icp-deep-scanner — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited icp-deep-scanner (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Turn the data already sitting in your connected tools into a rigorous, evidence-backed Ideal Customer Profile (ICP) and a library of buyer personas. Most ICPs are invented in a slide deck. This one is reverse-engineered from your actual best customers, your won/lost deals, your support tickets, and your reviews — then written so it can drive real decisions and feed the panel skills.
This skill is the data layer beneath customer-panel-of-experts, prospect-panel-simulator, and product-launch-war-room. Run it first; those skills read the persona library it writes.
$SUPABASE_TOKEN, $OPENAI_API_KEY) or in the MCP connection itself. If a source needs auth that isn't present, list it under "Sources I could not reach" and continue.Ask the user which tools to scan, or detect what's available. Map each to what it tells you:
| Source (examples) | What to extract | How to reach it |
|---|---|---|
| CRM (HubSpot, Salesforce, internal) | Closed-won vs closed-lost firmographics, titles of champions/buyers, deal size, sales cycle, win reasons | MCP connector or read-only API |
| Email / calendar | Who actually engages, meeting cadence, recurring objection language | Gmail/Calendar MCP, read-only |
| Support / tickets / chat | Top pain themes, words customers use, where they get stuck | Intercom/Zendesk export, logs |
| Reviews (G2, Capterra, Trustpilot, App Store) | Verbatim value language, switching triggers, deal-breakers | WebFetch / customer-review-aggregator |
| Product analytics (GA4, Clarity, Mixpanel) | Activation paths, who sticks, drop-off points | Analytics MCP / API |
| Billing (Stripe, Mercury) | Real revenue concentration, expansion vs churn by segment | Read-only API |
| Database (Supabase/Postgres) | Ground-truth usage and cohort behavior | Read-only SQL via $SUPABASE_TOKEN |
| Public web | Firmographic enrichment, market sizing, competitor positioning | WebSearch / WebFetch |
Present the list, mark which are reachable now, and confirm scope before scanning. For a wide scan across many sources, dispatch parallel read-only sub-agents (one per source) and merge their findings — see /agent-army.
For each reachable source, pull:
Record sample sizes and date ranges for everything. Flag anything based on fewer than ~5 data points as "thin signal."
Write icp-profile.md:
# Ideal Customer Profile — {COMPANY}
Generated: {timestamp} · Sources scanned: {list} · Confidence: {High/Med/Low}
## The ICP in one sentence
{Vertical} companies of {size} who {trigger}, evaluated against {alternative}, where the champion is a {title} and the economic buyer is a {title}.
## Firmographic fit (with evidence)
- Industry: ... (evidence: N of M closed-won)
- Size: ...
- Geography / model / stack: ...
## Anti-ICP — who to disqualify
- {Segment} — closes slow, churns fast, low ACV (evidence)
## Buying committee
- Economic buyer · Champion · Blocker · End user — each with real titles + what they care about
## Triggers & jobs-to-be-done
## Top buy reasons / top no-buy reasons (ranked, with counts)
## The customer's own language (verbatim, scrubbed)
## Economics — ACV, cycle, expansion, concentration risk
## Confidence & gaps — what's thin, what to instrument nextWrite personas/ — one file per persona (3–6 personas: typically the champion, the economic buyer, the blocker, and 1–2 key end users or segment variants). Each persona file is structured so the panel skills can load it directly:
---
persona_id: ops-leader-champion
role: Champion
archetype: "VP of Operations at a 50–200 person services firm"
based_on: "12 closed-won champions, CRM trailing 12 mo"
---
# {Archetype name}
- Goals / success metrics:
- Pains (verbatim language):
- What earns trust / what triggers skepticism:
- Buying authority & budget reality:
- Objections they raise (real, from lost deals):
- How they talk (tone, vocabulary, 2–3 scrubbed quotes):
- What would make them a hard NO:Also write personas/index.md listing every persona, its role in the committee, and its evidence base.
End with:
customer-panel-of-experts (debate a decision with these personas) or prospect-panel-simulator (pressure-test a pitch against them).Read-only unless told otherwise · no secrets in output · personas are archetypes, never dossiers · every claim cites its source and sample size · thin signal is labeled, not hidden.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.