Capture — Brain-Dump Organizer

A fast-to-action skill for transforming unstructured streams of mixed thoughts, tasks, and ideas into a clean four-section actionable system with zero information loss.

Invocation Triggers

Explicit phrases (any of):

• "brain dump"
• "capture this"
• "let me dump some ideas"
• "I've got a bunch of thoughts"
• "here's everything on my mind"
• "idea dump"
• "let me just get this out of my head"
• "I need to organize my thoughts"
• "here's what I'm thinking"

Implicit signals (no phrase, but the intent is unmistakable):

• User pastes or dictates a long unstructured block of mixed ideas, tasks, plans
• Multiple unrelated thoughts in one message without organizing framing
• A wall of bullet-y text covering 3+ unrelated topics

When you detect an implicit trigger, run the skill. Do NOT ask "do you want me to organize this?" first — the dump itself IS the request.

Operating Principles (All Five Apply Always)

1. Capture everything. Zero loss. Trivial items go in; the user prunes later. Never silently drop something because it "seemed unimportant".
2. Preserve voice. If the user said "build something crazy with AI", do NOT restate as "Explore innovative AI-driven solutions." Keep the energy and the casual register. See references/voice_preservation.md for concrete anti-patterns.
3. Match output complexity to input. A 5-task dump does NOT get forced into 4 elaborate sections. See references/complexity_matching.md and the Compressed Output Pattern below.
4. Be honest about ambiguity. If you're unsure what something means, flag it. Don't guess silently.
5. No action without approval. The ONLY immediate action is the organization itself. Every offer in Section 4 waits for the user's explicit pick.

Grill-Me Mid-Organization Clarifier

Capture is fast-to-action by design. No upfront intake. The dump is enough — start organizing immediately.

The grill-me discipline applies as a single mid-organization clarifying question, asked only when one item in the dump is genuinely ambiguous between task and project, AND the misclassification would meaningfully change the output:

Quick clarification — one item in your dump could go either way. Is [X] a one-shot task or a multi-step project?

Why I'm asking: If I guess wrong on a borderline item I either bury a project as a task or inflate a task into a project that doesn't need the structure. One question per dump prevents that.

Stop condition: Max 1 clarifying question per dump. After the answer (or if no clarification was needed), deliver the four (or compressed) sections.

If the dump is unambiguous, skip the clarifier entirely.

Anti-pattern (do not do this): asking 3 clarifying questions up front. That breaks the dump-and-organize flow that makes capture useful.

Section 1: Projects & Ideas

Cluster related items into themed projects when natural clustering exists. This section also holds:

• Standalone creative sparks
• Half-formed concepts
• "What if" thoughts
• Embedded decisions (Decide: X or Y) and open questions (Q: ...) — kept WITHIN the relevant project, NOT extracted into a separate top-level category

Format per project:

### {Project name in user's voice}

- {component / sub-idea}
- {component}
- Q: {open question this project needs answered}
- Decide: {decision this project requires}

Use the user's words for the project name. If the user wrote "ai dating app for ferrets", do NOT rename it to "AI-Powered Pet Companion Platform".

Section 2: Tasks

Flat, scannable, action-oriented. Includes:

• Explicit todos
• Decisions framed as Decide: ...
• Open questions framed as Resolve: ...

If a task belongs to a project from Section 1, append [Project: X] to link it — but don't repeat the project's context.

Format:

- {task in imperative voice}  [Project: X if related]
- Decide: {decision}  [Project: X if related]
- Resolve: {open question}
- ...

Section 3: Connections

This is where the skill earns its keep — and where fabrication is forbidden.

Workflow:

1. Inventory the workspace — Glob for filename patterns matching dump keywords, Grep for content matches, read the top-level directory structure. Use scripts/workspace_inventory.py to do this deterministically.
2. Match dump items to existing content — files / folders relating to dumped items, prior thinking in documents, in-progress projects with overlap.
3. Surface dependencies within the dump — items that affect each other, themes, ordering implications.
4. Be honest about inaccessibility — if you can't inspect the workspace (no filesystem available, MCP not connected), say so explicitly. Do NOT make up plausible-sounding connections.

Hard rule: NEVER fabricate connections. Only surface ones actually found by Glob/Grep/Read. If no real connections exist:

Connections: No connections found — workspace inventory clean.

If the workspace is inaccessible:

Connections: No workspace accessible from here. If you're running this from Claude Code or have a project with files attached, I can fill this in. Want to share where this work lives?

See references/workspace_detection.md for the per-context detection-tactic catalog.

Section 4: How I Can Help

Concrete offers, not abstract possibilities. Every offer specifies what would be produced AND where it would go.

✅ Right pattern	❌ Anti-pattern
"I can research Consensus MCP integration patterns and give you 3 options. Output: docs/consensus-options.md."	"You might want to look into integration approaches."
"I can draft the Q3 launch plan as a 1-pager. Output: chat reply, then docs/q3-launch.md if you want it filed."	"Maybe think about Q3 planning."
"I can scaffold the new auth module with the existing pattern from src/users/. Output: 4 files in src/auth/."	"We could explore auth options."

End with the directive question:

Which of these should I tackle?

Compressed Output Pattern

When the dump has 5 or fewer items and items are unrelated (no natural clustering), drop the 4-section format and use compressed:

## What I heard

- {item}
- {item}
- {item}
- ...

## How I can help

- {concrete offer with what + where}
- {concrete offer with what + where}

Which should I tackle?

The trigger is the complexity_estimator.py recommendation OR your judgment when no clusters exist. See references/complexity_matching.md for worked examples of when each format applies.

Workspace Detection Strategy

Context	Detection method
Claude Code CLI	Glob for files matching dump keywords; Grep for content matches; read top-level structure. Use scripts/workspace_inventory.py.
Claude.ai with project	Check project knowledge files for thematic overlap. List file titles; surface matches by keyword.
Connected tools (Notion, Drive, etc.)	Search via MCP if available. See Notion MCP section below.
No accessible workspace	State the limitation explicitly; ask user about their setup; do NOT fabricate.

Notion MCP (Optional)

This skill integrates with the Notion MCP connector when available. After delivering the organized sections to chat, attempt notion-query-data-sources. If it returns results, Notion is connected — run the save flow below. If it fails or is unavailable, skip silently and append the alert.

Save Flow (If Notion Connected)

Run this after the 4-section (or compressed) output is delivered and the user has not yet picked a Section 4 offer — it is a parallel background action, not a gate.

1. Call notion-search with keywords from the top Projects and Tasks to detect existing matching pages in the workspace
2. For each Project/Idea in Section 1: check if a matching project page already exists
   • If yes: surface the match to the user ("Found existing page: [title] — want me to link or update it?") — do NOT auto-update without approval
   • If no: offer to create it with notion-create-pages in a Projects database
3. For each Task in Section 2: call notion-query-database-view to check if a tasks database exists
   • If yes: offer to add the tasks to that database via notion-create-pages
   • If no: offer to create a Tasks database via notion-create-database and add them
4. All Notion actions are offers, not auto-executions — present a single grouped offer at the end of Section 4:

Save to Notion? I found [N] projects and [N] tasks from your dump. I can add them to your Notion workspace — [X] matching existing pages, [Y] new entries. Say "save to Notion" to proceed, or pick a Section 4 offer instead.

5. On approval: execute notion-create-pages for approved items, confirm with page links

Fallback (If Notion Not Connected)

💡 Notion not connected — your organized brain dump is output to chat only. Connect the Notion MCP connector to automatically save projects and tasks to your workspace. Setup: notion-mcp-server

Approval Gate

After the four (or compressed) sections are delivered:

• Wait for the user's explicit pick before doing anything else.
• If the user says "go" without picking a specific offer: honor it, but explicitly note any items you weren't 100% sure about so they can correct.
• The organization itself is the only auto-action. Every Section 4 offer requires green light.

Error Handling

Situation	Behavior
Workspace inaccessible	State this; skip Section 3 or surface "no workspace accessible" + ask about setup
Dump is very short (3-5 items)	Use compressed output; don't force 4 sections
Items are highly ambiguous	Flag in output, ask up to 1 clarifier (or skip clarifier and surface ambiguity in delivery)
Dump contains sensitive info	Acknowledge but don't echo verbatim if user asks for organization without quoting
Conflicting items in the dump	Surface the conflict in Section 1 or 3 explicitly (Conflict: X says A, Y says B)
User says "go" before approval	Honor it, but explicitly note items you weren't sure about

Tooling

Script	Role
scripts/workspace_inventory.py	Glob+Grep helper for Section 3. python workspace_inventory.py --root . --keywords "k1,k2" returns matches by keyword + folder structure.
scripts/dump_classifier.py	Regex-classifies each dump line into task / decision / question / idea / project-component. Heuristic — override with judgment.
scripts/complexity_estimator.py	Counts items, detects clustering signal, recommends format=full or format=compressed.

References

• references/workspace_detection.md — context-specific detection tactics (CLI / web / MCP / inaccessible)
• references/voice_preservation.md — corporate-speak anti-patterns with concrete examples
• references/complexity_matching.md — compressed vs full output, worked examples

Anti-Patterns To Reject

• Fabricating workspace connections that weren't actually Glob/Grep-verified
• Dropping items deemed "trivial" — capture everything, let the user prune
• Corporate-ifying the user's casual language
• Forcing 4-section structure when input is small (5 simple tasks doesn't need it)
• Acting on Section-4 offers immediately without approval
• Splitting decisions/questions into a separate top-level category instead of embedding them in the relevant project
• Vague Section-4 offers ("you might want to consider…")
• Asking 3+ clarifying questions up front (breaks fast-to-action)