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Prompt Proximity Architecture

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elvisun
prompt-proximity-architecture

Turn an approved measurement charter, ICPs, and buyer jobs into a budget-aware prompt coverage blueprint across proximity bands, aided status, information acts, journey states, roles, locales, evidence grades, partitions, and measurement lanes. Use before prompt wording to define required, optional, and prohibited canonical intent cells.

Overview

Publisherelvisun
Repositorynewsjack
Skill nameprompt-proximity-architecture
Stars
1.5K
Forks
151
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Prompt Proximity Architecture 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/elvisun/newsjack/tree/main/skills/prompt-proximity-architecture
  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/elvisun/newsjack.git /tmp/newsjack
mkdir -p .claude/skills
cp -r /tmp/newsjack/skills/prompt-proximity-architecture .claude/skills/prompt-proximity-architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prompt Proximity Architecture 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 Prompt Proximity Architecture 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 Prompt Proximity Architecture 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.

Prompt Proximity Architecture

Design the cells before writing the strings.

This skill inherits the ethical floor from skills/ETHICS.md. It enforces anti-hallucination and evidence-bound coverage. Anti-spray and human-send are not applicable.

Inputs

Require:

  • a measurement charter;
  • approved icp_hypotheses.json and buyer_jobs.json;
  • target run/review budget;
  • required locales, surfaces, and lanes;
  • any campaign partition and prior-panel constraints.

If the charter is provisional, design a provisional architecture and name the gaps. Reject a charter that says only “track AI visibility.”

Keep dimensions independent

Each intent cell fixes:

  • buyer job;
  • information act;
  • journey state;
  • material constraints;
  • persona or buying role;
  • locale and language;
  • prompt-proximity band;
  • expected answer kind.

Add independent tags for evidence grade, partition, lane eligibility, turn form, and optional funnel. Do not make funnel the schema.

Use the supported acts explain, diagnose, plan, generate, compare, recommend, verify, navigate, buy, implement, and troubleshoot. Include only acts entailed by the job evidence.

Use journey states problem_identification, exploration, requirements_building, supplier_selection, adoption, and post_purchase.

Assign proximity

BandNameStructureDefault aided status
B0_direct_brand_productBrandNames target brand/product; asks about facts, fit, use, reputation, support, or implementationtarget_aided
B1_comparison_purchaseShortlistShortlist, recommendation, alternatives, pricing, requirements, or comparisonunaided; competitor_aided when only competitors are supplied; target_aided for a declared target-vs-competitor comparison
B2_categoryCategoryNames an accepted solution category, not the targetcategory_aided
B3_problem_needProblemDescribes pain, risk, trigger, or constraint without category/targetunaided
B4_job_goalGoalAsks for progress/outcome without supplying a solution categoryunaided
B5_broad_discovery_storyMarketTrend, event, regulation, practice, or narrative connected to the jobunaided

Use the code in prompt_architecture.json and the name in every Markdown summary. The band codes are labels for structurally different prompts, not points on a scale: do not write "B4" in prose, do not order or average them, and do not describe one band as further along than another. See "Display names" in ../build-ai-visibility-panel/references/artifact-contracts.md.

Bands and aided status are correlated by construction — Brand is always target_aided, Category always category_aided, and Problem, Goal and Market are always unaided, so only Shortlist splits. Report the two together rather than as if they were independent findings, and do not present a band breakdown that merely restates the prompted split.

campaign_exposed is an independent flag. Never combine target-aided, competitor-aided, category-aided, unaided, or campaign-exposed cells in one denominator.

Because aided_status is single-valued, a B1 prompt naming both the target and a competitor is target_aided; record the competitor stimulus in the contamination exception and keep the cell in the aided partition. A B1 prompt naming competitors but not the target is competitor_aided.

Do not assign funnel mechanically:

  • B0 can be post-purchase, not BOFU.
  • B3 can describe an urgent funded decision.
  • B1 can be early exploration.
  • funnel may be null.

B5 requires fresh dated public evidence and a decay/refresh rule.

The charter's product/capability perimeter is also a coverage dimension. For every named area, create at least one evidence-supported job/cell or a coverage_gap with an exclusion/waiver and the evidence needed. Never silently omit a named area.

Allocate coverage

Cover every dimension that the evidence supports, not every Cartesian combination. Never generate a full persona × locale × act × constraint grid.

For a diagnostic default, target 30–48 unique unaided cells, two variants per cell, and three repeats per wave. For a standard panel, target 60–120 cells. Adjust to the user's budget and keep aided cells outside the unaided quota.

Stratify minimums and targets across:

  • B0–B5 where evidence supports each band;
  • job, journey, and information act;
  • ICP/role and materially distinct locale;
  • evidence grade/source type;
  • core, rotating, sentinel, control, and aided partitions;
  • closed-model, retrieval, consumer-surface, and campaign lanes.

Protect underrepresented but decision-relevant strata. Grade-D cells are rotating discovery until independently validated or explicitly promoted.

When the budget cannot meet required coverage, return an allocation conflict. Do not silently drop a band, role, locale, or control.

Prohibit unsupported cells

Mark a cell prohibited when it:

  • changes an informational need into a recommendation solely to elicit brands;
  • introduces a category not supported by the job evidence;
  • treats a competitor-named prompt as unaided;
  • uses target/campaign copy outside the allowed aided lane;
  • assumes translation is locale equivalence;
  • derives a core cell from current AI answers or target pages;
  • exceeds the declared sampling budget without a waiver.

Output

Give a Markdown coverage summary first: required dimensions, planned counts, gaps, conflicts, and why each missing band is unsupported.

Then write prompt_architecture.json:

json
{
  "schema_version": "1.0.0",
  "artifact_id": "architecture-<stable-slug>",
  "created_at": "RFC3339",
  "created_by": "declared agent or human",
  "source_manifest_hash": null,
  "warnings": ["hash_not_computed: compute source_manifest_hash before freeze"],
  "cells": [
    {
      "cell_spec_id": "spec-001",
      "job_id": "job-001",
      "icp_ids": ["icp-001"],
      "proximity_band": "B3_problem_need",
      "aided_status": "unaided",
      "campaign_exposed": false,
      "information_act": "diagnose",
      "journey_state": "problem_identification",
      "funnel": null,
      "persona_id": "role-001",
      "locale": "en-CA",
      "language": "en",
      "material_constraints": [],
      "expected_answer_kind": "diagnosis_and_options",
      "turn_form": "single_turn",
      "lane_eligibility": ["closed_model", "retrieval"],
      "partition": "core",
      "evidence_grade": "A",
      "target_variants": 2,
      "required": true,
      "reason_source_ids": ["source-001"]
    }
  ],
  "prohibited_cells": [],
  "allocation": {
    "core_cells": 36,
    "rotating_cells": 8,
    "sentinel_cells": 12,
    "control_cells": 0,
    "aided_cells": 6,
    "budget_status": "within_budget | conflict | waived"
  }
}

Before handoff, require every cells[].job_id and every cells[].icp_ids[] to resolve exactly. There is one authoritative field for each relationship. Never leave a stale ID and add a compensating alias such as job_id_authoritative; rewrite the cell or stop with an allocation/reference error.

Handoff

Pass the architecture plus a target-free blind brief to realistic-prompt-generation. Keep the contamination register outside the generator's context.

Frequently asked questions

What does the Prompt Proximity Architecture AI skill do?

Turn an approved measurement charter, ICPs, and buyer jobs into a budget-aware prompt coverage blueprint across proximity bands, aided status, information acts, journey states, roles, locales, evidence grades, partitions, and measurement lanes. Use before prompt wording to define required, optional, and prohibited canonical intent cells.

Why use Prompt Proximity Architecture on TypingMind?

Because you install it once and use it with any model. Prompt Proximity Architecture 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 Prompt Proximity Architecture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/elvisun/newsjack/tree/main/skills/prompt-proximity-architecture. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Prompt Proximity Architecture?

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 Prompt Proximity Architecture?

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

Is the Prompt Proximity Architecture AI skill free?

Yes. It is published on GitHub by elvisun 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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