From ds-crew
Use when starting fresh with data and unsure which skills to use — peeks at data, asks targeted questions, assembles a crew plan
How this skill is triggered — by the user, by Claude, or both
Slash command
/ds-crew:ds-conductThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Starting fresh: user has data and a fuzzy request but no clear workflow yet. The conductor
Starting fresh: user has data and a fuzzy request but no clear workflow yet. The conductor figures out the right crew and sequence before any analysis runs.
Typical triggers:
Skip this skill when the user already knows what they want (e.g. "run ds-spike on payments.csv to compute churn"). Call the named skill directly.
ds-conduct is a conductor, not an analyst. It never re-implements the skills it
orchestrates. If it needs to know something from the data, it calls data-profile. If it
needs intent clarified, it calls ds-clarify. If it needs analysis, it dispatches
ds-star-plus or ds-spike. All substance lives in the specialist skills.
Inspect every data file structurally without running analysis: row count, column names +
dtypes, null rates, unique counts for key columns, timestamp detection, candidate keys, file
relationships (shared columns across files). Reuse the fast analyzer from
../data-profile/SKILL.md (description-only mode, not a full profile). Goal: learn enough
to ask informed questions.
Produce a compact internal summary:
payments.csv: 2.8M rows | columns: payment_id (uuid, unique), merchant_id (int, 97% coverage),
created_at (datetime, range 2023-01–2024-12), amount_eur (float, 2% null), status (3 values)
merchants.json: 18K records | merchant_id (int, key), signup_date, country, tier, is_active (bool)
Shared key: merchant_id (both files)
Triggered patterns: timestamp, shared key, target-shaped column (is_active), internal report language
Many files (data-lake / N > ~100). When the peek surfaces too many files to load wholesale,
do not route on filename or embedding guesswork. Apply the column-level retrieval protocol —
../ds-star-plus/references/retrieval.md Stage 3 (column-name overlap, low-cardinality value
containment, join-key reachability, with the recall-biased keep rule) — to pick the relevant
file-set from the digests you already produced, then route on that scoped set. A file dropped here
is unrecoverable downstream, so keep anything strong on either embedding or structure.
Using the trigger catalog (references/trigger_catalog.md), identify every pattern that fired
in Stage 1. Formulate one targeted question per detected pattern, grounded in what was
actually seen ("I see a created_at column — is this a time-series problem?" not "do you
have time data?"). Extend with the clarification checklist from
../ds-clarify/references/clarify_checklist.md for any additional ambiguities.
Ask all triggered questions in a single batched message. Do not trickle questions one per round. Record every answer — unanswered items block plan assembly.
Fill in the workflow plan template (references/workflow_plan_template.md) with the answers
from Stage 2. Determine:
ds-spike N + personas, if ensemble selectedds-model is needed (prediction task) — if so, its first step is the feasibility & leakage gate (../ds-model/references/feasibility_gate.md): estimate the achievable ceiling (forecastability for time-series) and scan for leakage before modelinganalysis-spec.md path that ds-clarify will writeFold in distilled rules (if a store exists). Read the rules store (../ds-memory/SKILL.md
Mode 4 — ExpeL distillation; rules.jsonl) and keep any rule whose task-signature family overlaps
this task. Let matches shape the plan and the questions you raise — e.g. a rule "confirm
cancellation handling before summing revenue" becomes both a plan step and a Stage-2 question.
Rules are advisory; they never override the user or the verifier.
Escalation to search (hard single-number tasks). When a task is a single high-stakes number
that greedy solving keeps missing, route it to ../ds-search/SKILL.md (tree-search). If a
search_experience.jsonl store exists, pass the matching prior experience as the seed (its Step 0)
so the tree starts from past wins and avoids recorded dead-ends — search multiplies cost, so seeding
it well is what keeps the escalation affordable.
Present the filled plan to the user in plain language. Do not begin execution until the user approves.
Show the assembled plan. Wait for explicit user approval. On approval, execute skill by skill, pausing at each checkpoint to surface the output before proceeding.
At each checkpoint:
A conducted analysis: profile → spec → analysis result (consensus + minority report if spike was used), plus a run manifest listing:
analysis-spec.md pathThis skill is markdown driving a model across a long context. No process can enforce that the verifier, routing, and oscillation-handling steps actually ran as specified.
Mitigation: emit a structured runlog.json with these fields after each run:
rounds (int): number of loop iterationsverifier_verdicts (list): [{"round": N, "score": 1-4}, ...] for each roundoscillated (bool): whether oscillation was detectedsubrun_cost_usd (list): cost per sub-run (ds-spike only; empty list for single-solver skills)Validate it:
python3 ../ds-spike/scripts/runlog.py runlog.json
If it reports errors, the loop was not followed; do not present the answer as verified. This makes deviations detectable after the fact, not impossible.
| Resource | Path |
|---|---|
| Trigger catalog | references/trigger_catalog.md |
| Workflow plan template | references/workflow_plan_template.md |
| Evidence / theory | references/evidence.md |
| Clarification checklist | ../ds-clarify/references/clarify_checklist.md |
| Spec template | ../ds-clarify/references/spec_template.md |
Pairs with: data-profile · ds-clarify · ds-star-plus · ds-spike · ds-model
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Searches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
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