estimate-performance-headroom
SkillDev toolsLets your agent estimate how much performance improvement is left before hitting practical or theoretical limits.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the estimate-performance-headroom skill
About this skill
Estimate practical/human/theoretical ceilings and remaining performance headroom under stated assumptions.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/estimate-performance-headroom/SKILL.md and read by ahel’s review.
Purpose
Estimate practical, human, or theoretical ceilings and remaining performance headroom under stated assumptions.
Input contract
required: [current_performance, ceiling_reference, metric_schema]
optional: [human_baseline, oracle_bound, task_constraints, uncertainty_model]
constraints: [each ceiling must name its population, conditions, and assumptions]
Procedure
- Define the relevant ceiling and align its metric and conditions with current performance.
- Estimate the gap and uncertainty to each applicable ceiling.
- Separate attainable, theoretical, and assumption-dependent headroom.
- Identify evidence needed to reduce the dominant uncertainty.
Output contract
produces: [ceiling_estimates, headroom_estimates, assumption_register, uncertainty_priorities]
delta_fields: [findings, evidence_updates, uncertainties, recommended_jumps]
Quality gates
- Ceiling and current score are comparable.
- Headroom is not reported as an absolute fact when assumptions dominate.
Failure and counterexamples
Do not use an upper-bound theorem as a practical ceiling or equate benchmark maximum with human or task optimum.
Provenance map
resolved: headroom-estimation
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
estimate-performance-headroom- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine