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.
Interrogate a demand forecast before the business commits supply and inventory to it.
Install Demand Forecast Review
$ npx skills add mohitagw15856/pm-claude-skills --skill demand-forecast-review -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mohitagw15856/pm-claude-skills demand-forecast-review --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/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/demand-forecast-review .claude/skills/demand-forecast-review && 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 "demand-forecast-review" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/demand-forecast-review into .claude/skills/demand-forecast-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "demand-forecast-review", 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/mohitagw15856/pm-claude-skills/tree/main/skills/demand-forecast-reviewType 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 mohitagw15856/pm-claude-skills --skill demand-forecast-review -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mohitagw15856/pm-claude-skills demand-forecast-review --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/demand-forecast-review .agents/skills/demand-forecast-review && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "demand-forecast-review" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/demand-forecast-review into .agents/skills/demand-forecast-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "demand-forecast-review", 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 mohitagw15856/pm-claude-skills --skill demand-forecast-review -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mohitagw15856/pm-claude-skills demand-forecast-review --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/demand-forecast-review .cursor/skills/demand-forecast-review && 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 "demand-forecast-review" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/demand-forecast-review into .cursor/skills/demand-forecast-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "demand-forecast-review", 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/mohitagw15856/pm-claude-skills.git --path skills/demand-forecast-review--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 mohitagw15856/pm-claude-skills --skill demand-forecast-review -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mohitagw15856/pm-claude-skills demand-forecast-review --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/demand-forecast-review .gemini/skills/demand-forecast-review && 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 "demand-forecast-review" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/demand-forecast-review into .gemini/skills/demand-forecast-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "demand-forecast-review", 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 mohitagw15856/pm-claude-skills demand-forecast-reviewInstalls 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 mohitagw15856/pm-claude-skills --skill demand-forecast-review -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/demand-forecast-review .github/skills/demand-forecast-review && 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 "demand-forecast-review" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/demand-forecast-review into .github/skills/demand-forecast-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "demand-forecast-review", 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 mohitagw15856/pm-claude-skills --skill demand-forecast-review -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mohitagw15856/pm-claude-skills demand-forecast-review --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/demand-forecast-review .opencode/skills/demand-forecast-review && 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 "demand-forecast-review" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/demand-forecast-review into .opencode/skills/demand-forecast-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "demand-forecast-review", 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
demand-forecast-review- GitHub stars
- 1.4k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 796 words
- Files
- 1
- Skills in repo
- 1,348
- Repo updated
- First seen
- Licence
- MIT
At a glance
Interrogate a demand forecast before the business commits supply and inventory to it.
- Asked to review a demand plan
- SKILL.md covers What This Skill Produces, Required Inputs, Interrogation Framework and Output Format, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Challenge a forecast
What it does
Demand Forecast Review is an agent skill from mohitagw15856/pm-claude-skills. Interrogate a demand forecast before the business commits supply and inventory to it. Use when asked to review a demand plan, challenge a forecast, check forecast accuracy, decompose baseline vs uplift, or find hockey sticks in the numbers. Produces a forecast credibility review with baseline/uplift decomposition, MAPE and bias history, hockey-stick flags, an assumption register, and consensus-vs-statistical divergence analysis.
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Forecasting and time series. The repository describes itself as: 1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in… The licence is MIT.
When your agent uses it
- Asked to review a demand plan
- Challenge a forecast
- Check forecast accuracy
- Decompose baseline vs uplift
Example prompts
- “/demand-forecast-review”
What it can do on your machine
Read from SKILL.md and the folder at commit 1cbf1f0. 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.
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
Demand Forecast Review loads about 1.5k tokens when it runs. Until then it costs about 114 tokens; SKILL.md has 796 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 mohitagw15856/pm-claude-skills at commit 1cbf1f0, republished under its MIT licence (© mohitagw15856). 796 words, ~1,516 tokens.
Download SKILL.mdSave it as
.claude/skills/demand-forecast-review/SKILL.md (or your agent's skills folder).- name
- demand-forecast-review
- description
- Interrogate a demand forecast before the business commits supply and inventory to it. Use when asked to review a demand plan, challenge a forecast, check forecast accuracy, decompose baseline vs uplift, or find hockey sticks in the numbers. Produces a forecast credibility review with baseline/uplift decomposition, MAPE and bias history, hockey-stick flags, an assumption register, and consensus-vs-statistical divergence analysis.
Demand Forecast Review Skill
Every unit of forecast becomes purchase orders, capacity commitments, and inventory. This skill interrogates a forecast the way a supply planner must: separate the defensible baseline from hopeful uplift, confront the forecast with its own accuracy history, hunt for hockey sticks, and register every assumption so that when the number misses, you know which belief broke.
What This Skill Produces
- A baseline vs. uplift decomposition (statistical base + named uplift layers)
- Forecast accuracy history: MAPE and bias, with what they imply for buffering
- Hockey-stick and pattern-anomaly flags
- An assumption register with owner, evidence strength, and expiry
- Consensus vs. statistical divergence flags with a burden-of-proof call
- An overall credibility verdict: plan to it / plan to it with buffers / send back
Required Inputs
Ask for these if not provided:
- The forecast — by product/family and period, over the horizon under review
- History — actuals for the trailing 12+ months; prior forecasts vs. actuals if available (for MAPE/bias)
- Uplift drivers — promotions, launches, new customers, pipeline deals baked into the number
- Who built it — statistical, sales-driven, consensus; and what changed since last cycle
- Decision at stake — what the forecast will commit (buy, build, capacity) and its lead time
With no accuracy history, review structure and assumptions and state plainly: [accuracy unknown — treat forecast as unvalidated]. Never present conclusions as if history existed.
Interrogation Framework
1. Decompose baseline vs. uplift. Baseline = what history alone supports (trend + seasonality). Everything above it is an uplift layer that must be named: which promotion, which customer, which launch. Compute uplift share of total — above ~30% uplift, the forecast is a sales plan wearing a forecast's clothes, and each layer needs its own evidence.
2. Confront accuracy history.
| Metric | Read it as | Action threshold |
|---|---|---|
| MAPE (lag matched to decision lead time) | Noise level | >30% at family level: forecast can't carry item-level commitments |
| Bias (signed error, running) | Systematic lean | Same sign 3+ consecutive periods: correct the input, don't buffer around it |
Persistent over-forecast bias means excess inventory is being manufactured upstream; persistent under-forecast means service failures are planned in. Name which one this forecast has.
3. Hunt hockey sticks. Flag: quarter-end/year-end spikes with no order-book support; growth rates that jump beyond trailing actuals precisely when the plan needs them to; a ramp that has slipped right by one quarter in each successive cycle (the sliding hockey stick — the strongest sell-back signal there is).
4. Register assumptions. Every uplift and step-change gets a row: assumption, owner, evidence (order book / customer commitment / pipeline / hope), the period when reality will confirm or kill it, and the volume at stake if it fails.
5. Flag consensus vs. statistical divergence. Where consensus overrides the statistical line by >10%, the override carries the burden of proof. Check the track record: have past overrides beaten the stat model? If overrides historically added error, recommend planning supply to the statistical line and treating the delta as upside to option, not to stock.
Show full SKILL.md (312 more words)Show less
Output Format
Forecast Review: [scope / cycle]
1. Verdict — plan to it / plan with stated buffers / send back for rework, and the one-paragraph why.
2. Decomposition — table: Period | Baseline | Uplift layer(s) | Total | Uplift %.
3. Accuracy history — MAPE and bias at the decision lag, trend, and the buffering implication.
4. Flags — hockey sticks, sliding ramps, anomalies vs. history, each with the volume at stake.
5. Assumption register — Assumption | Owner | Evidence strength (committed / probable / speculative) | Confirms by | Units at stake.
6. Divergence analysis — consensus vs. statistical by family; where overrides exceed 10%, the recommendation on which line supply should plan to.
7. Questions for the demand owner — the 3–5 questions that must be answered before commitment.
Quality Checks
- Baseline and uplift separated, with uplift % computed and every layer named
- Accuracy metrics computed at the lag matching the commitment lead time — or absence declared
- Bias reported as signed and directional, not folded into MAPE
- Every hockey-stick flag cites the pattern evidence (order book, prior-cycle slippage)
- Every speculative assumption has an owner and a confirm-by date
- Verdict states what supply should actually plan to, not just critique
Anti-Patterns
- Do not present a forecast without its historical accuracy — a number with no track record is a guess with a spreadsheet
- Do not treat persistent bias as noise to buffer — a lean that repeats is an input error to fix at source
- Do not let uplift hide inside the baseline — unnamed uplift is unaccountable uplift
- Do not accept "the ramp moved right but the year is intact" without flagging it — sliding ramps rarely land
- Do not judge accuracy at aggregate level for item-level buys — mix error is where the money is lost
- Do not soften the verdict to keep the S&OP meeting comfortable — supply commits real cash to this number
Example Trigger Phrases
- "Review a demand plan."
- "Challenge a forecast."
- "Check forecast accuracy."
- "Find hockey sticks in the numbers."
© mohitagw15856, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/demand-forecast-review of mohitagw15856/pm-claude-skills.
Open the folder on GitHubat commit 1cbf1f0
Compare with similar skills
Demand Forecast Review 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 |
|---|---|---|---|---|---|---|
| Demand Forecast Review this skillmohitagw15856/pm-claude-skills | 1.4k | — | ~1.5k | Automated safety check: Pass | MIT | |
| 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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Related
Categories
Questions about Demand Forecast Review
What does Demand Forecast Review do?
Interrogate a demand forecast before the business commits supply and inventory to it. Demand Forecast Review is an agent skill from mohitagw15856/pm-claude-skills. Interrogate a demand forecast before the business commits supply and inventory to it.
When should I use Demand Forecast Review?
Demand Forecast Review fits situations like: asked to review a demand plan; challenge a forecast; check forecast accuracy; decompose baseline vs uplift.
How do I install Demand Forecast Review in Claude Code?
Run `npx skills add mohitagw15856/pm-claude-skills --skill demand-forecast-review -a claude-code`. Or copy the skill folder (skills/demand-forecast-review in mohitagw15856/pm-claude-skills) into .claude/skills/demand-forecast-review in your project. Claude Code loads it when a task matches its description.
How do I install Demand Forecast Review in Codex?
Run `npx skills add mohitagw15856/pm-claude-skills --skill demand-forecast-review -a codex`. Or copy the skill folder (skills/demand-forecast-review in mohitagw15856/pm-claude-skills) into .agents/skills/demand-forecast-review in your project. Codex loads it when a task matches its description.
Can I use Demand Forecast Review 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 mohitagw15856/pm-claude-skills --skill demand-forecast-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/demand-forecast-review, .gemini/skills/demand-forecast-review, .github/skills/demand-forecast-review and .opencode/skills/demand-forecast-review in your project.
What does Demand Forecast Review need to run?
SKILL.md names no scripts, command-line tools or credentials: Demand Forecast Review is instructions for the agent only.
Does Demand Forecast Review 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 Demand Forecast Review 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 Demand Forecast Review use?
Demand Forecast Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Demand Forecast Review use?
About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Demand Forecast Review?
Skills that share tags, products or a category with Demand Forecast Review: 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 Demand Forecast Review?
mohitagw15856 (a GitHub user) maintains it in mohitagw15856/pm-claude-skills, which has 1,434 GitHub stars. The repository holds 1,348 skills in this directory. The repository was last updated on October 9, 2026.
Source: mohitagw15856/pm-claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.