2195/quotesim-tape-validation/SKILL.md
Turn a low QuoteSim trust score into a real validated $/day number with zero CLI for the user. When the user wants to "validate" an asset, "get a real number", "make it trustworthy", or asks how to run the shadow logger, this skill sets up real-tape collection + scoring as scheduled tasks and reports back automatically when the readiness gate clears.
npx skillsauth add starchild-ai-agent/community-skills @2195/quotesim-tape-validationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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QuoteSim (open source: 2195/quotesim in the Starchild community-projects repo,
live demo at https://community.iamstarchild.com/2195-quotesim) simulates a
two-sided MM quoting strategy on any oracle-backed asset. Its fast OHLC model
overcounts fills (no queue position), so it self-reports a low trust score and
withholds dollar figures until validated. The trustworthy path: record a
live trade tape against the strategy's dry-run quotes for a few days, then
score the fill model on data it never saw. The user must never touch a
terminal — you drive everything via scheduled_task. This skill is how.
Find the product dir, in this order (set $QS accordingly):
output/quote-sim-product/ — original author's workspace layoutoutput/quotesim/src/ — installed from the community repocd /tmp && rm -rf scp && \
git clone --depth 1 --filter=blob:none --sparse \
https://github.com/Starchild-ai-agent/community-projects scp && \
cd scp && git sparse-checkout set 2195/quotesim && \
mkdir -p /data/workspace/output/quotesim && \
cp -r 2195/quotesim/* /data/workspace/output/quotesim/ && \
pip install flask requests --quiet
The runner (validate_runner.py) needs no API keys — it polls free public
Hyperliquid endpoints. Inside Starchild it routes via sc-proxy automatically
(httpcompat.py); outside it uses plain requests. Verify once:
cd $QS && python3 validate_runner.py --collect --coin BTC should print a
COLLECT line, not a traceback.
Trigger on any of: "validate <ASSET>", "get a real number for <ASSET>", "make <ASSET> trustworthy", "run the shadow logger", "how do I trust this", or a low (0–44/100) trust score the user wants resolved.
recentTrades + metaAndAssetCtxs). Every HL perp qualifies. Map the
user's asset to its HL coin symbol (e.g. HYPE, BTC).xyz:NATGAS, gold =
xyz:GOLD, TSLA = xyz:TSLA. recentTrades accepts the prefixed coin
directly, but mark lookup needs {"type":"metaAndAssetCtxs","dex":"xyz"}
(bare call will NOT find them → "no mark" error). The runner handles
xyz:* natively and sanitizes : in shadow filenames
(HL_xyz_NATGAS_*.jsonl). Pass VAL_COIN=xyz:NATGAS, never bare NATGAS.xyz: dex FIRST (it covers many commodities and
equities with real tapes); only fall back to "risk-shape-only" if absent.recentTrades returns ~10 prints per poll; a
1-minute poll captures low/mid-liquidity books well (exactly the "should I
list this?" candidates). On very thin tapes the 200-fill gate can take
WEEKS — tell the user a realistic timeline after the first day's fill count,
don't promise "2 days" blindly. Very high-volume majors undercount between
polls — note it; a websocket tape is future work.--score withholds all dollar figures until ≥200 predicted fills AND
≥2 days of tape. Below that it returns ready:false with a COLLECTING
progress line. This is the integrity rule — never surface partial-sample
dollars to the user.
VAL_STRAT.Use scheduled_task in command mode (cheap, no LLM). The runner persists
inventory across cycles in shadow/HL_<COIN>_state.json, so each run appends
to the same sample. Replace $QS with the absolute product dir.
scheduled_task(action="schedule",
title="QuoteSim tape collect — <COIN>",
schedule="every 1 minute",
deliver="local", # silent; no push per cycle
command="cd $QS && VAL_COIN=<COIN> VAL_SIDE=<SIDE_NOTIONAL> "
"VAL_STRAT='<STRAT_JSON>' python3 validate_runner.py --collect")
Record the returned job_id (topic or memory) so you can cancel it later.
A once-daily command task that runs --score and prints only when ready
(empty stdout = silent push-free run, zero cost). Convert the user's local
time to UTC for the cron.
scheduled_task(action="schedule",
title="QuoteSim tape score — <COIN>",
schedule="0 13 * * *", # pick ~daily, user's evening
command="cd $QS && python3 validate_runner.py --score --coin <COIN> "
"| python3 -c \"import sys,json; d=json.load(sys.stdin); "
"print('✅ '+d['coin']+' validated — trust the dollars now: '"
"+str(d.get('total_per_day'))+'/day (spread '+str(d.get('spread_per_day'))"
"+', directional '+str(d.get('directional_per_day'))+', '"
"+str(d['fills'])+' real fills over '+str(d['days'])+'d). Re-run it in QuoteSim.') "
"if d.get('ready') else ''\"")
One message: what you started, a realistic timeline ("collecting real tape on <COIN>; I'll message you the moment it's trustworthy — days on liquid books, weeks on thin ones"), and that they do nothing. Don't expose cron/job_ids/ paths unless they ask.
When the scorer reports ready, cancel the collector job
(scheduled_task action=cancel job_id=...) so it stops consuming cycles, and
optionally the scorer too. Confirm to the user and point them back to the
QuoteSim dashboard, where the asset now clears the trust gate.
python3 validate_runner.py --collect --coin HYPE # one cycle
python3 validate_runner.py --score --coin HYPE # current verdict
deliver="local" on the collector keeps it silent (no per-minute spam).shadow/HL_<COIN>_state.json and restart collection.--score ad hoc; ready:false includes a
fills/days progress line you can relay ("34 of 200 fills, day 1.2 of 2").development
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