Individual skill

deep-trajectory-analysis

Reconstruct and visually analyze paired agent, game, or policy trajectories to determine whether changed actions produced their intended effects. Use for move-history audits, replay/trace analysis, policy regressions, behavior calibration, causal first-divergence studies, reply-survival analysis, agent personality validation, promotion decisions, and reports that must connect aggregate outcomes to exact state-action sequences.

deep-trajectory-analysis/SKILL.md

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Repository stars (not skill ratings): 198
Repository last updated: Nov 9, 2026
License: MIT

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agents/openai.yamlasset · 280 bytes
references/evidence-contract.mddoc · 4,861 bytes
scripts/validate-report.mjsruns · 4,895 bytes

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# Deep Trajectory Analysis

Determine not merely whether a candidate scored better, but what it changed, whether the named
mechanism fired, how the environment replied, and whether the benefit survived to the final
outcome.

Read [references/evidence-contract.md](references/evidence-contract.md) before defining metrics or
building the report. Run [scripts/validate-report.mjs](scripts/validate-report.mjs) on a
machine-readable result that follows that contract.

## Establish the causal question

Write one sentence for each intended effect before inspecting favorable examples: