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Agent skill

Otif Analysis

by davila7 in davila7/claude-code-templates

Audit delivery performance from order-level data - compute the OTIF metric ladder (tolerant to strict), find where lateness concentrates, and quantify the gap between the reported KPI and what…

MITAuto-check passedLegal & Compliance

Install Otif Analysis

skills CLI
$ npx skills add davila7/claude-code-templates --skill otif-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install davila7/claude-code-templates otif-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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/otif-analysis .claude/skills/otif-analysis && rm -rf skills-src

Use ~/.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/

Facts

Skill name
otif-analysis
GitHub stars
32k
Token cost
~974 tokens
SKILL.md length
457 words
Files
1
Skills in repo
477
Repo updated
First seen
Licence
MIT

At a glance

Audit delivery performance from order-level data - compute the OTIF metric ladder (tolerant to strict), find where lateness concentrates, and quantify the gap between the reported KPI and what…

  • Works in 5 steps: Validate before computing. Count… → Compute the metric ladder on the same… → Decompose the gap. For the dimension… → …
  • The user mentions OTIF
  • 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

Otif Analysis is an agent skill from davila7/claude-code-templates. Audit delivery performance from order-level data - compute the OTIF metric ladder (tolerant to strict), find where lateness concentrates, and quantify the gap between the reported KPI and what customers experience. Use when the user mentions OTIF, on-time delivery, delivery performance, late orders, teslimat performansı, zamanında teslimat, or asks why customers complain despite a high on-time score. Differentiator - exposes measurement choices before optimizing operations.

Its SKILL.md is about 970 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 Legal & Compliance, covering OKRs and executive reporting and Contract review. 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 OTIF
  • On-time delivery
  • Delivery performance
  • Teslimat performansı

Example prompts

  • “/otif-analysis”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Validate before computing. Count duplicated order rows; flag impossible dates (actual before order date); count cancelled orders and state…
  2. Compute the metric ladder on the same population, strictest last
  3. Decompose the gap. For the dimension with the largest spread (try carrier, region, month, customer, product family), show OTIF per…
  4. Analyze the tail, not the mean. Average lateness hides the distribution. Report the share of orders 4+ days late and the worst decile…
  5. Reconcile before reporting. Recompute the headline OTIF once more directly from raw rows (single pass, no intermediate tables) and confirm…

What it can do on your machine

Read from SKILL.md and the folder at commit 14680ec. 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

Otif Analysis loads about 974 tokens when it runs. Until then it costs about 123 tokens; SKILL.md has 457 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~123
When it runs · the whole SKILL.md, loaded when a task matches
~974

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 14680ec, republished under its MIT licence (© davila7). 457 words, ~974 tokens.

Download SKILL.mdSave it as .claude/skills/otif-analysis/SKILL.md (or your agent's skills folder).
name
otif-analysis
description
Audit delivery performance from order-level data - compute the OTIF metric ladder (tolerant to strict), find where lateness concentrates, and quantify the gap between the reported KPI and what customers experience. Use when the user mentions OTIF, on-time delivery, delivery performance, late orders, teslimat performansı, zamanında teslimat, or asks why customers complain despite a high on-time score. Differentiator - exposes measurement choices before optimizing operations.

OTIF Analysis

Most "delivery problems" are measurement problems first. Before recommending any operational fix, establish what the honest number is and which definition choices inflate the reported one.

Required data

Order-level rows with: order_id, requested_delivery_date (what the customer asked for), promised_delivery_date (what was confirmed), actual_delivery_date, completeness info (lines ordered vs delivered, or qty ordered vs delivered), and a status/cancelled flag. Useful cuts: carrier, region, customer, product family.

If requested_delivery_date is missing, say so explicitly: only the promised-date rungs are computable, and the analysis cannot see sales-padding. Recommend capturing the requested date going forward.

Workflow

  1. Validate before computing. Count duplicated order rows; flag impossible dates (actual before order date); count cancelled orders and state how they will be treated. Report these counts in the output - an audit that silently cleans data is not an audit.
  2. Compute the metric ladder on the same population, strictest last:
    • L1: on-time vs promised date, with the tolerance window currently in use (ask what it is; if unknown, show +3 days and label it)
    • L2: on-time vs promised, zero tolerance
    • L3: on-time vs requested, zero tolerance
    • L4: OTIF = on-time vs requested AND order complete (all lines / full qty). In-full is judged at order level - a 9-of-10-lines delivery is not 90% on-time, it is one incomplete order
    • L5: OTIF with cancelled orders kept in the denominator Present the ladder as a table with the delta and the cause of each drop (tolerance, padding, partials, cancellations).
  3. Decompose the gap. For the dimension with the largest spread (try carrier, region, month, customer, product family), show OTIF per segment. Name the concentrated driver, not just the average.
  4. Analyze the tail, not the mean. Average lateness hides the distribution. Report the share of orders 4+ days late and the worst decile - those are the orders customers remember.
  5. Reconcile before reporting. Recompute the headline OTIF once more directly from raw rows (single pass, no intermediate tables) and confirm it matches. If it does not, stop and find out why.
Show full SKILL.md (125 more words)Show less

Pitfalls to check explicitly

  • Anchor choice: promised-date metrics hide sales padding. Compute average (promised - requested) days; if > 0.5, quantify its KPI effect.
  • Tolerance windows are policy, not truth. Show the tolerance-sensitivity curve if the tolerance is contested.
  • Cancelled orders quietly leaving the denominator flatter the metric.
  • Line-level averaging overstates performance versus order-level in-full.

Output format

  1. The ladder table (definition, result %, delta, cause)
  2. Three finding sentences, each: what moved / where it concentrates / what decision it needs
  3. A definitions footnote stating anchor date, tolerance, in-full rule and cancellation treatment - so the number cannot be misread

Worked example with charts and synthetic data: https://github.com/gulmezeren2-byte/otif-analytics

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/otif-analysis of davila7/claude-code-templates.

Open the folder on GitHubat commit 14680ec

Compare with similar skills

Otif Analysis 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.

Otif Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Otif Analysis this skilldavila7/claude-code-templates32k—~974Automated safety check: PassMIT
Partnerships EcosystemLeoYeAI/openclaw-master-skills2.2k—~5kAutomated safety check: PassMIT
Msa Review Commercial PurchaseLegalQuants/lq-ai150—~5.7kAutomated safety check: PassApache-2.0
Stakeholder Summaryanthropics/claude-for-legal9.6k2 repos~3.3kAutomated safety check: PassApache-2.0
Quality Manager Qmralirezarezvani/claude-skills28k—~4.7kAutomated safety check: PassMIT
Gc Reviewalirezarezvani/claude-skills28k—~1.2kAutomated safety check: PassMIT

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Categories

Questions about Otif Analysis

What does Otif Analysis do?

Audit delivery performance from order-level data - compute the OTIF metric ladder (tolerant to strict), find where lateness concentrates, and quantify the gap between the reported KPI and what…. Otif Analysis is an agent skill from davila7/claude-code-templates. Audit delivery performance from order-level data - compute the OTIF metric ladder (tolerant to strict), find where lateness concentrates, and quantify the gap between the reported KPI and what customers experience.

When should I use Otif Analysis?

Otif Analysis fits situations like: the user mentions OTIF; on-time delivery; delivery performance; teslimat performansı.

How do I install Otif Analysis in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill otif-analysis -a claude-code`. Or copy the skill folder (cli-tool/components/skills/operations/otif-analysis in davila7/claude-code-templates) into .claude/skills/otif-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Otif Analysis in Codex?

Run `npx skills add davila7/claude-code-templates --skill otif-analysis -a codex`. Or copy the skill folder (cli-tool/components/skills/operations/otif-analysis in davila7/claude-code-templates) into .agents/skills/otif-analysis in your project. Codex loads it when a task matches its description.

Can I use Otif Analysis 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 otif-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/otif-analysis, .gemini/skills/otif-analysis, .github/skills/otif-analysis and .opencode/skills/otif-analysis in your project.

What does Otif Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: Otif Analysis is instructions for the agent only.

Does Otif Analysis 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 Otif Analysis 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 Otif Analysis use?

Otif Analysis 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 Otif Analysis use?

About 974 tokens (SKILL.md is roughly 3.9k 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 Otif Analysis?

Skills that share tags, products or a category with Otif Analysis: Partnerships Ecosystem (LeoYeAI/openclaw-master-skills, 2.2k stars), Msa Review Commercial Purchase (LegalQuants/lq-ai, 150 stars), Stakeholder Summary (anthropics/claude-for-legal, 9.6k stars) and Quality Manager Qmr (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Otif Analysis?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,463 GitHub stars. The repository holds 477 skills in this directory. The repository was last updated on October 8, 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.