analyze-temporal-trajectory
SkillDev toolsLets your agent analyze claude skill trajectories: time-series trends, regime changes, inflection points, and forecasts with uncertainty.
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 analyze-temporal-trajectory skill
About this skill
Analyze an ordered temporal trajectory, including persistence/narrowing/widening tests, regime changes, inflection points, and optional extrapolation with uncertainty. Entity type, window, and forecast mode are parameters.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/analyze-temporal-trajectory/SKILL.md and read by ahel’s review.
Purpose
Analyze an ordered temporal trajectory for persistence, narrowing or widening, regime changes, inflection points, and optional uncertain extrapolation.
Input contract
required: [time_indexed_records, entity_type, metric, window]
optional: [forecast_mode, uncertainty_model, grouping_keys, event_markers]
constraints: [records are ordered by time; metric direction is explicit; extrapolation is optional and must carry uncertainty]
Procedure
- Normalize records by entity, metric, and time index and identify the observed frontier.
- Fit the caller-selected trajectory representation and inspect residuals and regime changes.
- Detect inflection points, persistence, narrowing, widening, or stable segments.
- If forecast mode is enabled, extrapolate with confidence bands and explicit caveats.
- Return trajectory data, regime annotations, and uncertainty.
If the trajectory suggests an abrupt regime change rather than smooth variation, consider detect-breakpoint as the next tactic.
Output contract
produces: [trajectory_series, fitted_model, inflection_points, regime_labels, forecast, uncertainty_notes]
delta_fields: [findings, evidence_updates, decisions, uncertainties]
Quality gates
- Preserve source minimum yield where the progress-curve mode applies: 2 progress curves, 3 years of history, 1 inflection point, and 2 headroom estimates.
- Retain filing time series, lifecycle/S-curve fitting, historical SOTA curve fitting, milestone projection with confidence intervals, and 2/5/10-year persistence-window comparisons when those modes are requested.
- Forecasts must state model, time window, confidence band, and uncertainty; do not turn a trend into a causal claim.
Parameterization
The caller must provide the entity schema, time window, metric and direction, grouping keys, frontier rule, model/forecast mode, uncertainty method, and event markers. For persistence testing provide the requested windows; for filing or SOTA analysis provide dates and comparable score records.
Failure and counterexamples
Reject unsorted or incomparable records, forecasts without uncertainty, or regime labels unsupported by the observed series. Do not infer a future milestone when the metric definition changes across time.
Provenance map
- resolved: knowledge-acquisition/trend-analysis
- resolved: knowledge-acquisition/progress-curve-fitting
- resolved: knowledge-acquisition/progress-curve-fitting
- resolved: knowledge-acquisition/progress-curve-construction
- resolved: experiment-execution/timeline-projection
- intermediate: Pass3/analyze-filing-trend
- intermediate: Pass3/fit-research-progress-curve
- intermediate: Pass3/project-research-timeline
- resolved: deep-insight/temporal-sensitivity-testing
Verbatim source criteria excerpts
progress-curve-constructionlines 66-69: Progress curves constructed 2; years of history covered 3; inflection points identified 1; headroom estimates produced 2.progress-curve-constructionlines 50 and 60: Annotated inflection points with causal attribution; headroom estimates with confidence intervals.
Preserved source criteria ledger
| source | physical line | kind | source criterion |
|---|---|---|---|
| knowledge-acquisition/progress-curve-construction | 46 | numeric | Minimum yield: 2 progress curves, 3 years history, 1 inflection point, 2 headroom estimates. |
| deep-insight/temporal-sensitivity-testing | 12 | structural | Test persistence across the caller-supplied time windows and classify narrowing, widening, or stable. |
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
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analyze-temporal-trajectory- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine