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.
npx skills add charlieviettq/awesome-agent-skill --skill account-researchgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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.
[](https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/account-research)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/account-research"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/account-research/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.
<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/account-research"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/account-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.01754 |
| Opus 5.5 | $0.00028 | $0.00702 |
| Sonnet 5.5 | $0.00014 | $0.00351 |
| Haiku 4.5 | $0.00007 | $0.00175 |
Grade A, and why
account-research 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.
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.
This is a copy
88% identical to account-research — 13 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Account Research
Get a complete picture of any company or person before outreach. This skill always works with web search, and gets significantly better with enrichment and CRM data.
How It Works
┌─────────────────────────────────────────────────────────────────┐
│ ACCOUNT RESEARCH │
├─────────────────────────────────────────────────────────────────┤
│ ALWAYS (works standalone via web search) │
│ ✓ Company overview: what they do, size, industry │
│ ✓ Recent news: funding, leadership changes, announcements │
│ ✓ Hiring signals: open roles, growth indicators │
│ ✓ Key people: leadership team from LinkedIn │
│ ✓ Product/service: what they sell, who they serve │
├─────────────────────────────────────────────────────────────────┤
│ SUPERCHARGED (when you connect your tools) │
│ + Enrichment: verified emails, phone, tech stack, org chart │
│ + CRM: prior relationship, past opportunities, contacts │
└─────────────────────────────────────────────────────────────────┘
Getting Started
Just tell me who to research:
- "Research Stripe"
- "Look up the CTO at Notion"
- "Intel on acme.com"
- "Who is Sarah Chen at TechCorp?"
- "Tell me about [company] before my call"
I'll run web searches immediately. If you have enrichment or CRM connected, I'll pull that data too.
Connectors (Optional)
Connect your tools to supercharge this skill:
| Connector | What It Adds |
|---|---|
| Enrichment | Verified emails, phone numbers, tech stack, org chart, funding details |
| CRM | Prior relationship history, past opportunities, existing contacts, notes |
No connectors? No problem. Web search provides solid research for any company or person.
Output Format
# Research: [Company or Person Name]
**Generated:** [Date]
**Sources:** Web Search [+ Enrichment] [+ CRM]
---
## Quick Take
[2-3 sentences: Who they are, why they might need you, best angle for outreach]
---
## Company Profile
| Field | Value |
|-------|-------|
| **Company** | [Name] |
| **Website** | [URL] |
| **Industry** | [Industry] |
| **Size** | [Employee count] |
| **Headquarters** | [Location] |
| **Founded** | [Year] |
| **Funding** | [Stage + amount if known] |
| **Revenue** | [Estimate if available] |
### What They Do
[1-2 sentence description of their business, product, and customers]
### Recent News
- **[Headline]** — [Date] — [Why it matters for your outreach]
- **[Headline]** — [Date] — [Why it matters]
### Hiring Signals
- [X] open roles in [Department]
- Notable: [Relevant roles like Engineering, Sales, AI/ML]
- Growth indicator: [Hiring velocity interpretation]
---
## Key People
### [Name] — [Title]
| Field | Detail |
|-------|--------|
| **LinkedIn** | [URL] |
| **Background** | [Prior companies, education] |
| **Tenure** | [Time at company] |
| **Email** | [If enrichment connected] |
**Talking Points:**
- [Personal hook based on background]
- [Professional hook based on role]
[Repeat for relevant contacts]
---
## Tech Stack [If Enrichment Connected]
| Category | Tools |
|----------|-------|
| **Cloud** | [AWS, GCP, Azure, etc.] |
| **Data** | [Snowflake, Databricks, etc.] |
| **CRM** | [e.g. Salesforce, HubSpot] |
| **Other** | [Relevant tools] |
**Integration Opportunity:** [How your product fits with their stack]
---
## Prior Relationship [If CRM Connected]
| Field | Detail |
|-------|--------|
| **Status** | [New / Prior prospect / Customer / Churned] |
| **Last Contact** | [Date and type] |
| **Previous Opps** | [Won/Lost and why] |
| **Known Contacts** | [Names already in CRM] |
**History:** [Summary of past relationship]
---
## Qualification Signals
### Positive Signals
- ✅ [Signal and evidence]
- ✅ [Signal and evidence]
### Potential Concerns
- ⚠️ [Concern and what to watch for]
### Unknown (Ask in Discovery)
- ❓ [Gap in understanding]
---
## Recommended Approach
**Best Entry Point:** [Person and why]
**Opening Hook:** [What to lead with based on research]
**Discovery Questions:**
1. [Question about their situation]
2. [Question about pain points]
3. [Question about decision process]
---
## Sources
- Source 1 (add URL when publishing)
- Source 2 (add URL when publishing)
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.
- 1mo ago First seen · 283 lines · 71 tokens per session scan A 15c73c17b989
account-research is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (26 stars, last pushed 3mo ago), licensed MIT. It adds 71 tokens to every session and 1,754 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to account-research, differing in 13 lines, and is treated as a copy.
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