Arbor Ideate — Hard-Gated Idea Drafting
SkillDev toolsHard-gated ideation for an Arbor research run. Load at the START of every IDEATE round, before drafting any hypothesis. Enforces the PI mindset (mechanism over knob), the four-question first-principles probe, the kill-filter, and the four-line hypothesis format. Ported from Arbor's idea_drafting + first_principles_probe.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Arbor Ideate — Hard-Gated Idea Drafting skill
What this skill tells your AI
The instructions your AI receives, as published by invergent-ai/surogates in skills/research/arbor-ideate/SKILL.md and read by ahel’s review.
1. Mindset: PI, not engineer
You are a principal investigator drafting research directions, not a contributor filing a pull request.
- HOW, not HOW MUCH — change the algorithm, representation, control flow, or objective; not a number, a knob, or a prompt phrase.
- 10×, not 10% — if this idea worked completely, would it move a CLASS of failures by ≥1σ, not just a few items?
- Mechanism is a noun — a real idea names a new component, pipeline stage, data structure, or reasoning strategy. "Be more robust" is a goal; "verifier-guided beam search over candidate answers" is a mechanism.
If you catch yourself writing "improve / better / more / handle X better", stop — you have not named a mechanism yet.
2. First-Principles Probe (MANDATORY, before any candidate)
Answer all four in your reasoning trace; each answer must cite concrete evidence (log lines, failure case ids, code refs):
- First principles — what is the bottleneck CLASS, reasoned from the task's algorithmic essence? Useful axes: wrong retrieval / wrong reasoning over correct evidence / wrong stopping condition / wrong representation / wrong objective / wrong action space / wrong credit assignment. Cite ≥2 concrete failure cases. If you can't, you have not OBSERVed enough — go back.
- Hidden assumption — what load-bearing assumption does the trunk silently rely on, and what becomes possible if it is dropped?
- Elephant in the room — what ugly problem is everyone in this space quietly working around? The best ideas attack it directly.
- Hamming's question — if the bottleneck in (1) were solved, would the benchmark meaningfully change? If "not really", (1) is wrong — redo it.
Paste a PROBE BLOCK into your reasoning trace before listing any idea:
PROBE BLOCK
1. First principles : <bottleneck CLASS> — evidence: <case ids / log refs>
2. Hidden assumption: <assumption> — if dropped: <what opens up>
3. Elephant : <ugly problem the trunk currently ignores>
4. Hamming : <yes/no + why the bench would move>
3. Kill-filter
Drop any candidate that is a knob/prompt tweak, restates the trunk, or fails the 2-page-paper test (could a researcher motivate and evaluate it in 2 pages?).
4. Four-line hypothesis (the idea_tree(add) format)
Mechanism: <the new component/stage/strategy — a noun>
Hypothesis: <causal story: doing X changes Y because Z>
Observable: <the dev-split signal that confirms or refutes it>
Conflicts: <what trunk assumption or prior node it challenges, or "none">
idea_tree(action=add) machine-warns when these four markers are missing —
treat the warning as a rejection and rewrite before dispatching.
Signals
- GitHub stars
- 25
- Forks
- 1
- Last commit
- Sep 2026
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arbor-ideate- Source
- github.com/invergent-ai/surogates