brain-sync

name	brain-sync
description	Sync the current project's context into your AI Brain. Reads project files, compares against existing brain knowledge via progressive disclosure, and captures only new or changed information.
argument-hint	["--name <project-name>"]

Brain Sync

Sync the current project's context into the AI Brain so future conversations have up-to-date knowledge about this project.

Arguments

• $ARGUMENTS — Optional:
  • --name <project-name> — Override the auto-derived project name

Parse the name value from $ARGUMENTS if provided.

Workflow

Step 1: Gather Project Context

Read the following from the current working directory. Skip any that don't exist.

Project identity:

• README.md
• package.json, Cargo.toml, pyproject.toml, or go.mod (whichever exists)
• CLAUDE.md

Git state:

• Run git branch --show-current
• Run git log --oneline -20
• Run gh pr list --limit 10 (skip if gh is unavailable)

Project structure:

• Run ls -la at the project root

Strategic context:

• If docs/ exists, list its contents and selectively read files that reveal project direction (specs, architecture docs, roadmaps). Do not read every file.
• Read GOALS.md, TODO.md, or similar planning files if they exist.

Step 2: Derive Project Name

If --name was provided, use that. Otherwise, derive the project name using this precedence:

1. The name field from package.json / Cargo.toml / pyproject.toml
2. The first heading in README.md
3. The current directory name (fallback)

Step 3: Search Brain for Existing Knowledge (Progressive Disclosure)

3a. Triage via compact index.

Call mcp__ai-brain__search_thoughts with:

• query: the project name
• limit: 10

This returns a compact index: each hit has {id, summary, snippet, type, topics, score}. Do NOT assume full content is present — there is none; snippet is ~240 chars.

3b. Identify hydration candidates.

From the index rows, select up to 5 candidates that look materially related (by summary + snippet + topics). Discard unrelated or obviously-stale rows based on snippet alone.

3c. Hydrate.

Call mcp__ai-brain__get_thoughts with ids: [<up to 5 ids>]. This returns full content for those specific thoughts. Only these hydrated results participate in the diff.

Step 4: Synthesize and Diff

Compare the current project state (from Step 1) against the hydrated thoughts (from Step 3c):

• Identify information that is new (not in any hydrated thought)
• Identify information that has changed (contradicts or updates an existing thought)
• Identify information that is unchanged (already accurately captured)

For each unchanged fact, note the thought:<id> that already captures it — you'll reference these in the report.

Step 5: Sync to Brain

Based on the diff from Step 4:

First sync (no hydrated thoughts, or all candidates were unrelated): Capture a comprehensive project summary via mcp__ai-brain__capture_thought. Structure the content with the project name first. Example format:

Project: <name> — <one-line description>. Tech stack: <technologies>. Key features: <features>. Current status: <status>. Next steps: <direction>.

If the summary would be excessively long, split into 2-3 focused thoughts (e.g., project overview, current status/roadmap). Collect each thoughtId returned.

Subsequent syncs (hydrated thoughts found): Only capture thoughts for meaningful changes. Frame each as an update:

Update: <project-name> — <what changed> (<date>). <new status or direction>.

Skip unchanged information. If nothing meaningful has changed, capture no thoughts.

No changes: Tell the user the brain is already up to date and skip to Step 6.

Step 6: Report to User

Briefly tell the user:

• What was synced (or that everything was already current)
• How many new thoughts were captured, each cited as thought:<id>
• Key highlights of what changed — cite updates as thought:<new-id> and the prior thoughts they supersede as thought:<old-id> where applicable
• Unchanged facts cited as thought:<id> so the user can confirm coverage