Agent skill

Icp Evidence Analysis

by elvisun in elvisun/newsjack

Turn a source-bound company and market dossier into testable ideal-customer-profile hypotheses, buying roles, triggers, constraints, disqualifiers, standing, counterevidence, and research gaps.

MITAuto-check passedMarketing & SEO

Install Icp Evidence Analysis

skills CLI
$ npx skills add elvisun/newsjack --skill icp-evidence-analysis -a claude-code

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

GitHub CLI
$ gh skill install elvisun/newsjack icp-evidence-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/elvisun/newsjack.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/icp-evidence-analysis .claude/skills/icp-evidence-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
icp-evidence-analysis
GitHub stars
1.5k
Token cost
~1.4k tokens
SKILL.md length
528 words
Files
1
Skills in repo
30
Repo updated
First seen
Licence
MIT

At a glance

Turn a source-bound company and market dossier into testable ideal-customer-profile hypotheses, buying roles, triggers, constraints, disqualifiers, standing, counterevidence, and research gaps.

  • Works in 5 steps: factual perimeter and permission status; → supported ICPs and buying roles; → counterevidence and exclusions; → …
  • Company description
  • SKILL.md covers Inputs, Build the evidence perimeter, Form ICP hypotheses and Confidence, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Icp Evidence Analysis is an agent skill from elvisun/newsjack. 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.

Its SKILL.md is about 1.4k 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 Marketing & SEO, covering Positioning and messaging and AI search optimization. The repository describes itself as: The open-source skills that turn your agent into a full PR team. The licence is MIT.

When your agent uses it

  • Company description
  • Monitor profile
  • Evidence manifest needs to become defensible ICP inputs for buyer research
  • An AI-visibility prompt panel

Example prompts

  • “/icp-evidence-analysis”

Workflow steps

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

  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.

What it can do on your machine

Read from SKILL.md and the folder at commit b5a8dc8. 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 (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    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

Icp Evidence Analysis loads about 1.4k tokens when it runs. Until then it costs about 95 tokens; SKILL.md has 528 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

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 elvisun/newsjack at commit b5a8dc8, republished under its MIT licence (© elvisun). 528 words, ~1,380 tokens.

Download SKILL.mdSave it as .claude/skills/icp-evidence-analysis/SKILL.md (or your agent's skills folder).
name
icp-evidence-analysis
description
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.
metadata.category
AI visibility

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.

Show full SKILL.md (264 more words)Show less

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.

© elvisun, 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 skills/icp-evidence-analysis of elvisun/newsjack.

Open the folder on GitHubat commit b5a8dc8

Compare with similar skills

Icp Evidence 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.

Icp Evidence Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Icp Evidence Analysis this skillelvisun/newsjack1.5k—~1.4kAutomated safety check: PassMIT
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Research Brandonvoyage-ai/gtm-engineer-skills1.3k—~1.3kAutomated safety check: PassMIT
CompeteHouseofmvps/ultraship123—~1.1kAutomated safety check: NotesMIT
Position MeVarnan-Tech/opendirectory674—~1kAutomated safety check: PassMIT
SEO Geoeunomia-bpf/eunomia.dev236—~2.1kAutomated safety check: PassMIT

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Categories

Questions about Icp Evidence Analysis

What does Icp Evidence Analysis 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. Icp Evidence Analysis is an agent skill from elvisun/newsjack. Turn a source-bound company and market dossier into testable ideal-customer-profile hypotheses, buying roles, triggers, constraints, disqualifiers, standing, counterevidence, and research gaps.

When should I use Icp Evidence Analysis?

Icp Evidence Analysis fits situations like: company description; monitor profile; evidence manifest needs to become defensible ICP inputs for buyer research; an AI-visibility prompt panel.

How do I install Icp Evidence Analysis in Claude Code?

Run `npx skills add elvisun/newsjack --skill icp-evidence-analysis -a claude-code`. Or copy the skill folder (skills/icp-evidence-analysis in elvisun/newsjack) into .claude/skills/icp-evidence-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Icp Evidence Analysis in Codex?

Run `npx skills add elvisun/newsjack --skill icp-evidence-analysis -a codex`. Or copy the skill folder (skills/icp-evidence-analysis in elvisun/newsjack) into .agents/skills/icp-evidence-analysis in your project. Codex loads it when a task matches its description.

Can I use Icp Evidence 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 elvisun/newsjack --skill icp-evidence-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/icp-evidence-analysis, .gemini/skills/icp-evidence-analysis, .github/skills/icp-evidence-analysis and .opencode/skills/icp-evidence-analysis in your project.

What does Icp Evidence Analysis need to run?

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

Does Icp Evidence Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Icp Evidence 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 Icp Evidence Analysis use?

Icp Evidence 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 Icp Evidence Analysis use?

About 1.4k tokens (SKILL.md is roughly 5.5k 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 Icp Evidence Analysis?

Skills that share tags, products or a category with Icp Evidence Analysis: Marketing Os (Yuzzyuk/marketing-os, 540 stars), Research Brand (onvoyage-ai/gtm-engineer-skills, 1.3k stars), Compete (Houseofmvps/ultraship, 123 stars) and Position Me (Varnan-Tech/opendirectory, 674 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Icp Evidence Analysis?

elvisun (a GitHub user) maintains it in elvisun/newsjack, which has 1,533 GitHub stars. The repository holds 30 skills in this directory. The repository was last updated on October 7, 2026.

Source: elvisun/newsjack on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.