Isolate

/isolate — Subagent Research Delegator

Research happens in subagent context windows; only synthesized findings enter the main context. Durable findings are then persisted to the store so no future session repeats the work.

Store pre-flight

Existing store state: !python3 "${CLAUDE_PLUGIN_ROOT}/scripts/wisci.py" scan

If the scan shows an error or unexpanded variable, run the bundled script manually: scripts/wisci.py relative to this skill's directory.

Before spawning anything, check the scan for a .wisci/context/ file covering the research topic:

• Fresh match: load it. Narrow the research to what it does not answer — or skip research entirely and present the stored knowledge.
• Stale match: research anyway; in the synthesis, reconcile against the stored file ("what changed since this was written").
• No match: research from scratch.

Execution Flow

1. Parse task. Read $ARGUMENTS. Note override words: deep/thorough (wider sweep), save (force persist), nosave (inline only).

2. Plan decomposition. Decide autonomously — no approval step. For each subtask pick the agent type:

   • Codebase questions → subagent_type: "Explore" (read-only)
   • External questions (library docs, best practices, comparisons) → subagent_type: "general-purpose" with instructions to use web search/fetch
   • Hybrid tasks mix both.

   Agent count: 1 for focused single-area research, 2-3 for natural splits, up to 5 only when $ARGUMENTS says deep/thorough. When multiple agents cover one topic, give each a distinct lens (code structure, git history, docs/web) rather than redundant copies of the same search.

3. Spawn all agents in a single message (parallel). Each prompt contains: the specific investigation task, focus areas, and the required output shape — structured markdown with exact file paths, line numbers, function names, and error messages. Vague summaries are not acceptable subagent output.

4. Collect and handle failures. An agent that returns nothing or dies: retry it once; still nothing → report the coverage gap explicitly in the synthesis. Never present partial coverage as complete.

5. Synthesize inline using the output format below: key findings per agent, patterns and contradictions across them, actionable conclusions, remaining gaps.

6. Persist durable findings. Judge what the research produced:

   • Durable (architecture maps, integration research, comparisons, external docs findings — anything a future session would otherwise re-discover): invoke the write skill with the topic so it lands in .wisci/context/ with a References manifest. On platforms without skill invocation, follow the write skill's procedure directly. This skill contains no write logic of its own.
   • Ephemeral (a quick lookup answered in a paragraph): inline only — a trivial store entry is rot, not memory.
   • save/nosave in $ARGUMENTS overrides this judgment.

Output Format

## /isolate Results: <task summary>

### <Agent/lens 1 name>
<Structured findings with file paths and specifics>

### <Agent/lens N name>
...

### Synthesis
<Combined insights, contradictions, recommendations, gaps>

End with: ~N tokens entered context. Persisted: .wisci/context/<topic>.md (or "not persisted — ephemeral").

Key Constraints

• Findings are presented inline first, persistence second — the user reads results now either way.
• No approval step for decomposition; the user sees results, not plans.
• Parallel execution: multiple agents always spawn in one message.
• Runs inline (not forked) — it must spawn children and load results into the main window.