Scope research

Scope Research

Survey the codebase against a proposed requirement and report the concrete facts a reader would need to assess scope themselves — which areas would be touched, and what is true about each of those touchpoints right now. The skill states facts; the reader draws the conclusions.

A scope assessment expressed as a t-shirt size or a single LOC number hides the reasoning that produced it. Naming the touchpoints — each with an honest LOC range and the facts around it (callers, prior changes, test state, conventions) — gives the reader something they can actually weigh, and surfaces the questions worth asking before the work begins.

When to invoke

• Manual only. The user explicitly asks to research the scope of a requirement, or runs /scope-research.
• Never auto-invoke.
• Skip trivial single-file or one-line changes where research adds nothing.
• The skill never issues a verdict — no t-shirt size, no time estimate, no risk rating. LOC change appears as honest ranges per touchpoint (e.g. ~5–20 lines), never as a single number or bucket label. It surfaces facts only.

Workflow

Step 1 — Anchor the requirement

The requirement can arrive as:

• an inline description in the chat,
• a local file path the user gave (e.g. specs/new-feature.md) — read it directly,
• a pasted ticket body / spec in the chat.

Read what the user provided. If the requirement is too vague to research (e.g. "make the app faster"), ask clarifying questions with AskUserQuestion before going to the codebase. Do not guess at intent.

Step 2 — Detect project code-navigation conventions

Before exploring, check the project's rule files for any prescribed code-search system — an MCP server (e.g. Serena, a code-index server), a custom indexer, a documented architecture map, or any other navigation convention. Look in this order:

1. CLAUDE.md (repo root, then .claude/CLAUDE.md)
2. AGENTS.md
3. .cursor/rules/ or .cursorrules
4. .github/copilot-instructions.md
5. README.md / CONTRIBUTING.md — sections on code navigation

If the project prescribes a tool, use it instead of (or in addition to) the generic Read/Grep/Glob path. If nothing is prescribed, fall back to the generic tools.

Step 3 — Initial scan

The main agent does a first pass to locate likely-affected areas: grep for the key nouns/verbs from the requirement, read the top hits, and form a rough picture of where the change would land.

Step 4 — Targeted exploration

If the scope is broader than one area, dispatch scope-research-surveyor agents in parallel (single message, multiple Agent tool calls) — typically 1–3, but as many as the requirement honestly warrants. Each surveyor gets a distinct, specific search focus — examples:

• "Callers of <symbol> and the call sites' surrounding context."
• "Prior similar changes in git history (git log -S, git log --grep)."
• "Current test state for module <X> — which test files cover it, what they assert, what they do not."
• "Conventions currently used in <area> for <concern>."

Two principles govern how many to dispatch — there is no hard cap:

1. Each focus must be load-bearing. A focus that could merge into another without losing clarity should merge — don't open a surveyor for it. A surveyor is one full inference run plus a report you have to read; it has to earn that cost.
2. You will have to integrate every report. More surveyors = harder synthesis. If you find yourself wanting more than ~4–5 surveyors, consider whether the requirement should be researched in stages instead of one wide fan-out.

Pass each surveyor: the requirement (as currently understood), its specific focus, the repository path, and any context already gathered. The surveyor's contract — stance, tool boundaries, no-fabrication rule, the required Focus / Facts / Coverage output format, and the per-fact Change size LOC-range field — is defined in agents/scope-research-surveyor.md. Do not re-specify any of that in the dispatch call.

For tightly scoped requirements, skip this step entirely — the main agent's direct exploration is enough.

Step 5 — Resolve uncertainty

If anything material to the report is still unclear after exploration, ask the user with AskUserQuestion. Examples of material uncertainty:

• The requirement could be implemented in two genuinely different places and the choice would change the touchpoint list.
• A key behaviour the requirement implies is not specified (e.g. "rate limiting" — per user? per IP? per endpoint?).
• The codebase has two plausible interpretations of a term the requirement uses.

Keep asking until no remaining material questions. Uncertainty is not an output category — it is resolved before the report exists.

Step 6 — Write the report

Present the report in chat in this shape:

Requirement (one paragraph)

Restate what the skill understood the requirement to be, after any clarifications. The user can correct here before reading further.

Estimated total change

A single line at the top of the report, after the requirement restatement:

Estimated total change: ~A–B lines

A and B are the sums of the lower and upper bounds of the per-touchpoint Change size ranges below. This is an additive sum of honest per-touchpoint ranges, not a grand-judgment "size". If any touchpoint's Change size is n/a, exclude it from the sum and say so on a follow-up line.

Affected areas / files

A list. Each entry:

• <path> — modify / add / delete — Change size: ~X–Y lines — one sentence on the role this file plays in the change.

Group by subsystem if helpful. Use file:line references where a specific location matters. The Change size per touchpoint mirrors what the surveyors reported in their Facts items — use their numbers, do not re-estimate.

Relevant facts

For each touchpoint (or for the change overall), state neutral facts:

• callers / dependents of an affected symbol (file:line references)
• prior similar changes in git history (commit hashes + one-line summaries)
• current test coverage state for the affected area
• existing conventions / patterns the change would intersect with
• data-shape, migration, or interface-contract facts

Facts are stated, not labelled as "risks" and not weighted. Do not write "this is risky because…" or "low risk". Write the fact; the reader decides.

End — cross-skill hint

If the report is long (≥150 lines, ≥5 H2/H3 sections, or simply a lot to scan), suggest the user run /html-article on it. Otherwise skip the hint.

Notes

• No script — pure LLM + Read/Grep/Glob + Agent. Same shape as ab-review.
• The skill is read-only: it never writes code, never edits files outside of the report it returns in chat, never commits.
• The report stays in the conversation. If the user wants a saved artifact, they can run /html-article on the report after the fact.
• No verdicts: no t-shirt size, no time estimate, no risk rating. LOC change appears only as honest per-touchpoint ranges (and their additive sum at the top of the report) — never as a single number or bucket. If the user asks for a t-shirt size or a single LOC number, restate this constraint and offer to surface more facts instead.