# Research

> Use when collecting evidence for the business value of an AI skill or workflow into a provenance-tagged research package.

- Skill: `tony/research` (Agent Skill)
- Install (CLI): `npx skillmds@latest add tony/research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tony/research/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: tony (https://skillmd.com/u/tony)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tony/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:
`!date +%F`

Kernel (WSL detection):
`!grep -qi microsoft /proc/version 2>/dev/null && echo "WSL" || echo "(not WSL)"`

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

gh:
`!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 `AskUserQuestion` panel: generate a report (ask which
tier), collect more, or stop. Skip the panel in plan mode or when
running non-interactively.