research-plan

name

research-plan

description

Planner-agent operations for the Deep Researcher Reflect Evolve system: create an initial research plan, reflect on the current plan against the global research context, update the plan, or score research progress (0-100). Use when a research session needs a one-off planning operation outside the full /deep-research loop, or when the user invokes /research-plan with one of the modes below. Args: <mode> [<topic-slug>]. Modes: create, reflect, update, progress.

Research Plan — Planner Agent

This skill executes a single phase of the Planner role from the Deep Researcher Reflect Evolve architecture. It operates on the same per-topic working directory used by /deep-research:

.research/<slug>/
├── plan.md
├── context.md
└── progress.json

Argument parsing

The first arg is the mode. The optional second arg is a topic slug (matches a directory under .research/). If no slug is given and exactly one .research/* directory exists, use it. If multiple exist, ask the user which.

For mode create, the args are create <full topic string> instead — a new working directory is created from a slugified version of the topic.

Modes

create <topic>

Create the working directory, initialize progress.json, and produce the initial Research Plan.

Slugify: lowercase, replace non-alphanumerics with -, collapse repeats, trim to 40 chars. Working dir: .research/<slug>/.

Spawn a fresh general-purpose subagent. Brief:

Topic: <topic>. Write an initial research plan to <plan_path> as a hierarchical markdown checklist that decomposes the topic into 5–10 investigation areas, each with 2–4 concrete sub-questions written as - [ ] checkboxes. The plan must be exhaustive enough to support a PhD-level report on this topic. Do not perform any web search — you are a planner only. Return when the file is written.

Then write progress.json:

{"iteration": 0, "percent": 0, "max_iterations": 12, "topic": "<topic>"}

reflect [<slug>]

Critically review the current plan against accumulated findings.

Spawn a fresh subagent. Brief:

Read <plan_path> and <context_path>. Critically assess: (1) which sub-questions are now answered; (2) what unforeseen sub-topics emerged; (3) what redundant paths should be terminated.

Output a structured reflection to stdout in this exact format:

ANSWERED: <list>
EMERGED:  <list>
TERMINATE: <list>
CHANGES_NEEDED: <yes|no>
RATIONALE: <2-4 sentences>

Do NOT modify the plan file.

Show the reflection block to the user.

update [<slug>]

Apply the most recent reflection to the plan. If no reflection has been run in this conversation, run reflect first, then update.

Spawn a fresh subagent. Brief:

Apply this reflection to <plan_path>:

Edit the plan in place: tick [x] answered items, add new items from EMERGED, strike through terminated items as ~~text~~. Preserve all already-answered history. Return when the file is updated.

progress [<slug>]

Score research progress 0–100.

Spawn a fresh subagent. Brief:

Read <plan_path> and <context_path>. Score research progress as an integer 0–100 reflecting coverage AND depth needed for a PhD-level report. Be conservative — shallow coverage of many areas is not high progress. Output exactly one JSON object on stdout: {"percent": <int>, "rationale": "<one sentence>"}

Update progress.json with the returned percent and increment iteration. Show the percent and rationale to the user.

Notes

• This skill is for one-off planner operations. For the full loop, use /deep-research.
• All four modes spawn fresh subagents so context stays narrow — the invoking conversation is not polluted with plan/context content.