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 alebgl77/claude-inc --skill account-researchgit clone --depth 1 https://github.com/alebgl77/claude-incWrote 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/alebgl77/claude-inc/account-research)<a href="https://agentmods.dev/skills/alebgl77/claude-inc/account-research"><img src="https://agentmods.dev/badge/skills/alebgl77/claude-inc/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/alebgl77/claude-inc/account-research"><img src="https://agentmods.dev/badge/skills/alebgl77/claude-inc/account-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00070 | $0.00651 |
| Opus 5 | $0.00035 | $0.00326 |
| Sonnet 5 | $0.00014 | $0.00130 |
| Haiku 4.5 | $0.00007 | $0.00065 |
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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Account Research — Prospector
"Know them better than their own website does."
When to use
- "Research {company} before I contact them"
- "Who is {name} at {company}?"
- "Why would {company} buy {my product}?"
- Always before
draft-outreachorcall-prep— research is their fuel - Works from public web (search + site + LinkedIn-style signals); upgrades with any CRM/enrichment MCP if connected
Workflow
- Frame the mission: what does the user sell, and what would make this account a good or bad fit? Write the fit hypothesis first.
- Company scan: what they do (in one sentence a human would say), size/stage signals, business model, who their customers are.
- Signal sweep: last 90 days — funding, hiring, launches, leadership changes, tech choices, public complaints. Date every signal.
- People map: likely buyer, likely champion, likely blocker — with role-based reasoning when names aren't public.
- Pain hypotheses: top 3, each tied to an observed signal (not generic industry pains).
- Entry angle: the single most credible reason to talk to them THIS month, plus 2 conversation openers in natural language.
- Disqualifiers: honest list of reasons to skip this account. Recommendation: pursue / park / drop.
Output format
## Account brief — {company}
Fit hypothesis: {1 line} · Verdict: PURSUE / PARK / DROP
**What they do**: ...
**Signals (dated)**: • {date} — {signal} → {why it matters}
**People**: buyer {role} · champion {role} · blocker {role}
**Top pains (evidence-tied)**: 1. ... 2. ... 3. ...
**Entry angle**: ...
**Openers**: "..." / "..."
**Disqualifiers**: ...
Quality bar
- Every signal is dated and sourced (link or "observed on their site")
- Pains reference evidence, not industry clichés
- The entry angle would survive being read aloud to the prospect
- A clear verdict — no fence-sitting
- Under 1 page; a rep can absorb it in 3 minutes
Example
Ask: "Research Maison Verdier (wine e-commerce) for my SEO service." Produced: brief showing recent PrestaShop 9 migration + 2 job posts for marketing, pain hypothesis "traffic lost in migration", entry angle referencing their broken category pages, verdict PURSUE with two openers.
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.
- 9d ago First seen · 55 lines · 70 tokens per session scan A 87ac4bf7d580
account-research is a skill published in the GitHub repository alebgl77/claude-inc (14 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 651 once invoked, about $0.0003 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 2026-08-30.
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