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-chat-agents skill.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
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.
Requires orchestrating-chat-agents skill to be installed. Load it first:
import sys
sys.path.insert(0, '/mnt/skills/user/orchestrating-chat-agents/scripts')
from claude_client import invoke_claude, invoke_parallel, parse_json_response
# 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 \
"How can we record neural activity?" \
--depth 3 \
--criteria "impact,novelty,feasibility" \
--output /mnt/user-data/outputs/neural_recording_tree.md
| Parameter | Default | Notes |
|---|---|---|
problem | required | Natural language problem statement |
--depth | 2 | Max recursion depth. Depth 2 ≈ 16 leaves, depth 3 ≈ 64 leaves |
--criteria | impact,novelty,feasibility | Comma-separated evaluation dimensions |
--output | tiling_tree.md | Output markdown path |
Depth guidance: Start with depth 2 to validate the problem framing. Increase to 3 only when the domain genuinely warrants it — depth 3 generates ~64 leaves and ~40 API calls.
invoke_parallel): each receives one node to split, returns MECE branches with explicit exclusion statementsinvoke_claude): single agent scores all leaves for cross-leaf consistencyParallel splitting happens level-by-level (not node-by-node), so a depth-2 tree makes only 2 API round-trips for the splitting phase regardless of branching factor.
A markdown file containing:
Good trees have:
If all leaves feel obvious, the split criteria were too coarse. Redo the tree with more precise definitions at the branch level where it went flat.
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GitHub repository access in containerized environments using REST API and credential detection. Use when git clone fails, or when accessing private repos/writing files via API.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Decide which model, effort level, and cascade shape each subagent gets, and how to keep improvement loops safe (evaluator-as-selector, stop on regression). Routes on measured cost-per-completed-task rather than per-token price, because a tier's token count varies more by task shape than price varies across tiers. Covers per-model effort semantics, the concision lever, cascade preconditions, context handoff, and watching a subagent fan-out live. Use when choosing a subagent's model or effort, when choosing how to escalate a failed attempt, or when asked which model or effort a task should get. Grounded in a 2026-07 calibration, a 2026-08 coding-cost study, a 2026-09 seeded-bug repair battery, a 2026-10 Haiku 5.5 ladder priced per spawn, and a 2026-10 retry-versus-escalation ladder on 295 SWE-bench Verified tasks; Managed Agents API specifics are operational, not calibrated.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Securely manages API credentials for multiple providers (Anthropic Claude, Google Gemini, GitHub). Use when skills need to access stored API keys for external service invocations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guidance for asking clarifying questions when user requests are ambiguous, have multiple valid approaches, or require critical decisions. Use when implementation choices exist that could significantly affect outcomes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Pre-change blast-radius report for a symbol or file. Walks tree-sitting references, augments with a plain-text scan over non-parsed files (configs, plain docs), and clusters affected sites by feature (`_FEATURES.md`) or top-level package. Use when about to refactor, rename, or delete something in a repo you don't own — "what breaks if I change `validateUser`", "who calls this", "is this safe to remove", "where is this used", "blast radius", "impact analysis". This is the CONVERGENT pre-change risk skill — for "what is this repo?" use exploring-codebases; for "where is X?" use searching-codebases.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Read Bluesky/ATProto without depending on Bluesky's AppView — interactions on a user's posts, thread replies, any repo's records, network-wide backlinks across every lexicon, an account's handle and PDS history, which collections are active network-wide, and layer-by-layer outage diagnosis. Use when bsky.app or the AppView is down or slow, when a Bluesky read returns timeouts or 5xx, when asked who liked/replied/quoted/reposted something, when pulling live Bluesky context cheaply, when reading records straight from a PDS, when asked who references a record or account anywhere on the network, whether an account has changed handle or migrated servers, or which lexicons and non-Bluesky apps are active on atproto. Complements browsing-bluesky, which routes everything through the AppView and fails when it does.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.