Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add oaustegard/claude-skills --skill tiling-treegit clone --depth 1 https://github.com/oaustegard/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oaustegard/claude-skills/tiling-tree)<a href="https://agentmods.dev/skills/oaustegard/claude-skills/tiling-tree"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/tiling-tree/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oaustegard/claude-skills/tiling-tree"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/tiling-tree.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00104 | $0.00872 |
| Opus 5.5 | $0.00042 | $0.00349 |
| Sonnet 5.5 | $0.00021 | $0.00174 |
| Haiku 4.5 | $0.00010 | $0.00087 |
Grade A, and why
tiling-tree scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 15d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-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
Running the Tiling Tree
# 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
Parameters
| 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.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 15d ago Changed · +1 tokens per session 563fb67ac6d6
- 19d ago Changed 283398aecb90
- 1mo ago First seen · 84 lines · 103 tokens per session scan A 90f419499547
tiling-tree is a skill published in the GitHub repository oaustegard/claude-skills (155 stars, last pushed yesterday), licensed MIT. It adds 104 tokens to every session and 872 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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