detect-performance-discrepancy
SkillProductivityLets your agent compare benchmark scores for the same method across sources and explain why they differ.
Use detect-performance-discrepancy in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add detect-performance-discrepancy and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the detect-performance-discrepancy skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
About this skill
Detect material score discrepancies for the same method/task across sources and propose likely explanatory condition differences.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/detect-performance-discrepancy/SKILL.md and read by ahel’s review.
Purpose
Detect material score discrepancies for the same method or task across sources and identify plausible condition differences.
Input contract
required: [performance_records, method_key, task_key, metric_schema]
optional: [protocol_records, condition_schema, uncertainty_estimates]
constraints: [comparisons require aligned metric direction and declared conditions]
Procedure
- Align records by method, task, metric, and observation context.
- Quantify score differences with uncertainty and identify materially different pairs.
- Compare datasets, prompts, evaluators, budgets, and protocol conditions.
- Rank plausible explanations and retain unresolved alternatives.
Output contract
produces: [discrepancy_pairs, condition_difference_map, explanation_candidates, residual_uncertainties]
delta_fields: [findings, evidence_updates, uncertainties, open_questions]
Quality gates
- Materiality uses a declared comparison basis.
- Protocol mismatch is separated from method change.
Failure and counterexamples
Do not call rounding noise a discrepancy or infer a method improvement from non-equivalent evaluation conditions.
Provenance map
resolved: discrepancy-identificationresolved: discrepancy-analysis
Signals
- GitHub stars
- 503
- Forks
- 42
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
detect-performance-discrepancy- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine
github.com/yogsoth-ai/de-anthropocentric-research-engine
More in Productivity
Skill · coreyhaines31
More in Productivitygws-calendar
Skill · googleworkspace
More in Productivitylark-workflow-standup-report
Skill · larksuite
More in Productivitywriting-plans
Skill · obra
More in Productivityenergy-procurement
Skill · affaan-m
More in Productivityhomelab-pihole-dns
Skill · affaan-m
More in Productivity