Longbridge Quant
helsome/folio
Quantitative strategy frameworks: pairs trading/cointegration, volatility regime strategies, seasonality/calendar effects, multi-factor models (IC/IR), factor research and screening, correlation…
Evaluate demand-forecast quality honestly - WMAPE, bias and Forecast Value Added against a naive benchmark over a rolling-origin backtest.
Install Forecast Accuracy Review
$ npx skills add davila7/claude-code-templates --skill forecast-accuracy-review -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install davila7/claude-code-templates forecast-accuracy-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/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/operations/forecast-accuracy-review .claude/skills/forecast-accuracy-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 "forecast-accuracy-review" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/operations/forecast-accuracy-review into .claude/skills/forecast-accuracy-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "forecast-accuracy-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/davila7/claude-code-templates/tree/main/cli-tool/components/skills/operations/forecast-accuracy-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 davila7/claude-code-templates --skill forecast-accuracy-review -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install davila7/claude-code-templates forecast-accuracy-review --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .agents/skills && cp -r skills-src/cli-tool/components/skills/operations/forecast-accuracy-review .agents/skills/forecast-accuracy-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 "forecast-accuracy-review" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/operations/forecast-accuracy-review into .agents/skills/forecast-accuracy-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "forecast-accuracy-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 davila7/claude-code-templates --skill forecast-accuracy-review -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install davila7/claude-code-templates forecast-accuracy-review --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/cli-tool/components/skills/operations/forecast-accuracy-review .cursor/skills/forecast-accuracy-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 "forecast-accuracy-review" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/operations/forecast-accuracy-review into .cursor/skills/forecast-accuracy-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "forecast-accuracy-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/davila7/claude-code-templates.git --path cli-tool/components/skills/operations/forecast-accuracy-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 davila7/claude-code-templates --skill forecast-accuracy-review -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install davila7/claude-code-templates forecast-accuracy-review --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/cli-tool/components/skills/operations/forecast-accuracy-review .gemini/skills/forecast-accuracy-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 "forecast-accuracy-review" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/operations/forecast-accuracy-review into .gemini/skills/forecast-accuracy-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "forecast-accuracy-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 davila7/claude-code-templates forecast-accuracy-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 davila7/claude-code-templates --skill forecast-accuracy-review -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .github/skills && cp -r skills-src/cli-tool/components/skills/operations/forecast-accuracy-review .github/skills/forecast-accuracy-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 "forecast-accuracy-review" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/operations/forecast-accuracy-review into .github/skills/forecast-accuracy-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "forecast-accuracy-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 davila7/claude-code-templates --skill forecast-accuracy-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 davila7/claude-code-templates forecast-accuracy-review --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/cli-tool/components/skills/operations/forecast-accuracy-review .opencode/skills/forecast-accuracy-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 "forecast-accuracy-review" agent skill from https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/operations/forecast-accuracy-review into .opencode/skills/forecast-accuracy-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "forecast-accuracy-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
forecast-accuracy-review- GitHub stars
- 33k
- Token cost
- ~884 tokens
- SKILL.md length
- 413 words
- Files
- 1
- Skills in repo
- 479
- Repo updated
- First seen
- Licence
- MIT
At a glance
Evaluate demand-forecast quality honestly - WMAPE, bias and Forecast Value Added against a naive benchmark over a rolling-origin backtest.
- Works in 6 steps: Profile the demand first. Per SKU… → Set the benchmarks. Naive (last period)… → Backtest rolling-origin. One-step-ahead… → …
- The user mentions forecast accuracy
- SKILL.md covers Required data, Workflow, Pitfalls to check explicitly and Output format
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Forecast Accuracy Review is an agent skill from davila7/claude-code-templates. Evaluate demand-forecast quality honestly - WMAPE, bias and Forecast Value Added against a naive benchmark over a rolling-origin backtest. Use when the user mentions forecast accuracy, MAPE, demand planning performance, tahmin doğruluğu, talep tahmini, or asks whether a forecasting process or tool is worth it. Differentiator - judges the process (value added vs doing nothing), not just the model.
Its SKILL.md is about 880 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 and Trading and backtesting. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.
When your agent uses it
- The user mentions forecast accuracy
- Demand planning performance
- Tahmin doğruluğu
- Asks whether a forecasting process
Example prompts
- “/forecast-accuracy-review”
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Profile the demand first. Per SKU compute mean, CV and zero-period share; classify smooth / erratic / intermittent / lumpy (defaults: CV…
- Set the benchmarks. Naive (last period) always; seasonal naive when 2+ full seasons exist. These are non-negotiable controls.
- Backtest rolling-origin. One-step-ahead forecasts for each of the last 6+ periods, expanding window, using only data before each origin. A…
- Score with honest metrics
- Deliver the FVA verdict. FVA = WMAPE(naive) - WMAPE(candidate), per segment and overall. Negative FVA means the process destroys value…
- Validate. Recompute WMAPE for one model directly from the raw backtest rows and confirm it matches the table before presenting.
What it can do on your machine
Read from SKILL.md and the folder at commit c0ca7da. 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
Links to these hosts (documentation or services it may open):
github.com
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
Forecast Accuracy Review loads about 884 tokens when it runs. Until then it costs about 106 tokens; SKILL.md has 413 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 davila7/claude-code-templates at commit c0ca7da, republished under its MIT licence (© davila7). 413 words, ~884 tokens.
Download SKILL.mdSave it as
.claude/skills/forecast-accuracy-review/SKILL.md (or your agent's skills folder).- name
- forecast-accuracy-review
- description
- Evaluate demand-forecast quality honestly - WMAPE, bias and Forecast Value Added against a naive benchmark over a rolling-origin backtest. Use when the user mentions forecast accuracy, MAPE, demand planning performance, tahmin doğruluğu, talep tahmini, or asks whether a forecasting process or tool is worth it. Differentiator - judges the process (value added vs doing nothing), not just the model.
Forecast Accuracy Review
A forecast is only worth what it adds over the free alternative: shipping last period's number. Every review must answer "how many points does this process add over naive?" before any model discussion.
Required data
Per-SKU demand history at the planning bucket (usually monthly): sku, period, qty. If evaluating an existing forecast, also the forecast values with their creation dates (to avoid hindsight leakage). 18+ periods per SKU for a meaningful backtest; flag SKUs with less.
Workflow
- Profile the demand first. Per SKU compute mean, CV and zero-period share; classify smooth / erratic / intermittent / lumpy (defaults: CV 0.5 and 1.0 boundaries, intermittency at >25% zero periods - state them, adjust to natural breaks). Accuracy expectations differ by class; never report one blended number alone.
- Set the benchmarks. Naive (last period) always; seasonal naive when 2+ full seasons exist. These are non-negotiable controls.
- Backtest rolling-origin. One-step-ahead forecasts for each of the last 6+ periods, expanding window, using only data before each origin. A single train/test split is one lucky draw - do not accept it.
- Score with honest metrics:
- WMAPE = sum(|error|) / sum(actual) - the volume-weighted headline
- Bias = sum(error) / sum(actual) - direction; a fine WMAPE with persistent bias is quietly building excess stock or stockouts
- MAPE only as a footnote, and always disclose how many zero-actual periods it dropped
- Deliver the FVA verdict. FVA = WMAPE(naive) - WMAPE(candidate), per segment and overall. Negative FVA means the process destroys value - say it plainly.
- Validate. Recompute WMAPE for one model directly from the raw backtest rows and confirm it matches the table before presenting.
Show full SKILL.md (151 more words)Show less
Pitfalls to check explicitly
- MAPE with zeros: undefined on zero-actual periods; silently dropping them fakes precision on intermittent SKUs.
- MAPE asymmetry rewards under-forecasting (errors capped at 100% below, unbounded above).
- Aggregation mix: a good total can hide terrible A-item accuracy; always show the value-weighted cut.
- Lumpy segments: if WMAPE > ~100%, the honest recommendation is an inventory-policy answer (buffers, MTO), not a better model.
- Hindsight leakage: forecasts must predate actuals; check timestamps when auditing an existing process.
Output format
- Scoreboard table: model x (WMAPE, bias, MAPE-footnote), sorted by WMAPE
- FVA statement: "the process adds/destroys X points vs naive" - overall and per segment
- Segment table (pattern x best approach)
- Two or three recommendation sentences tied to segments, not globals
Worked example with five baseline models and charts: https://github.com/gulmezeren2-byte/forecast-accuracy-lab
Source: industrial-engineering-ai-skills by Eren Gulmez (MIT). The full method pack - entry skill, role agents, data-hygiene rules and artifact templates - lives there.
© davila7, 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 cli-tool/components/skills/operations/forecast-accuracy-review of davila7/claude-code-templates.
Open the folder on GitHubat commit c0ca7da
Compare with similar skills
Forecast Accuracy 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 |
|---|---|---|---|---|---|---|
| Forecast Accuracy Review this skilldavila7/claude-code-templates | 33k | — | ~884 | Automated safety check: Pass | MIT | |
| Longbridge Quanthelsome/folio | 271 | 1 repos | ~1.6k | Automated safety check: Pass | MIT | |
| Quant Statistical MethodsHKUDS/Vibe-Trading | 35k | — | ~4k | Automated safety check: Pass | MIT | |
| Walk Forward Validationagiprolabs/claude-trading-skills | 410 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Forecastingericrisco/rsc-harness | 180 | — | ~2.8k | Automated safety check: Pass | MIT | |
| Options Spread Conviction EngineLeoYeAI/openclaw-master-skills | 2.2k | — | ~5.7k | Automated safety check: Notes | MIT |
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Categories
Questions about Forecast Accuracy Review
What does Forecast Accuracy Review do?
Evaluate demand-forecast quality honestly - WMAPE, bias and Forecast Value Added against a naive benchmark over a rolling-origin backtest. Forecast Accuracy Review is an agent skill from davila7/claude-code-templates. Evaluate demand-forecast quality honestly - WMAPE, bias and Forecast Value Added against a naive benchmark over a rolling-origin backtest.
When should I use Forecast Accuracy Review?
Forecast Accuracy Review fits situations like: the user mentions forecast accuracy; demand planning performance; tahmin doğruluğu; asks whether a forecasting process.
How do I install Forecast Accuracy Review in Claude Code?
Run `npx skills add davila7/claude-code-templates --skill forecast-accuracy-review -a claude-code`. Or copy the skill folder (cli-tool/components/skills/operations/forecast-accuracy-review in davila7/claude-code-templates) into .claude/skills/forecast-accuracy-review in your project. Claude Code loads it when a task matches its description.
How do I install Forecast Accuracy Review in Codex?
Run `npx skills add davila7/claude-code-templates --skill forecast-accuracy-review -a codex`. Or copy the skill folder (cli-tool/components/skills/operations/forecast-accuracy-review in davila7/claude-code-templates) into .agents/skills/forecast-accuracy-review in your project. Codex loads it when a task matches its description.
Can I use Forecast Accuracy 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 davila7/claude-code-templates --skill forecast-accuracy-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/forecast-accuracy-review, .gemini/skills/forecast-accuracy-review, .github/skills/forecast-accuracy-review and .opencode/skills/forecast-accuracy-review in your project.
What does Forecast Accuracy Review need to run?
SKILL.md names no scripts, command-line tools or credentials: Forecast Accuracy Review is instructions for the agent only.
Does Forecast Accuracy Review access the network?
SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.
Is Forecast Accuracy 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 Forecast Accuracy Review use?
Forecast Accuracy 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 Forecast Accuracy Review use?
About 884 tokens (SKILL.md is roughly 3.5k 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 Forecast Accuracy Review?
Skills that share tags, products or a category with Forecast Accuracy Review: Longbridge Quant (helsome/folio, 271 stars), Quant Statistical Methods (HKUDS/Vibe-Trading, 35k stars), Walk Forward Validation (agiprolabs/claude-trading-skills, 410 stars) and Forecasting (ericrisco/rsc-harness, 180 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Forecast Accuracy Review?
davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,512 GitHub stars. The repository holds 479 skills in this directory. The repository was last updated on October 10, 2026.
Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.