القائمة
Use when you already have multiple analysis answers — clusters them into consensus + minority report
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when setting up or repairing the Python env for analysis — detects uv/venv/conda/poetry/pipenv, installs core packages
Use when building or improving a predictive model — AIDE-style solution tree with leakage discipline and empirical leaderboard
Use when a data question is fuzzy or high-stakes — clarifies scope and writes analysis-spec.md before running a solver
Use when starting fresh with data and unsure which skills to use — peeks at data, asks targeted questions, assembles a crew plan
"Use when onboarding a dataset or asking 'what\'s in this data?' — per-column quality report with join-compatibility checks; flags possible PII/sensitive columns"
Use when browsing, pruning, or seeding a run from past analyses stored across sessions
| name | ds-reconcile |
| description | Use when you already have multiple analysis answers — clusters them into consensus + minority report |
Use ds-reconcile when you already have N candidate answers (from any source — different runs, different analysts, different tools) and want them reconciled into a consensus view.
Do NOT use when you need to produce new analyses from scratch; use ds-spike for a full
parallel ensemble run that produces and then reconciles answers.
Always preserve the minority report. Disagreements between candidates are the insight, not noise to be suppressed. A unanimous result with high confidence is more trustworthy than a forced consensus that hides a dissenting answer.
{"id": "run-1", "answer": 41.7, "sufficient": True, "assumptions": ["refunds excluded"]}
sufficient field is optional but important: verified answers are weighted 4× higher
than unverified ones (WEIGHT_SUFFICIENT=1.0 vs WEIGHT_UNVERIFIED=0.25).ds-verify first to populate sufficient: True/False.
This step is recommended whenever the answer stakes are non-trivial.aggregate(records) from ../ds-spike/scripts/aggregate.py.{
"answer": "<consensus answer>",
"confidence": 0.8,
"support": ["run-1", "run-3"],
"n_solvers": 3,
"n_clusters": 2,
"unanimous": false,
"minority_report": [
{
"answer": "<dissenting answer>",
"support": ["run-2"],
"assumptions": ["refunds included"]
}
]
}
Report confidence as the fraction of total weight (not raw count) behind the consensus,
so that verified answers count more than unverified ones.
from aggregate import aggregate
result = aggregate(records)
print("consensus:", result["answer"], "confidence:", result["confidence"])
print("minority_report:", result["minority_report"])
../ds-spike/scripts/aggregate.pyaggregate(records), cluster_results(records)sufficient=True → weight 1.0; sufficient=False/missing → weight 0.25aggregate(records)["confidence"] — fraction of weight behind the consensusaggregate(records)["minority_report"] — list of dissenting answer clusters