audience-icp-filter

audience-icp-filter is a skill for Claude Code, Codex from swan-gtm/gtm-skills. It costs 156 tokens per session (1,622 once invoked), scanned A, original, MIT.

A lead-screening workflow that sorts an existing list of people into likely ideal-customer matches, people needing review, and poor matches. An ideal customer profile (ICP) describes the customer traits a company wants to reach.

In plain words
What is it for?
It is for cleaning event, webinar, newsletter, community, CRM, or prospecting lists before contacting people.
Why use it?
It removes irrelevant contacts before outreach and explains why each person was included or excluded. It also checks whether the list contains enough data to support each filter.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for cleaning event, webinar, newsletter, community, CRM, or prospecting lists before contacting people.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/swan-gtm/gtm-skills/audience-icp-filter
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add swan-gtm/gtm-skills --skill audience-icp-filter
Clone the repo
git clone --depth 1 https://github.com/swan-gtm/gtm-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for audience-icp-filter

README.md
[![agentmods](https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/audience-icp-filter/github.svg)](https://agentmods.dev/skills/swan-gtm/gtm-skills/audience-icp-filter)
Your own site
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/audience-icp-filter"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/audience-icp-filter/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for audience-icp-filter

Your own site · 80×15
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/audience-icp-filter"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/audience-icp-filter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 156 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,622 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00156 $0.01622
Opus 5 $0.00078 $0.00811
Sonnet 5 $0.00031 $0.00324
Haiku 4.5 $0.00016 $0.00162

Measured 9d ago against content hash d0bb4fa65d98, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

audience-icp-filter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/build.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/erwann-lefevre/audience-icp-filter/SKILL.md · 81 lines

How it starts

The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Applies to a list that already exists and needs sorting before anyone contacts it. Produces four labelled buckets, a reason per lead, and reconciled counts.

Every imported list is mostly noise

Colleagues are in it. Competitors are watching. A third of the job titles are unreadable, half the companies are stale, and somewhere in there are the twelve people actually worth a message. The default response — skim it, sort by gut, start sequencing — leaks in the direction that hurts most: someone's own coworker gets a cold pitch.

This skill starts from an existing list. Importing or scraping is a different job with different prerequisites; folding it in makes both slower.

Check the data before asking about the ICP

The instinct is to ask what the ICP is first. Do the opposite: measure what the list actually contains, then ask only about criteria the data can support.

There is no point offering geography filtering on a list where the location field is empty. It doesn't filter anything — it routes everyone into review and calls that a result. The same applies to industry, and to exclusion when there's no company and no email to match on.

Run the coverage check, read which criteria are blocked, and say so before the conversation about the ICP starts. If enrichment is needed, quote what it will cost and get an explicit yes before spending anything. If the team declines, proceed — but name the criteria you dropped and say that exclusion is now best-effort. Filtering on a criterion the data can't support and presenting the result as clean is the worst available outcome, because it looks like work. The field-by-field thresholds and the ICP question set are in references/coverage-and-icp.md.

Two passes, and neither is optional

Pass 1 is deterministic. scripts/build.py pattern-matches seniority and function across the title and, when the title is silent, the bio; applies exclusions uniformly across every identity field; and refuses to emit a result whose bucket counts don't reconcile against the input. Nobody gets lost, and the same list classifies the same way twice. Never hand-sort a list — it's unauditable and it's exactly how colleagues leak through.

Read the full file on GitHub · 81 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 81 lines · 156 tokens per session scan A d0bb4fa65d98

Subscribe to this mod's changes

audience-icp-filter is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 156 tokens to every session and 1,622 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Related

Other skills, from other repositories

afrexai-lead-hunter

Enterprise-grade B2B lead generation, enrichment, scoring, and outreach sequencing for AI agents. Find ideal prospects, enrich with verified data, score against your ICP, and generate personalized outreach — all autonomously.

LeoYeAI/openclaw-master-skills · 50 tokens

reddit-leads

Discover B2B leads from Reddit using AI-powered lead scoring via reddapi.dev Leads API. Finds high-intent signals, scores them 0-100, and classifies by lead type (painpoint, solutionrequest, complaint, featurerequest, comparison). Perfect for competitor poaching, pain point discovery, and sales prospecting.

lignertys/reddit-research-skills · 71 tokens

lead-gen

Use when building and qualifying a prospect list before anyone reaches out — a falsifiable ICP, named accounts/contacts from Apollo/ZoomInfo/Clay, deduped against the CRM, tiered by fit+intent+engagement. NOT writing or sending the outreach (that is cold-outreach), NOT tracking the deal after first contact (that is…

ericrisco/rsc-harness · 77 tokens

Lead Research Assistant

Research company and contact information for sales outreach.

claude-office-skills/skills · 12 tokens

intent-outreach

Run a local research, enrichment, and outreach-drafting workflow over company domains with bring-your-own provider keys. Use when a user wants a reviewed SDR campaign or grounded cold-email drafts. Trigger with "run an outreach campaign", "prospect these companies", or "/intent-outreach".

jeremylongshore/intent-outreach · 61 tokens

sdr-agent

Autonomous SDR that monitors pipeline, drafts outreach, qualifies inbound, and manages follow-ups.

Autter-dev/agentic-sales-skills · 21 tokens