-based DCA hypothesis over a window
  • gating each buy on a dip below a short-term average
  • recording every decision with the numbers behind it

Important

This recipe records a session inside a paper workspace. It never places a live order. Paper results may overstate live performance: fees and slippage are simulated and there are no partial fills (see kraken-paper-strategy).

Pacing is external. /loop owns the schedule and fires one round per interval (see kraken-playground → Pacing). One decision per interval, paced from outside, so the session is interventional over the window (see kraken-playground → Running over a Window).

Params

Every number that gates a buy or skip goes in --strategy-params, not only in prose reasons. That is what makes two sessions comparable knob-for-knob:

  • dollars_per_buy: quote currency deployed per qualifying round
  • rounds: session length in decision rounds
  • interval_s: spacing between rounds; set the /loop interval to this value
  • dip_threshold_pct (dip-gated variant): only buy when price is at least this far below the short-term average; omit for unconditional time-based DCA

Quick Start

Natural language:

Dollar-cost-average into Bitcoin over 10 hours. Buy $100 every hour if the
price dips more than 0.10% below the 1-hour SMA. Record all buys, skips, and
average cost. At the end, show P&L.

Start the Session

Work inside a paper workspace (create one once: kraken workspace create dca --capital 10000 --mode paper), then:

bash
export KRAKEN_WORKSPACE=dca

kraken session start \
  --symbols BTC/USD --channels ticker,trade --to duckdb,jsonl \
  --label dca-btc-$(date +%Y%m%d-%H%M%S) \
  --strategy recipe-playground-dca \
  --strategy-params '{"dollars_per_buy":100,"rounds":10,"interval_s":3600,"dip_threshold_pct":-0.10}' \
  -o json 2>/dev/null &

# The session_started stdout line carries the id: {"type":"session_started","session":"s<n>",...}

Print the session id to the user right after starting, and again in the final report — it is the handle for resuming the /loop, checking kraken session show, and locating the artifacts.

Schedule the Rounds

/loop owns the pacing. Set its interval to interval_s and it fires each round on cadence (late under jitter, never early). Parse the interval from the user's request: "every hour" to 1h, "every 30 minutes" to 30m, "twice a day" to 12h.

The scheduler floor is 60 seconds (/loop, cron, and ScheduleWakeup all clamp to a one-minute minimum). interval_s must be >= 60; a sub-minute cadence is impossible — clamp to 60 and tell the user if they ask for less.

/loop 1h "Run one DCA round for session s<n> in workspace dca per recipe-playground-dca"

Do not re-add an elapsed-time gate inside the round. /loop already enforces the spacing.

Each Round

On each firing: stop if the session is complete, otherwise READ, THINK, ACT.

Check the last completed round against rounds. The round cursor lives in the session's typed state cell (kraken session state get/set) — not in the decision log, whose reasons stay free narrative (keep writing "round N" in them for the post-mortem story, but nothing parses it). Reading the cursor is O(1) however long the session runs, and a typo'd field refuses at set instead of silently steering the loop:

bash
ROUNDS=10   # the `rounds` you set in --strategy-params
SESSION_ID=s1   # from the session_started line

# The typed cursor is the loop's memory: unset reads as round 0.
LAST_ROUND=$(kraken session state get --session "$SESSION_ID" -o json 2>/dev/null \
  | jq '.cursor.round // 0')
if [ "${LAST_ROUND:-0}" -ge "$ROUNDS" ]; then
  kraken session stop -o json 2>/dev/null
  exit 0
fi
ROUND=$((LAST_ROUND + 1))
# CLAIM the round BEFORE acting: a crash after the claim skips one buy
# (harmless); the reverse order would redo the round and buy twice. `set`
# replaces the whole cursor — carry every field your strategy tracks.
kraken session state set --round "$ROUND" -o json 2>/dev/null

READ: price and the short-term average. Mind the response shapes: the ticker is keyed by Kraken's INTERNAL pair name (XXBTZUSD, not BTC/USD), so read the price through the value (.[].last_price). The ohlc rows live under .candles; read that array directly — the sibling last cursor would poison naive iteration of the object.

bash
PRICE=$(kraken ticker BTCUSD -o json 2>/dev/null | jq -r '.[].last_price')
# True 1h SMA: the last twelve 5-minute closes (NOT --interval 60, whose
# full-window mean is a multi-day average).
SMA=$(kraken ohlc BTCUSD --interval 5 -o json 2>/dev/null | jq '[.candles[-12:][].close] | add / length')

Fail loud, never fabricate: if either value comes back empty, the round must not guess — note an alert and end the firing, or the decision log fills with plausible-looking false reasons:

bash
if [ -z "$PRICE" ] || [ -z "$SMA" ]; then
  kraken session note --kind alert --symbol BTC/USD \
    --reason "round $ROUND aborted: READ failed (price='$PRICE' sma='$SMA')" -o json 2>/dev/null
  exit 0
fi

Compute in jq/bc, do not eyeball it.

THINK:

  • vs_sma = (price - sma) / sma * 100
  • If the ticker spread is wider than 1%, skip and note it. A wide spread distorts the paper fill.
  • If vs_sma <= dip_threshold_pct, buy dollars_per_buy / price units. Otherwise skip.

ACT (buy):

bash
kraken order buy BTC/USD <volume> --type market \
  --reason "DCA round N: BTC/USD at <price> is <vs_sma>% below 1h SMA <sma> (threshold <dip>%); deploying \$<dollars_per_buy>" \
  -o json 2>/dev/null

Inside the paper workspace the order fills on the paper account, and the reason lands in the active session's decision log.

ACT (skip):

bash
kraken session note --kind skip --symbol BTC/USD \
  --reason "skip round N: BTC/USD at <price> is <vs_sma>% from 1h SMA (threshold <dip>%)" \
  -o json 2>/dev/null

The round is already claimed, so nothing to write after acting: buy or skip, log the reason, and exit the firing.

Always pass the numeric reason so the decision log carries the "why" behind every round.

Show full SKILL.md (227 more words)Show less

Stop and Review

bash
kraken session stop -o json 2>/dev/null
kraken session show -o json 2>/dev/null | jq '.summary'
kraken explain pnl --session latest -o json 2>/dev/null | jq '{anchor, waterfall: [.components[] | {kind, amount}]}'

kraken session decisions --session "$SESSION_ID" -o json 2>/dev/null \
  | jq -c '.decisions[] | {kind, symbol, reason, timestamp}'

Report:

  • total deployed vs expected (dollars_per_buy × rounds)
  • buys vs skips, with dip measurements
  • average fill cost vs window mean
  • P&L, and whether the dip threshold fit the tape

Hard Rules

  • This recipe records a session inside a paper workspace. It never places a live order.
  • /loop drives the pace and the agent runs each round. The CLI has no scheduler and runs no strategy.
  • Do not add a pacing gate inside the round; /loop enforces spacing.
  • A scheduled /loop fire only runs what the agent may invoke unprompted. Keep kraken order buy, kraken session note, and kraken session show permitted non-interactively, or a fire stalls waiting on an approval you never see.
  • Keep every gating number in --strategy-params and the arithmetic in jq/bc, so a rerun on the same tape reproduces the decisions.
  • The session directory (session.json, decisions.jsonl, and the tape sinks) is owned by the CLI recorder. The agent only reads it, and only for the stop-time summary. Never write, edit, mkdir, or append inside it — every buy and skip goes through kraken order buy / kraken session note, so the recorder stays the single writer.
  • If you hit a mismatch between what you are trying to do and the CLI's interface or responses — including a mismatch between this skill and the installed CLI version's contract — feel free to submit feedback with kraken feedback.

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

Files

Just SKILL.md in skills/recipe-playground-dca of krakenfx/kraken-cli.

Open the folder on GitHubat commit aa56e59

Compare with similar skills

Recipe Playground Dca next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Recipe Playground Dca this skillkrakenfx/kraken-cli751—~2.2kAutomated safety check: PassMIT
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Theme Detectortradermonty/claude-trading-skills3k2 repos~4.9kAutomated safety check: PassMIT
Creating Financial ModelsChen-zexi/open-ptc-agent7293 repos~1.3kAutomated safety check: PassMIT
Stock APIzhangxiangliang/stock-api2k—~507Automated safety check: PassMIT
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Questions about Recipe Playground Dca

What does Recipe Playground Dca do?

Example driver: drive a time-based dollar-cost-averaging hypothesis into a recorded session. Recipe Playground Dca is an agent skill from krakenfx/kraken-cli. Example driver: drive a time-based dollar-cost-averaging hypothesis into a recorded session.

When should I use Recipe Playground Dca?

Recipe Playground Dca fits situations like: business, Finance & HR work in your project.

How do I install Recipe Playground Dca in Claude Code?

Run `npx skills add krakenfx/kraken-cli --skill recipe-playground-dca -a claude-code`. Or copy the skill folder (skills/recipe-playground-dca in krakenfx/kraken-cli) into .claude/skills/recipe-playground-dca in your project. Claude Code loads it when a task matches its description.

How do I install Recipe Playground Dca in Codex?

Run `npx skills add krakenfx/kraken-cli --skill recipe-playground-dca -a codex`. Or copy the skill folder (skills/recipe-playground-dca in krakenfx/kraken-cli) into .agents/skills/recipe-playground-dca in your project. Codex loads it when a task matches its description.

Can I use Recipe Playground Dca 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 krakenfx/kraken-cli --skill recipe-playground-dca -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/recipe-playground-dca, .gemini/skills/recipe-playground-dca, .github/skills/recipe-playground-dca and .opencode/skills/recipe-playground-dca in your project.

What does Recipe Playground Dca need to run?

Going by SKILL.md and its folder, Recipe Playground Dca needs the command-line tools its instructions call (jq).

Does Recipe Playground Dca access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Recipe Playground Dca safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Recipe Playground Dca use?

Recipe Playground Dca is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Recipe Playground Dca use?

About 2.2k tokens (SKILL.md is roughly 8.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Recipe Playground Dca?

Skills that share tags, products or a category with Recipe Playground Dca: Technical Analyst (tradermonty/claude-trading-skills, 3k stars), Theme Detector (tradermonty/claude-trading-skills, 3k stars), Creating Financial Models (Chen-zexi/open-ptc-agent, 729 stars) and Stock API (zhangxiangliang/stock-api, 2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Recipe Playground Dca?

krakenfx (a GitHub organization) maintains it in krakenfx/kraken-cli, which has 751 GitHub stars. The repository holds 59 skills in this directory. The repository was last updated on August 7, 2026.

Source: krakenfx/kraken-cli on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.