analyze-temporal-trajectory SKILL.md skillA
analyze-temporal-trajectory from yogsoth-ai/de-anthropocentric-research-engine is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add yogsoth-ai/de-anthropocentric-research-engine/analyze-temporal-trajectory@git:20260923.cbc38aaPins git:20260923.cbc38aa in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- yogsoth-ai/de-anthropocentric-research-engine
- Path
skills/analyze-temporal-trajectory/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260923.cbc38aa, committed Sep 23, 2026
- Size
- 3,899 bytes
- Repository stars
- 508
- License
- Apache-2.0
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.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# analyze-temporal-trajectory ## Purpose Analyze an ordered temporal trajectory for persistence, narrowing or widening, regime changes, inflection points, and optional uncertain extrapolation. ## Input contract ```yaml 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 ```yaml produces: [trajectory_series, fitted_model, inflection_points, regime_labels, forecast, uncertainty_notes] …
Read the whole file at its exact version.
How to install
mdr add yogsoth-ai/de-anthropocentric-research-engine/analyze-temporal-trajectory@sha256:bdd7286cfd8a7daeThe line at the top of this page pins the label git:20260923.cbc38aa, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_cte6ebgd6lped273)
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Versions
The registry has recorded 1 version of this file, committed on Sep 23, 2026.
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Source
yogsoth-ai/de-anthropocentric-research-engine · 508 stars · license Apache-2.0 · pushed 2026-10-23 · branch main
API
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