tiling-tree
Exhaustive problem space exploration using the MIT Synthetic Neurobiology "tiling tree" method. Partitions a problem into MECE (Mutually Exclusive, Collectively Exhaustive) subsets recursively via parallel subagents, then evaluates leaf ideas against specified criteria. Use when users say "tiling tree", "tile the solution space", "exhaustively explore approaches to", "what are all the ways to", or request a MECE breakdown of a problem. Requires orchestrating-agents skill.
npx skills add majiayu000/claude-skill-registry --skill tiling-tree --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Tiling Tree Implements the MIT Synthetic Neurobiology tiling tree method: recursively partition a problem space into non-overlapping, collectively exhaustive subsets until reaching actionable leaf ideas, then evaluate those leaves. ## Core Concept The method's power comes from MECE splits forcing exploration of unfamiliar territory. A split is only valid when you can state precisely what each branch **excludes** — if you can't, the criterion is too vague and branches will overlap. Key insight from the source method: always look for the "third option" that falls outside an obvious binary split. The bloodstream-secretion approach to neural recording only emerged because "wired vs. wireless" was defined precisely enough to reveal it covered neither case. ## When to Use - "What are all the ways we could solve X?" - "Apply the tiling tree method to Y" - "Exhaustively map the solution space for Z" - Any request for MECE decomposition of a problem domain ## Setup Requires `orchestrating-agents` skill to be installed. Load it first: ```python import sys sys.path.insert(0, '/mnt/skills/user/orchestrating-agents/scripts') from claude_client import invoke_claude, invoke_parallel, parse_json
- Core Concept
- When to Use
- Setup
- Running the Tiling Tree
- Parameters
- Architecture
- Output
- Interpreting Results
Basic usage python3 /mnt/skills/user/tiling-tree/scripts/tiling_tree.py "Your problem here" With options python3 /mnt/skills/user/tiling-tree/scripts/tiling_tree.py \
What does the tiling-tree skill do?
Exhaustive problem space exploration using the MIT Synthetic Neurobiology "tiling tree" method. Partitions a problem into MECE (Mutually Exclusive, Collectively Exhaustive) subsets recursively via parallel subagents, then evaluates leaf ideas against specified criteria. Use when users say "tiling tree", "tile the solution space", "exhaustively explore approaches to", "what are all the ways to", or request a MECE breakdown of a problem. Requires orchestrating-agents skill.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill tiling-tree --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.