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Icp Evidence Analysis

CommunityPopular
elvisun
icp-evidence-analysis

Turn a source-bound company and market dossier into testable ideal-customer-profile hypotheses, buying roles, triggers, constraints, disqualifiers, standing, counterevidence, and research gaps. Use when a URL, company description, monitor profile, or evidence manifest needs to become defensible ICP inputs for buyer research or an AI-visibility prompt panel.

Overview

Publisherelvisun
Repositorynewsjack
Skill nameicp-evidence-analysis
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 Icp Evidence Analysis 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/icp-evidence-analysis
  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/icp-evidence-analysis .claude/skills/icp-evidence-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Icp Evidence Analysis 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 Icp Evidence Analysis 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 Icp Evidence Analysis 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.

ICP Evidence Analysis

Build hypotheses from evidence. Do not write persona fiction.

This skill inherits the ethical floor from skills/ETHICS.md. It enforces anti-hallucination and permission checks. Anti-spray and human-send are not applicable because this skill produces research artifacts, not outreach.

Inputs

Accept any combination of:

  • a company URL and user-supplied description;
  • source_manifest.json;
  • public product, pricing, integration, security, support, certification, filing, review, or coverage pages;
  • an existing Newsjack monitor profile as unverified leads;
  • target markets, exclusions, permitted-data rules, or fact-check results.

If only a URL and description are supplied, research enough independent public evidence to test the website's claims. Never treat a target's own positioning as buyer behavior.

Build the evidence perimeter

For every material claim, retain:

  • source ID, canonical URL or authorized file;
  • title/publisher, access or publication date, and source type;
  • exact span or a faithful short paraphrase;
  • whether it is company_asserted, buyer_behavior, or independent;
  • fact type, confidence, and permission status.

Keep negative and conflicting evidence. Stop if a source is private, unlawfully obtained, outside the user's permission, or contains personal data that is not needed.

Separate:

  • products, capabilities, limitations, integrations, proof assets, and geographies;
  • declared segments from independently supported demand;
  • exact brand, product, domain, people, slogan, proprietary-category, campaign, and flattering-claim terms for the later contamination register;
  • competitors named by evidence from alternatives merely guessed by the model.

Classify by publisher provenance, not by the claim's tone. A target-authored postmortem, benchmark, customer story, or technical article remains company_asserted; split it from any third-party analysis instead of blending both into one independent record.

Use fact-check when a material external claim lacks primary or independent support.

Form ICP hypotheses

Create testable contexts, not demographic biographies. Each hypothesis must include:

  • organization, team, household, or user context;
  • triggering condition or struggling moment;
  • likely user, champion, economic buyer, approver, blocker, and post-purchase user when evidence supports them;
  • constraints, disqualifiers, and geography;
  • company capability that establishes standing;
  • supporting evidence, counterevidence, confidence, and open questions.

A core-panel ICP needs at least one independent or behavioral source. A website-only ICP stays low confidence and hypothesis_only unless a human explicitly promotes it.

Do not:

  • invent age, income, title, company size, maturity, or buying committee;
  • assume current customers define the target population;
  • infer market size or prevalence from source counts;
  • invert product features into buyer jobs;
  • erase evidence that contradicts positioning;
  • decide prompt wording, weights, or funnel stages.

Confidence

  • high: multiple relevant sources, including direct behavior or strong independent evidence, agree.
  • medium: one strong source or several consistent weaker sources; material gaps remain.
  • low: company assertion, sparse proxy evidence, or conflict dominates.

Use null for unsupported fields. Never fill a schema slot with a plausible guess.

Output

Give the human a concise Markdown summary first:

  1. factual perimeter and permission status;
  2. supported ICPs and buying roles;
  3. counterevidence and exclusions;
  4. open research questions;
  5. Gate 1 decision: ready_for_human_review, needs_research, or stop_permission_failure.

Then write icp_hypotheses.json with the shared envelope:

json
{
  "schema_version": "1.0.0",
  "artifact_id": "icp-<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"],
  "gate_status": "ready_for_human_review",
  "icps": [
    {
      "icp_id": "stable-id",
      "label": "Evidence-bound context label",
      "context": {},
      "triggers": [{"text": "Observed trigger", "source_ids": ["source-001"]}],
      "roles": [{"role": "champion", "label": "controller", "source_ids": ["source-001"]}],
      "constraints": [],
      "disqualifiers": [],
      "standing_claim_ids": ["claim-001"],
      "supporting_source_ids": ["source-001"],
      "counterevidence_source_ids": [],
      "confidence": "high | medium | low",
      "status": "supported | hypothesis_only | excluded",
      "open_questions": []
    }
  ]
}

Every material field must trace to source IDs or be null. Preserve source spans in source_manifest.json; do not duplicate long excerpts here.

Handoff

After human Gate 1, pass approved ICP IDs, rejected IDs, permitted source IDs, target markets, and unresolved questions to buyer-job-intent-analysis. Do not silently promote hypotheses.

Frequently asked questions

What does the Icp Evidence Analysis AI skill do?

Turn a source-bound company and market dossier into testable ideal-customer-profile hypotheses, buying roles, triggers, constraints, disqualifiers, standing, counterevidence, and research gaps. Use when a URL, company description, monitor profile, or evidence manifest needs to become defensible ICP inputs for buyer research or an AI-visibility prompt panel.

Why use Icp Evidence Analysis on TypingMind?

Because you install it once and use it with any model. Icp Evidence Analysis 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 Icp Evidence Analysis in TypingMind?

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

Which AI models can use Icp Evidence Analysis?

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 Icp Evidence Analysis?

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

Is the Icp Evidence Analysis 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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