Analyze Temporal Trajectory

Organization

yogsoth-ai

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.

Overview

Publisher	yogsoth-ai
Repository	`de-anthropocentric-research-engine`
Skill name	`analyze-temporal-trajectory`
Stars	505
Forks	43
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by yogsoth-ai on GitHub. Read the source before you install it.

Installation

Install the Analyze Temporal Trajectory AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/analyze-temporal-trajectory
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/analyze-temporal-trajectory

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine.git /tmp/de-anthropocentric-research-engine
mkdir -p .claude/skills
cp -r /tmp/de-anthropocentric-research-engine/skills/analyze-temporal-trajectory .claude/skills/analyze-temporal-trajectory

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyze Temporal Trajectory in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analyze Temporal Trajectory on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analyze Temporal Trajectory is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

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

yaml
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.

Frequently asked questions

What does the Analyze Temporal Trajectory AI skill do?

Why use Analyze Temporal Trajectory on TypingMind?

Because you install it once and use it with any model. Analyze Temporal Trajectory is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analyze Temporal Trajectory in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/analyze-temporal-trajectory. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Analyze Temporal Trajectory?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analyze Temporal Trajectory?

As many as you like. As long as a model supports skills, you can use Analyze Temporal Trajectory with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analyze Temporal Trajectory AI skill free?

Yes. It is published on GitHub by yogsoth-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

analyze-temporal-trajectory

Purpose

Input contract

Procedure

Output contract

Quality gates

Parameterization

Failure and counterexamples

Provenance map

Verbatim source criteria excerpts

Preserved source criteria ledger

Frequently asked questions

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