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Iso 24495 4

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GaZmagik
iso-24495-4

Provisional task skill for organisational plain language implementation (based on ISO/CD 24495-4, committee draft). Activates for plain language gap analysis, policy drafting, review workflow design, and organisational readiness for the future published standard. Does not activate for ordinary writing, rewriting, or reviewing of individual documents.

Overview

PublisherGaZmagik
Repositoryiso-24495
Skill nameiso-24495-4
Stars
199
Forks
11
Bundled files
26
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 26 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by GaZmagik on GitHub. Read the source before you install it.

Installation

Install the Iso 24495 4 AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/GaZmagik/iso-24495/tree/main/skills/iso-24495-4
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/GaZmagik/iso-24495.git /tmp/iso-24495
mkdir -p .claude/skills
cp -r /tmp/iso-24495/skills/iso-24495-4 .claude/skills/iso-24495-4
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Iso 24495 4 in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Iso 24495 4 on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Iso 24495 4 is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ISO/CD 24495-4 - Plain Language (Organisational Implementation) [PROVISIONAL DRAFT]

Provisional status: ISO 24495-4 is a Committee Draft (ISO/CD 24495-4) and is not yet published; its text is not public. This skill is original guidance based on the draft's public scope. It does not reproduce ISO text and its output is never a compliance statement. Expect revision when the standard is published.

Turns the agent from a copy editor into an implementation consultant. It assesses an organisation's capacity to produce plain language across governance, capability, process, measurement, and culture, rather than the quality of any single text.

Scope & Execution Boundaries

  1. Activation Rules:

    • Activate for: plain language gap analysis, maturity assessment, policy or style guide drafting, review workflow design, training planning, readiness for the future published standard.
    • Never activate for: writing, rewriting, summarising, or reviewing an individual document. Those tasks belong to iso-24495-1, -2, -3, and -5.
  2. Confirmation Before Sweeps:

    • Both audit scripts read many files. State which directory will be swept and get the user's confirmation before running either.
  3. Evidence Primacy:

    • Never assert a maturity level without evidence: a found artefact, a confirmed answer, or a measured count. Absent evidence scores as absent.

The Audit Workflow

Run the four steps in order. Each feeds the next.

  1. Sweep for process artefacts (primary):
    • Run bun scripts/audit-evidence-cli.ts <workspace-dir> --json evidence.json.
    • This detects the system: policy, review workflow, automated checks, training, glossary. It reads presence only; you evaluate the quality of what it finds.
  2. Audit the document corpus (secondary):
    • Run bun scripts/audit-corpus-cli.ts <corpus-dir> --json findings.json.
    • Output feeds the Measurement dimension only, as evidence of what the system produces. Text quality alone never raises a maturity level: excellent text can come from one unsupported expert, and flawed text can coexist with a strong process that is catching it.
  3. Interview the human (3 to 5 questions maximum):
    • Use references/interview-guide.md. Ask only what the file system cannot show (leadership, culture, training delivery). Record answers in the answers.json structure and cite evidence for each true.
    • Score with bun scripts/score-maturity-cli.ts answers.json --json maturity.json. Scoring is deterministic so levels cannot drift between sessions.
  4. Generate the gap report:
    • Run bun scripts/generate-report-cli.ts findings.json evidence.json maturity.json --state <dir>/state.json --out gap-report.md.
    • State is append-only; successive audits produce a trend table that proves or disproves progress. Store state.json in the organisation's repository, not in the skill directory (installed skills may be read-only).

Quantitative Rules & Hard Constraints

  1. Proxies, Not Compliance: Corpus metrics (sentence length, paragraph length, legalese, heading depth) are mechanical proxies. Never present them, or this report, as ISO compliance or certification.
  2. Interview Cap: Ask the human at most 5 questions per audit. Infer everything else from artefacts.
  3. Ranked Actions: Every gap in the report gets a recommended action, ranked by reader impact against effort, tied to the criterion it unblocks.
  4. Language Boundary: The text heuristics are English-centric. For non-English corpora, skip step 2 and say so in the report.

Contrastive Examples

Example 1: Reporting a Gap

  • ❌ Not aligned (Vague, Unevidenced):

    "The organisation's culture around plain language seems weak and should be improved."

  • ✅ ISO/CD 24495-4 (Draft) Aligned:

    Culture: Level 1. Leadership is aware of the policy, confirmed in interview, but does not champion it. No leader has communicated it in the last year, and no feedback loop exists. Action (unblocks leadership-champions): the policy owner asks one executive sponsor to open the next all-hands with a plain language example.

Pre-Output Self-Audit Checklist

Before delivering a gap report, audit against these checks:

  • Correct activation: Was this an implementation task, not a writing task?
  • Sweep confirmed: Did the user approve each directory sweep?
  • Evidence cited: Does every maturity level and true answer cite an artefact, answer, or count?
  • Proxies labelled: Are corpus metrics presented as proxies feeding Measurement only?
  • State saved: Was state.json updated so the next audit can show a trend?
  • Provisional stated: Does the report declare the committee-draft basis and its limitations?

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Iso 24495 4 AI skill do?

Provisional task skill for organisational plain language implementation (based on ISO/CD 24495-4, committee draft). Activates for plain language gap analysis, policy drafting, review workflow design, and organisational readiness for the future published standard. Does not activate for ordinary writing, rewriting, or reviewing of individual documents.

Why use Iso 24495 4 on TypingMind?

Because you install it once and use it with any model. Iso 24495 4 is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Iso 24495 4 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GaZmagik/iso-24495/tree/main/skills/iso-24495-4. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Iso 24495 4?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Iso 24495 4?

As many as you like. As long as a model supports skills, you can use Iso 24495 4 with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Iso 24495 4 AI skill free?

Yes. It is published on GitHub by GaZmagik under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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