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/analyze-temporal-trajectory

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.

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de-anthropocentric-research-engine
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-temporal-trajectory --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/analyze-temporal-trajectory

Context preview

The summary Claude sees to decide when to auto-load 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.

SKILL.md

analyze-temporal-trajectory.SKILL.md
name: analyze-temporal-trajectory
description: "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."

analyze-temporal-trajectory

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

1. Normalize records by entity, metric, and time index and identify the observed frontier. 2. Fit the caller-selected trajectory representation and inspect residuals and regime changes. 3. Detect inflection points, persistence, narrowing, widening, or stable segments. 4. If forecast mode is enabled, extrapolate with confidence bands and explicit caveats. 5. 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-construction` lines 66-69: Progress curves constructed 2; years of history covered 3; inflection points identified 1; headroom estimates produced 2.
  • `progress-curve-construction` lines 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. |

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The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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Repo: yogsoth-ai/de-anthropocentric-research-engine