Skill reference: trend-analysis

Skill reference: trend-analysis

The trend-analysis skill authors one document genre: a trajectory report — a foresight deliverable that states the current direction of change, the observable signals behind it, the forces driving and dampening it, and the plausible forward scenarios so a reader can plan against an uncertain future. This reference describes what that document type is, how the skill produces one, when it earns its place, and the provenance behind it.

Property	Value
Authors	A trajectory report (trend analysis / foresight report)
Purpose group	Research & market intelligence
MIF conceptType	semantic
Target MIF level	3
Primary source	IFTF/WEF/APF-style foresight-report convention

What this document type is

A trend-analysis report is a foresight deliverable, not a snapshot: it exists to say where something is headed, not just where it stands. Its defining trait is the mandatory trajectory or scenario diagram — a time-series trajectory rendered as a Mermaid xychart-beta, a scenario-evolution diagram rendered as a Mermaid stateDiagram-v2, or both when the data supports it. A report with no such figure is not a conformant trend-analysis deliverable, because a trend without a time-anchored visual is narration, not analysis. The report proceeds through Trajectory (the current direction of change and its recent history), Signals (observable indicators, each cited and classed leading or lagging), Drivers & Inhibitors (the forces accelerating or dampening the trend), Scenarios (2 to 4 plausible forward paths over a stated horizon with their triggers and confidence), and Implications & Watch-list (what to monitor going forward). It follows the widely-used IFTF/WEF/APF foresight-report pattern, which is conventional practice rather than a codified standard — no standards body has formalized a foresight/trend-report format, and the report itself must say so rather than claim conformance to a named standard.

This is distinct from a fieldwork-and-sampling study of a market at a point in time (a market-research-report) and from ranking vendors on two evaluation axes (a competitive-quadrant) — both project a market’s present state rather than its trajectory over time. It is also distinct from a decision proposal: it does not scope what to build, so it projects to MIF as semantic content rather than a requirements or design document.

How the skill produces one

trend-analysis is a genre skill: it carries the trajectory-report pattern as durable instructions plus exemplars, and writes the artifact over a MIF floor so the result is at once a human-readable report and a machine-conformant unit.

• Pattern, made operational. The skill encodes the five required sections and treats the trajectory/scenario diagram as mandatory rather than optional matter, anchors every trajectory claim in time, separates observed signal from projected scenario, states confidence per scenario, and requires the convention-not-standard caveat to be stated plainly. It adds two opt-in, additive sub-structures — a STEEP/PESTLE environmental scan and a methodology appendix — that render only when explicitly requested, leaving the five-section default unchanged otherwise.
• Exemplars set the bar. Like every genre in the suite it ships good-l1.md (the MIF Level-1 floor), good.md (the Level-3 target), bad.md (a counter-example — a report that asserts a single forward scenario as settled fact with no trajectory or scenario diagram anywhere), and evals/evals.json. The check-exemplars gate proves good-l1.md validates at L1 and good.md at Level 3.
• MIF projection. The document is authored with MIF frontmatter (via the shared mif-frontmatter substrate) and a conceptType of semantic, reflecting that the report is declarative trajectory-and-scenario knowledge rather than a time-bound event or step sequence. mif-validate proves the Markdown ↔ JSON-LD round-trip is lossless before the document is considered done.

When it is beneficial

Reach for trend-analysis when the deliverable must track how something is changing and project it forward under uncertainty — a technology adoption curve, a demand trajectory, a regulatory or social shift — and a reader needs to plan against multiple plausible futures rather than a single forecast. The mandatory trajectory/scenario diagram and the signal-vs-driver discipline are the artifact’s reason to exist: they force the report to distinguish what has actually been observed from what is merely projected.

Do not use it for a fieldwork-and-sampling study of a market at a point in time — that calls for a market-research-report’s Background & Objectives, mandatory sampling and fieldwork disclosure, Findings, and Conclusions & Recommendations, not a trajectory over a time horizon. Do not use it to rank vendors in a market on two evaluation axes — that is a competitive-quadrant, built around Completeness of Vision vs. Ability to Execute and per-vendor strengths/cautions, not a trend over time. And do not use it to scope what to build and why before design — that is a prd or feature-spec. Where a comparison-table decision report is what is actually needed, use engineering instead.

Example

A trend-analysis report titled “Trend Analysis: Global Data Center Electricity Demand” opens with an as-of date and a horizon through 2030, states the Trajectory (data center electricity consumption rising steadily since 2022, driven by cloud growth and AI buildout, per the IEA’s Electricity 2024 projection of roughly doubled demand) alongside a Mermaid xychart-beta plotting the trajectory from 2022 through 2030. Signals classify hyperscaler AI infrastructure capex and utility large-load interconnection requests as leading indicators, and metered electricity consumption as a lagging one. Drivers & Inhibitors weighs continued AI workload growth and on-premise migration against grid interconnection queues, chip supply constraints, and efficiency gains. Scenarios lay out three forward paths — efficiency-tempered growth, grid-constrained plateau, and unconstrained AI buildout — each with a trigger and a confidence level, paired with a Mermaid stateDiagram-v2 showing the branch from the current trajectory to each scenario. Implications & Watch-list closes with the concrete indicators (capex guidance, interconnection queue lengths, accelerator efficiency claims) that would confirm or break each scenario, and a References list resolving the inline [1] citation to the IEA report.

Provenance & citations

• Genre source — IFTF/WEF/APF foresight-report convention: a trajectory/signals/drivers/scenarios pattern used broadly across institutional foresight practice; not a codified standards-body format, which the report itself must disclose.
• Exemplar source — IEA, Electricity 2024: the report’s good.md exemplar grounds its trajectory claim in this projection, https://www.iea.org/reports/electricity-2024.
• Skill provenance: authored by the trend-analysis skill in the mif-docs plugin, https://github.com/modeled-information-format/mif-docs-plugin; the skill’s exemplars and evals/ define and verify the pattern.
• MIF conformance: the document projects to canonical JSON-LD under the MIF specification, https://mif-spec.dev, and is proven lossless by mif-validate.
• Index: this skill is one entry in the skills by purpose catalog; its research and decision-adjacent sibling is engineering.