account-intelligence

account-intelligence is a skill for Claude Code from alebgl77/claude-inc. It costs 88 tokens per session (1,475 once invoked), scanned A, original, MIT.

A research guide for finding and qualifying business accounts that match a target customer profile. It records source-linked evidence and separates observable facts from assumptions about buying interest.

In plain words
What is it for?
Use it to build an account list, validate growth signals, support account-based marketing, and assess whether companies may be ready for sales or product-led outreach.
Why use it?
It reduces duplicate or weak account research and avoids treating a score or public signal as proof that a company intends to buy. It also keeps account identities and exclusions consistent.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the claude-inc plugin — 64 skills, 3 commands, 11 agents shipped together

Good fit Use it to build an account list, validate growth signals, support account-based marketing, and assess whether companies may be ready for sales or product-led outreach.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/alebgl77/claude-inc/account-intelligence
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 alebgl77/claude-inc --skill account-intelligence
Clone the repo
git clone --depth 1 https://github.com/alebgl77/claude-inc

Made for: Claude Code.

Or install claude-inc, the plugin that ships this one along with the rest of its 64 skills, 3 commands, 11 agents.

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 account-intelligence

README.md
[![agentmods](https://agentmods.dev/badge/skills/alebgl77/claude-inc/account-intelligence/github.svg)](https://agentmods.dev/skills/alebgl77/claude-inc/account-intelligence)
Your own site
<a href="https://agentmods.dev/skills/alebgl77/claude-inc/account-intelligence"><img src="https://agentmods.dev/badge/skills/alebgl77/claude-inc/account-intelligence/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 account-intelligence

Your own site · 80×15
<a href="https://agentmods.dev/skills/alebgl77/claude-inc/account-intelligence"><img src="https://agentmods.dev/badge/skills/alebgl77/claude-inc/account-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 88 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,475 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.
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 $0.00088 $0.01475
Opus 5.5 $0.00035 $0.00590
Sonnet 5.5 $0.00018 $0.00295
Haiku 4.5 $0.00009 $0.00147

Measured 1mo ago against content hash 5a10e191c65a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

account-intelligence 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 1mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/extract_signals.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/account-intelligence/SKILL.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Account Intelligence — Signal Analyst

When to use

Build an account cohort, validate a growth trigger, support ABM or investigate product-led sales readiness. For a single active deal's call preparation and personal outreach, hand the accepted account to Sales and its account-research manual.

Inputs

  • ICP version, must-haves, exclusions, target business problem and qualification decision.
  • Approved account sources or user-provided exports; existing account IDs and exclusions from Sales.
  • Research horizon, maximum cohort size, data minimization requirements and freshness windows.
  • Public page snapshots or authorized first-party account events; access scope and source metadata.

Workflow

  1. Define the unit. Choose the legal/operating account and stable ID. Record parent/subsidiary relationships and deduplicate aliases/domains before scoring. Exclude existing opportunities, competitors or customers when the experiment requires it.
  2. Specify signals before research. Name observable facts relevant to the problem: a dated workflow launch, an integration requirement, a published operations role or authorized repeated product use. Define where to look and what would disconfirm relevance. Funding and hiring alone do not establish budget, pain or buying intent.
  3. Collect minimally. Prefer first-party company pages and authorized exports. Save source URL, publisher, precise supporting excerpt or selected value, snapshot hash where available, and researcher. Do not collect contact lists, sensitive attributes or hidden identifiers. Follow source access limits; do not bypass access controls.
  4. Separate clocks. event_at is the event's asserted date; published_at is the page's claimed publication date; observed_at is when a researcher actually saw or captured it; extracted_at is when a parser processed a saved file. Unknown stays null. A current extraction does not refresh an old observation. Record the timezone and whether each date is source-asserted or independently recorded.
  5. Validate and decay. Label direct evidence, corroborated inference or unverified claim. Set valid_until and a concrete recheck per signal type. Starting policy: job/change signals recheck within 30 days; firmographic fit within 90 days; product usage within the agreed recent account window. These are operating defaults, not statistical truths. Expired or undated signals can inform research but cannot satisfy a freshness gate.
  6. Qualify explainably. Publish a rubric before ranking. Score evidence-backed fit separately from readiness; require must-haves and no exclusions. Missing evidence is unknown, never a silent zero or an inferred positive. Show rule-by-rule evidence and reason codes. A score is a prioritization rule, not a conversion probability.
  7. Test relevance. Inspect a sample of both high and low scores, including stale and contradictory records. Review likely false positives with Sales. Compare later acceptance by score band and revise the rubric only on a new version/cohort.
  8. Hand off and reconcile. Give Marketing an audience/problem brief with proof and Sales an account packet with open questions, freshness and proposed next step. Sales decides outreach and commercial qualification. Record accepted/rejected/deferred reasons and send outcomes to revenue-operations; do not create or update CRM deals without authorized Sales ownership.

Read the full file on GitHub · 73 lines

Files

What ships with it

3 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. 1mo ago First seen · 73 lines · 88 tokens per session scan A 5a10e191c65a

Subscribe to this mod's changes

account-intelligence is a skill published in the GitHub repository alebgl77/claude-inc (22 stars, last pushed 15d ago), licensed MIT. It adds 88 tokens to every session and 1,475 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-27.

Related

account-intelligence compared with skills from other repositories

account-intelligence next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
account-intelligencealebgl77/claude-incSkill2215d agoMITA88Claude Code
mcp-larkaahl/skillsSkill1641mo agoMITA45Claude Code, Codex
checklistdeciqAI/knowledge-skillsSkill1312d agoMITA105Claude Code, Codex
deep-workdeciqAI/knowledge-skillsSkill1312d agoMITA123Claude Code, Codex
dichotomy-of-controldeciqAI/knowledge-skillsSkill1312d agoMITA143Claude Code, Codex
intrinsic-drivedeciqAI/knowledge-skillsSkill1312d agoMITA107Claude Code, Codex
knowing-and-doing-as-onedeciqAI/knowledge-skillsSkill1312d agoMITA118Claude Code, Codex