Business Research

Business Research

Collect the data behind a business-value claim for an AI skill or agentic workflow — productivity, time saved, quality, capacity, delivery outcomes — into the interim run package the report commands render from. Value is stated in engineer-hours, cycle time, throughput, quality, and capacity. Never money.

Read these first; they bind every step:

• references/interim-format.md — the run package layout and where runs live.
• references/provenance.md — the four tags, the anti-inflation rules, and the no-currency contract.
• references/measurement.md — what the data must feed: the saving formulas, statistics discipline, the counterfactual ladder, and the quality guardrails.
• references/instruments.md — per-instrument probes, collection discipline (timezone and cohort conventions, the GraphQL filteredCount trap, absence snapshots), and the collection targets in query-ready form.

User arguments: $ARGUMENTS

Context

Today — run this command and read the output:

date +%F

Kernel (WSL detection) — run this command and read the output:

grep -qi microsoft /proc/version 2>/dev/null && echo "WSL" || echo "(not WSL)"

Instruments on PATH — run this command and read the output:

for c in git gh jq; do command -v $c >/dev/null && echo "$c: available" || echo "$c: unavailable"; done

gh — run this command and read the output:

gh auth status 2>&1 | head -3

Procedure

1. Scope and orchestration plan

Present an orchestration plan before touching disk: what will be measured, over what pinned date range, with which instruments, to which output directory. Enter plan mode if the host supports it — Claude Code EnterPlanMode; Cursor, Codex, or Gemini via /plan or Shift+Tab; otherwise present the plan as plain text and pause. Ask where to write the run, defaulting per the location rules in interim-format.md (Documents root; on WSL prefer the Windows Documents folder when detected). Wait for confirmation.

2. Instrument discovery — never assumption

Probe what is actually available using the per-instrument probes in instruments.md; never assume an instrument exists. Candidates (illustrative, not a fixed list): git history; gh (verify auth and rate limits before relying on it); ticket-tracker MCPs or CLIs such as Jira or Linear; CI telemetry; session logs; time-tracking exports. Record every instrument as available or unavailable in the run README. Data an unavailable instrument would have provided is recorded as unknown — never fabricated, never silently skipped.

3. Collect

For each available instrument, run pinned-window queries per the collection discipline in instruments.md and snapshot raw output into raw/ before deriving anything. Targets — collect what the instruments support and record the rest as unknown:

• Task and PR cycle times, review latency. Prefer GraphQL over the Search API for reliability; paginate; compute distributions client-side.
• Rework signals: reverts, reopened items, CI failure and retry rates, PR-size drift.
• Per-task timing where measurable: manual baseline vs AI-assisted duration, including verification/review time and failed-run time.
• Adoption signals: distinct users of the skill vs the eligible population — record license-holding and active use as separate numbers.
• Skill build and maintenance time: reconstructed from history if possible, else ESTIMATED with rationale.

4. Write the package

Emit the full interim format from interim-format.md: run README, source manifest with verbatim queries, assumptions register, raw snapshots, per-topic measurements, and findings.md. Every figure tagged; every unknown listed with what data would resolve it.

Rules

• Raw snapshots are immutable once written.
• An unavailable instrument yields unknown, not an estimate — unless the user supplies an assumption, which is registered as ESTIMATED with rationale and owner.
• No currency in any output, per provenance.md.

Output

Open with a one-line hero (✓ Run written: <run path>, window <start>..<end> or ⚠ Halted: <reason>), then exactly these sections:

1. ## Scope — what was measured and the pinned window.
2. ## Instruments — available vs unavailable, and what each unavailable one leaves unknown.
3. ## Collected — per instrument: what landed in raw/ and measurements/.
4. ## Package — the run path, count of registered assumptions, and the open unknowns.

End with an ask-user-choice panel: generate a report (ask which tier), collect more, or stop. Skip the panel in plan mode or when running non-interactively.

Portability notes

• ask-user-choice — present the listed options and wait for the user to pick one. Hosts with a structured multiple-choice tool (Claude Code's AskUserQuestion) should use it; otherwise print a numbered list and wait for a numbered reply. Never proceed on an assumed answer.
• $ARGUMENTS — the text the user passed when invoking this skill. If your host does not substitute it, read it as the user's request in the current turn, and ask when there is none.
• Bundled files — every relative path in this skill points at a file shipped inside this skill directory. Read them from here, not from the host's plugin tree.