abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
@yogsoth-aiInstall SkillAutomation
Skill
/estimate-performance-headroom
Estimate practical/human/theoretical ceilings and remaining performance headroom under stated assumptions.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill estimate-performance-headroom --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill estimate-performance-headroom -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill estimate-performance-headroom -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill estimate-performance-headroom -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill estimate-performance-headroom -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill estimate-performance-headroom -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill estimate-performance-headroom -a windsurf -y
SKILL.md
estimate-performance-headroom.SKILL.mdname: estimate-performance-headroom
description: "Estimate practical/human/theoretical ceilings and remaining performance headroom under stated assumptions."
estimate-performance-headroom
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
1. Define the relevant ceiling and align its metric and conditions with current performance. 2. Estimate the gap and uncertainty to each applicable ceiling. 3. Separate attainable, theoretical, and assumption-dependent headroom. 4. 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`
Read more
name: estimate-performance-headroom description: "Estimate practical/human/theoretical ceilings and remaining performance headroom under stated assumptions."
estimate-performance-headroom
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
1. Define the relevant ceiling and align its metric and conditions with current performance. 2. Estimate the gap and uncertainty to each applicable ceiling. 3. Separate attainable, theoretical, and assumption-dependent headroom. 4. 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`
Ships withde-anthropocentric-research-engine
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
512200 skills
Stars on GitHub
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- 512
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Questions about estimate-performance-headroom.
- Which coding agents can use estimate-performance-headroom?
- estimate-performance-headroom installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.