TimesFM Forecasting
google-research/timesfm
Forecasts any univariate time series zero-shot with Google's TimesFM model, returning point forecasts and calibrated prediction intervals without training.
Predictive analytics for Dynatrace — time series forecasting with the timeseries-forecast tool, capacity saturation planning, trend and anomaly detection across hosts, services, and infrastructure.
Install Dt Obs Predictive Analytics
$ npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-predictive-analytics -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Dynatrace/dynatrace-for-ai dt-obs-predictive-analytics --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Dynatrace/dynatrace-for-ai.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dt-obs-predictive-analytics .claude/skills/dt-obs-predictive-analytics && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "dt-obs-predictive-analytics" agent skill from https://github.com/Dynatrace/dynatrace-for-ai/tree/main/skills/dt-obs-predictive-analytics into .claude/skills/dt-obs-predictive-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dt-obs-predictive-analytics", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Dynatrace/dynatrace-for-ai/tree/main/skills/dt-obs-predictive-analyticsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-predictive-analytics -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Dynatrace/dynatrace-for-ai dt-obs-predictive-analytics --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Dynatrace/dynatrace-for-ai.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/dt-obs-predictive-analytics .agents/skills/dt-obs-predictive-analytics && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "dt-obs-predictive-analytics" agent skill from https://github.com/Dynatrace/dynatrace-for-ai/tree/main/skills/dt-obs-predictive-analytics into .agents/skills/dt-obs-predictive-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dt-obs-predictive-analytics", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-predictive-analytics -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Dynatrace/dynatrace-for-ai dt-obs-predictive-analytics --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Dynatrace/dynatrace-for-ai.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/dt-obs-predictive-analytics .cursor/skills/dt-obs-predictive-analytics && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "dt-obs-predictive-analytics" agent skill from https://github.com/Dynatrace/dynatrace-for-ai/tree/main/skills/dt-obs-predictive-analytics into .cursor/skills/dt-obs-predictive-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dt-obs-predictive-analytics", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Dynatrace/dynatrace-for-ai.git --path skills/dt-obs-predictive-analytics--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-predictive-analytics -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Dynatrace/dynatrace-for-ai dt-obs-predictive-analytics --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Dynatrace/dynatrace-for-ai.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/dt-obs-predictive-analytics .gemini/skills/dt-obs-predictive-analytics && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "dt-obs-predictive-analytics" agent skill from https://github.com/Dynatrace/dynatrace-for-ai/tree/main/skills/dt-obs-predictive-analytics into .gemini/skills/dt-obs-predictive-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dt-obs-predictive-analytics", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Dynatrace/dynatrace-for-ai dt-obs-predictive-analyticsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-predictive-analytics -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Dynatrace/dynatrace-for-ai.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/dt-obs-predictive-analytics .github/skills/dt-obs-predictive-analytics && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "dt-obs-predictive-analytics" agent skill from https://github.com/Dynatrace/dynatrace-for-ai/tree/main/skills/dt-obs-predictive-analytics into .github/skills/dt-obs-predictive-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dt-obs-predictive-analytics", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-predictive-analytics -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Dynatrace/dynatrace-for-ai dt-obs-predictive-analytics --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Dynatrace/dynatrace-for-ai.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/dt-obs-predictive-analytics .opencode/skills/dt-obs-predictive-analytics && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "dt-obs-predictive-analytics" agent skill from https://github.com/Dynatrace/dynatrace-for-ai/tree/main/skills/dt-obs-predictive-analytics into .opencode/skills/dt-obs-predictive-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dt-obs-predictive-analytics", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
dt-obs-predictive-analytics- GitHub stars
- 162
- Token cost
- ~2.3k tokens
- SKILL.md length
- 844 words
- Files
- 6 (incl. references)
- Skills in repo
- 33
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Predictive analytics for Dynatrace — time series forecasting with the timeseries-forecast tool, capacity saturation planning, trend and anomaly detection across hosts, services, and infrastructure.
- Works in 6 steps: timeseries returns arrays — one value… → arrayLast(arr) = most recent value;… → Growth = (arrayLast - arrayFirst) /… → …
- Tasks that involve Forecasting and time series
- SKILL.md covers Analysis Disciplines, Choosing the Right Detection…, When to Use This Skill and Important Constraints, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Dt Obs Predictive Analytics is an agent skill from Dynatrace/dynatrace-for-ai. Predictive analytics for Dynatrace — time series forecasting with the timeseries-forecast tool, capacity saturation planning, trend and anomaly detection across hosts, services, and infrastructure.
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/anomaly-scoring.md`, `references/capacity-forecasting.md` and `references/forecasting-analyzer.md`).
It sits in Data & Analytics, covering Forecasting and time series. The repository describes itself as: Skills, prompts, and instructions for building AI agents on top of Dynatrace production context. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Forecasting and time series
Example prompts
- “/dt-obs-predictive-analytics”
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- timeseries returns arrays — one value per time slot per entity
- arrayLast(arr) = most recent value; arrayFirst(arr) = oldest
- Growth = (arrayLast - arrayFirst) / number_of_intervals
- Always filter isNotNull(field) before sorting to avoid null ordering issues
- Use toLong() when dividing Long fields to avoid type errors
- Use dt.smartscape.* not deprecated dt.entity.* in DQL display fields; use dt.smartscape.* in by:{} grouping clauses for entity-level queries
What it can do on your machine
Read from SKILL.md and the folder at commit 4f9aa71. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are dql).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Dt Obs Predictive Analytics loads about 2.3k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 844 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from Dynatrace/dynatrace-for-ai at commit 4f9aa71, republished under its Apache-2.0 licence (© Dynatrace). 844 words, ~2,293 tokens.
Download SKILL.mdSave it as
.claude/skills/dt-obs-predictive-analytics/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- dt-obs-predictive-analytics
- description
- Predictive analytics for Dynatrace — time series forecasting with the timeseries-forecast tool, capacity saturation planning, trend and anomaly detection across hosts, services, and infrastructure.
- license
- Apache-2.0
Predictive Analytics Skill
Forecast resource saturation, detect trends, analyze anomalies, and characterize signal behavior using DQL and Dynatrace analyzer tools.
Analysis Disciplines
| # | Discipline | Use when … |
|---|---|---|
| 1 | Forecast and Prediction | Predicting future metric values for capacity planning, cost estimation, or proactive alerting |
| 2 | Detecting Changes | A metric shifted — find when the character of the signal changed, regardless of whether it crossed a limit |
| 3 | Detecting Violations | A metric is currently out of bounds — find entities that exceed or fall below an acceptable range |
| 4 | Timeseries Characteristics | Characterizing a signal's seasonality, noise level, and trend before further analysis |
Choosing the Right Detection Tool
The single most important decision: are you asking "did this metric change?" or "is this metric currently wrong?"
| Question | Tool | Why |
|---|---|---|
| "Did this metric change in the last N hours?" | timeseries-novelty-detection | Detects when the signal's character changed (spike, step, trend onset, variability shift) without requiring a known acceptable limit |
| "Which services spiked or dropped recently?" | timeseries-novelty-detection with SPIKE / CHANGE_IN_VALUES | Finds the specific entities and timestamps where change occurred; returns empty for stable signals |
| "When did CPU start trending up?" | timeseries-novelty-detection with TREND_IN_VALUES | Pinpoints the onset of a directional shift |
| "Which hosts are currently above 90% CPU?" | static-threshold-analyzer | Known fixed limit — fire alerts when exceeded |
| "Which services are currently above their usual load?" | adaptive-anomaly-detector | Learns the normal distribution from the data and flags sustained threshold violations |
| "Which services are high right now vs. their weekly pattern?" | seasonal-baseline-anomaly-detector | Accounts for time-of-day/day-of-week patterns before deciding what is anomalous |
Decision rule in plain language
- Use
timeseries-novelty-detectionwhen the question contains "changed", "shifted", "spiked", "dropped", "started", "when did", or "did anything unusual happen". The tool answers whether a change occurred and when. It requires no predefined threshold. - Use an anomaly detector (
adaptive,seasonal, orstatic) when the question is about ongoing or current state relative to an expected range: "which are highest", "who is violating", "what is above X". These tools count violation samples inside a sliding window — they confirm how long something has been bad, not whether the signal changed.
Pitfall: Running
adaptive-anomaly-detectoron a broad fleet to answer "which service changed load?" typically flags every service that has any variation, producing low-signal results. Usetimeseries-novelty-detectionfirst to identify entities where the load character genuinely shifted, then use the anomaly detectors to measure the severity of those specific signals.
When to Use This Skill
- Capacity: "Which hosts will hit 90% CPU in the next 30 days?"
- Forecast: "Forecast service request volume for the next 7 days"
- Trend: "Is memory usage growing across our Kubernetes nodes?"
- Anomaly: "Which services have unusual error rates right now?"
- Baseline: "How does today's traffic compare to last week?"
- Signal profile: "Is this metric seasonal or trending before I set up alerting?"
Show full SKILL.md (371 more words)Show less
Important Constraints
Dynatrace Forecast Analyzer supports univariate forecasting only — predicting one metric based on its own historical values. Multivariate forecasting (using multiple metrics as inputs) requires external tools (Python, R, Azure AutoML).
Tooling Rule: Run analyses using Dynatrace tools: timeseries-forecast, adaptive-anomaly-detector, seasonal-baseline-anomaly-detector, static-threshold-analyzer, and timeseries-novelty-detection. Use execute-dql for DQL queries.
Result Analysis Rule: Always analyse and summarise results directly from the raw tool output. Derive all numbers, trends, and conclusions inline.
Result Presentation Format
Always present forecast results as a structured table:
| Column | Content |
|---|---|
| Rank | 🥇 🥈 🥉 ordered by urgency or magnitude |
| Signal / Entity | Metric name and entity or dimension |
| Last Actual | Most recent non-null value from the historical series |
| Forecast | Point forecast at the end of the horizon |
| Range | Lower – Upper confidence band at the same horizon point |
| Trend | % change from Last Actual to Forecast: 🔴 >+20% / 🟠 +5–20% / 🟢 ±5% stable / 🔵 −5–20% declining / ⚫ <−20% sharp drop |
| Action | ✅ No action / ⚠️ Monitor / 🔴 Act now |
Always follow the table with a Key Findings section (3–5 bullet points, ranked by priority).
Core DQL Techniques
DQL has no native forecast function. For forward-looking forecasts, use timeseries-forecast (see references/forecasting-analyzer.md).
Key DQL Rules
timeseriesreturns arrays — one value per time slot per entityarrayLast(arr)= most recent value;arrayFirst(arr)= oldest- Growth =
(arrayLast - arrayFirst) / number_of_intervals - Always
filter isNotNull(field)before sorting to avoid null ordering issues - Use
toLong()when dividingLongfields to avoid type errors - Use
dt.smartscape.*not deprecateddt.entity.*in DQL display fields; usedt.smartscape.*inby:{}grouping clauses for entity-level queries
Standard Query Patterns
Moving Average Trend
timeseries cpu = avg(dt.host.cpu.usage), from: now()-24h, interval: 1h, by: {dt.smartscape.host}
| fieldsAdd moving_avg = arrayMovingAvg(cpu, 4)
| fieldsAdd current = arrayLast(cpu)
| fieldsAdd trend = arrayLast(cpu) - arrayFirst(cpu)
| filter isNotNull(current)
| sort trend desc
| limit 20
| fields dt.smartscape.host, current, trend, moving_avgSaturation Risk Classification
timeseries cpu = avg(dt.host.cpu.usage), from: now()-7d, interval: 1h, by: {dt.smartscape.host}
| fieldsAdd p95 = arrayPercentile(cpu, 95)
| fieldsAdd saturation_risk = if(p95 > 85, "HIGH", else: if(p95 > 70, "MEDIUM", else: "LOW"))
| filter isNotNull(p95)
| sort p95 desc
| fields dt.smartscape.host, p95, saturation_riskDays to Saturation Forecast
timeseries cpu = avg(dt.host.cpu.usage), from: now()-30d, interval: 1d, by: {dt.smartscape.host}
| fieldsAdd current = arrayLast(cpu)
| fieldsAdd daily_growth = (arrayLast(cpu) - arrayFirst(cpu)) / 30
| filter isNotNull(current)
| fieldsAdd days_to_saturation = if(daily_growth > 0, toLong((90 - current) / daily_growth), else: 9999)
| sort days_to_saturation asc
| limit 20
| fields dt.smartscape.host, current, daily_growth, days_to_saturationAnomaly Scoring
timeseries cpu = avg(dt.host.cpu.usage), from: now()-24h, interval: 1h, by: {dt.smartscape.host}
| fieldsAdd baseline_avg = arrayAvg(cpu)
| fieldsAdd current = arrayLast(cpu)
| fieldsAdd anomaly_score = if(isNotNull(current) and isNotNull(baseline_avg), abs(current - baseline_avg), else: 0)
| sort anomaly_score desc
| limit 20
| fields dt.smartscape.host, current, baseline_avg, anomaly_scoreMetric Discovery
Before forecasting, discover available metrics by keyword:
metrics from: now() - 1h
| filter contains(metric.key, "cpu")
| summarize count(), by: {metric.key}
| sort `count()` descReference Guides
references/forecasting-analyzer.md—timeseries-forecasttool: data requirements, parameter reference, interval selection, horizon limits, common pitfallsreferences/capacity-forecasting.md— CPU/memory/disk/K8s saturation forecasts; multi-resource risk scoring; days-to-saturation DQL patternsreferences/anomaly-scoring.md—adaptive-anomaly-detector,seasonal-baseline-anomaly-detector,static-threshold-analyzer; DQL deviation scoringreferences/novelty-detection.md—timeseries-novelty-detectiontool: spike, drop, step change, trend onset, and variability change detection; all novelty types; parameter reference; worked examplesreferences/trend-detection.md—timeseries-novelty-detectionfor trend onset and change points; week-over-week joins; growth rate and acceleration detection
Related Skills
- dt-dql-essentials — DQL syntax,
timeseriescommand rules, array function reference - dt-obs-hosts — Host and process metrics catalog
- dt-obs-services — Service RED metrics for service-level trend analysis
- dt-obs-problems — Davis AI problem history for anomaly correlation
© Dynatrace, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 5 other files (references) in skills/dt-obs-predictive-analytics of Dynatrace/dynatrace-for-ai.
- SKILL.md
- references/anomaly-scoring.md
- references/capacity-forecasting.md
- references/forecasting-analyzer.md
- references/novelty-detection.md
- references/trend-detection.md
Open the folder on GitHubat commit 4f9aa71
Compare with similar skills
Dt Obs Predictive Analytics next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Dt Obs Predictive Analytics this skillDynatrace/dynatrace-for-ai | 162 | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.7k | 15 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Timesfm ForecastingzLanqing/codex-claude-academic-skills | 4.7k | 3 repos | ~7.5k | Automated safety check: Notes | Apache-2.0 | |
| Find Hypertable Candidatestimescale/pg-aiguide | 1.9k | 1 repos | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Pensieve Searcharkohut/pensieve | 1.4k | — | ~8.2k | Automated safety check: Pass | Apache-2.0 |
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Categories
Questions about Dt Obs Predictive Analytics
What does Dt Obs Predictive Analytics do?
Predictive analytics for Dynatrace — time series forecasting with the timeseries-forecast tool, capacity saturation planning, trend and anomaly detection across hosts, services, and infrastructure. Dt Obs Predictive Analytics is an agent skill from Dynatrace/dynatrace-for-ai. Predictive analytics for Dynatrace — time series forecasting with the timeseries-forecast tool, capacity saturation planning, trend and anomaly detection across hosts, services, and infrastructure.
When should I use Dt Obs Predictive Analytics?
Dt Obs Predictive Analytics fits situations like: tasks that involve Forecasting and time series.
How do I install Dt Obs Predictive Analytics in Claude Code?
Run `npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-predictive-analytics -a claude-code`. Or copy the skill folder (skills/dt-obs-predictive-analytics in Dynatrace/dynatrace-for-ai) into .claude/skills/dt-obs-predictive-analytics in your project. Claude Code loads it when a task matches its description.
How do I install Dt Obs Predictive Analytics in Codex?
Run `npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-predictive-analytics -a codex`. Or copy the skill folder (skills/dt-obs-predictive-analytics in Dynatrace/dynatrace-for-ai) into .agents/skills/dt-obs-predictive-analytics in your project. Codex loads it when a task matches its description.
Can I use Dt Obs Predictive Analytics in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-predictive-analytics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dt-obs-predictive-analytics, .gemini/skills/dt-obs-predictive-analytics, .github/skills/dt-obs-predictive-analytics and .opencode/skills/dt-obs-predictive-analytics in your project.
What does Dt Obs Predictive Analytics need to run?
SKILL.md names no scripts, command-line tools or credentials: Dt Obs Predictive Analytics is instructions for the agent only.
Does Dt Obs Predictive Analytics access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Dt Obs Predictive Analytics safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Dt Obs Predictive Analytics use?
Dt Obs Predictive Analytics is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Dt Obs Predictive Analytics use?
About 2.3k tokens (SKILL.md is roughly 9.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.5k tokens, read only when the agent opens those files.
What are the alternatives to Dt Obs Predictive Analytics?
Skills that share tags, products or a category with Dt Obs Predictive Analytics: TimesFM Forecasting (google-research/timesfm, 34k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars), Timesfm Forecasting (zLanqing/codex-claude-academic-skills, 4.7k stars) and Find Hypertable Candidates (timescale/pg-aiguide, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Dt Obs Predictive Analytics?
Dynatrace (a GitHub organization) maintains it in Dynatrace/dynatrace-for-ai, which has 162 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 1, 2026.
Source: Dynatrace/dynatrace-for-ai on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.