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Estimate theoretical/practical ceiling vs current SOTA gap
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Append research process and results to the current Phase's context file. Each append MUST contain >=500 lines of markdown covering both process and results. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase.
Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed.
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the backprop attribution is a judgment call.
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
| name | headroom-estimation |
| description | Estimate theoretical/practical ceiling vs current SOTA gap |
| execution | subagent |
| prompt | ./prompt.md |
| input | task_name, current_sota, human_performance, theoretical_bounds |
Quantify the remaining improvement potential for a task by estimating various performance ceilings and computing the gap between current SOTA and those ceilings. Distinguishes between theoretical limits (information-theoretic), practical limits (current paradigm), and human performance baselines.
| Field | Type | Description |
|---|---|---|
| task_name | string | The target task |
| current_sota | object | {method, score, metric, dataset, date} |
| human_performance | object | {score, conditions, source} or null |
| theoretical_bounds | object | {bound_type, value, derivation} or null |
{
"task": "string",
"dataset": "string",
"metric": "string",
"ceilings": {
"theoretical": {
"value": null,
"type": "information_theoretic|bayes_optimal|combinatorial",
"derivation": "string",
"confidence": "high|medium|low|speculative"
},
"human": {
"value": null,
"conditions": "string",
"source": "string",
"is_expert": true,
"confidence": "high|medium|low"
},
"practical": {
"value": null,
"assumptions": "string",
"based_on": "string",
"confidence": "medium|low|speculative"
}
},
"headroom": {
"vs_theoretical": null,
"vs_human": null,
"vs_practical": null,
"interpretation": "string"
},
"saturation_assessment": {
"status": "saturating|active_progress|early_stage|unknown",
"evidence": "string",
"years_to_human_parity": null
}
}