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Ssot Context Loop

Community
mia-ppp
ssot-context-loop

When the user wants a governed, persistent product marketing source of truth, to set up an SSOT, scan field signals for drift, audit AI output, or manage approval gates for positioning changes. Use commands such as /setup, /scan, /confirm, /propose, /approve, /stale, /state, /brief, /manifest, and /audit-output. For lightweight bootstrap context, see product-marketing-context. This skill governs ongoing PMM knowledge and requires human approval for core changes.

Overview

Publishermia-ppp
RepositoryPMM-Skills
Skill namessot-context-loop
Stars
76
Forks
2
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by mia-ppp on GitHub. Read the source before you install it.

Installation

Install the Ssot Context Loop 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/mia-ppp/PMM-Skills/tree/main/skills/ssot-context-loop
  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/mia-ppp/PMM-Skills.git /tmp/PMM-Skills
mkdir -p .claude/skills
cp -r /tmp/PMM-Skills/skills/ssot-context-loop .claude/skills/ssot-context-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ssot Context Loop 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 Ssot Context Loop 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 Ssot Context Loop 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.

SSOT Context Loop

Maintain a governed PMM knowledge system for one client or project. It separates approved strategy from living field intelligence, detects drift, records decisions, and never changes canonical strategy without human approval.

When to Use

Use this skill to create or maintain ssot/<client-slug>/, scan new field or market inputs, review drift, audit generated assets, or manage canon decisions. Load references/ssot-spec.md before acting. It is the detailed operational specification for the file schemas, commands, signal model, gates, and templates.

Relationship to Other Skills

  • product-marketing-context establishes understanding and assembles proposals at .agents/product-marketing-context.md. It remains separate. If no SSOT exists, it may serve as context. If an SSOT exists for the active client or project, the SSOT is more authoritative for claims it covers.
  • Never automatically overwrite .agents/product-marketing-context.md.
  • positioning-strategy may develop strategy, but canon changes enter this SSOT only through its evidence and approval gates.
  • messaging-framework should source differentiators from core and persona messaging from SSOT persona research. Do not create either standalone.
  • icp-and-buyer-personas and customer-research can inform persona and living evidence files. competitor-profiling can inform competitive and market evidence.
  • sales-enablement, marketing-copy, launch-strategy, market-entry-orchestration, and launch-readiness-check can read mapped SSOT files to keep assets aligned.
  • events can adapt approved messages for event context and routes material narrative conflicts through this SSOT's evidence and human-approval gates.
  • output-quality-check checks a skill's artifact against that skill's declared requirements. It does not audit canon consistency or change this SSOT.
  • Use repo-level manifest.yaml in the user's project to map skills or agents to the SSOT files they read. Start with /manifest to create or update it.
  • The SSOT is upstream context. Other skills do not call this skill to decide canon, and this skill does not depend on their outputs as authority by default.

Operating Rules

  • Separate facts, which are recorded events, from opinions, which are strategy. Document opinions and ground them in facts.
  • Never invent a claim. Cite the quote or data, speaker and role, account type, system, date, and outcome or cost when known.
  • Every content line in every canonical SSOT file must end with exactly one evidence tag: [C], [A], or [G]. [C] is cited, [A] is assumed, and [G] is a gap. Apply decision-specific readiness: hold consequential unsupported decisions, continue independent work, and retain weak-evidence hypotheses with the evidence needed to resolve them. Approval remains separate.
  • Core holds its existing strategy domains; scoped approved decisions/references cover topics without a core home, per the spec. Initial specialist proposals may use supplied evidence before canon exists. Existing canon remains authoritative.
  • Give each claim one canonical home and reference dependencies. Update only explicitly approved affected sections, which may span files. Record uncertain placement in 09-iteration.md.
  • Field signals inform the canon, but do not automatically change it. Every finding gets one proposed verdict and waits at the required human gate.
  • Never write to core without Gate 2 approval. Never edit or delete comms/ input. Deprecate a section in place and name its replacement instead of deleting it.
  • Follow the reference's file schema, staleness thresholds, taxonomy, and command outputs. state.md is written only by the reconciler, never by hand.
  • Output without em dashes. Keep sentences to two lines at most. Lead bullets with outcomes and remove filler.

Command Routing

Route explicit commands to the matching workflow in references/ssot-spec.md:

  • /setup <client> creates the baseline SSOT from supplied source documents.
  • /scan <client> archives new inputs, classifies signals, and reports drift.
  • /confirm <IDs> and /reject <ID> <reason> perform Gate 1 decisions.
  • /propose prepares confirmed changes and impact analysis for Gate 2.
  • /approve <IDs> and /revise <ID> <notes> handle Gate 2 decisions.
  • /stale, /state, /brief, /manifest, and /audit-output produce their specified reports or project files.

For reviewed context proposals, read ../_shared/context-establishment.md and follow the spec's intake path. Preserve provenance and incoming IDs. Baselines wait at Gate 2; existing-context updates pass Gate 1 then Gate 2. Evidence readiness and human approval are separate; partial approval does not settle held decisions. For unstructured goal/material intake without a command, route to PMC to establish understanding. Resolve only missing scope. Never infer approval from silence.

Output Behavior

Write or propose files in the user's working project, never inside this skill or the PMM-Skills repository. Keep raw uploaded material in the project's immutable ssot/<client-slug>/comms/ directory. Respect the workflow's stop points.

After each workflow-authorized file creation or change, commit and push the generated SSOT output to the project's GitHub-backed repository. GitHub is the durable source of truth, not the chat. Preserve the required stop points: /scan stops at Gate 1, and proposed changes wait at Gate 2. Never modify core or commit a core change before explicit human approval at Gate 2.

For Gate 2 approval, return each ready-to-commit file in full, one code block per file with its path as the heading. Include the decision record for every core change, regenerated state.md, and a commit message. Commit and push the approved files after approval.

For /scan, return the drift report, three one-line priority findings, and "Needs your call" questions, then stop at Gate 1. For /brief, keep the team summary to ten lines or fewer and omit internal gate details.

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 Ssot Context Loop AI skill do?

When the user wants a governed, persistent product marketing source of truth, to set up an SSOT, scan field signals for drift, audit AI output, or manage approval gates for positioning changes. Use commands such as /setup, /scan, /confirm, /propose, /approve, /stale, /state, /brief, /manifest, and /audit-output. For lightweight bootstrap context, see product-marketing-context. This skill governs ongoing PMM knowledge and requires human approval for core changes.

Why use Ssot Context Loop on TypingMind?

Because you install it once and use it with any model. Ssot Context Loop 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 Ssot Context Loop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mia-ppp/PMM-Skills/tree/main/skills/ssot-context-loop. 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 Ssot Context Loop?

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 Ssot Context Loop?

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

Is the Ssot Context Loop AI skill free?

Yes. It is published on GitHub by mia-ppp 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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