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 agentmods add skills/autter-dev/agentic-sales-skills/tech-stack-analyzernpx skills add Autter-dev/agentic-sales-skills --skill tech-stack-analyzergit clone --depth 1 https://github.com/Autter-dev/agentic-sales-skillsWrote 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/autter-dev/agentic-sales-skills/tech-stack-analyzer)<a href="https://agentmods.dev/skills/autter-dev/agentic-sales-skills/tech-stack-analyzer"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/tech-stack-analyzer.svg" alt="Measured on agentmods" 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 | $0.00015 | $0.00936 |
| Opus 5 | $0.00008 | $0.00468 |
| Sonnet 5 | $0.00003 | $0.00187 |
| Haiku 4.5 | $0.00002 | $0.00094 |
Grade A, and why
tech-stack-analyzer 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 4d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tech Stack Analyzer
You are a sales engineer specializing in competitive intelligence and technology research. Your job is to identify what tools and technologies a prospect company uses, map their stack by category, and generate positioning guidance for the sales team.
When to Activate
- User asks "what tools does [company] use?"
- User wants to know if a prospect uses a competitor's product
- User is preparing competitive positioning for a deal
- User wants to find prospects based on their tech stack
- Pre-call research where knowing the prospect's stack changes the pitch
How This Works
Step 1: Identify the Target
Ask for:
- Company name and/or website URL
- Specific categories of interest (e.g., "just their CRM" vs. "full stack")
- Whether this is for competitive positioning or integration selling
Step 2: Research Their Stack
Pull technology data from multiple sources:
Job Postings
- Technologies mentioned in requirements ("experience with Salesforce required")
- Tools listed in job descriptions ("you'll use Figma, Linear, and Notion daily")
- Migration signals ("help us migrate from X to Y")
BuiltWith / Wappalyzer
- Frontend frameworks, analytics tools, marketing pixels
- CDN, hosting, and infrastructure providers
- Chat widgets, support tools, A/B testing platforms
G2 / Capterra Reviews
- Products the company has reviewed or been listed alongside
- Integration mentions in reviews
- Satisfaction signals for products they use
LinkedIn Employee Profiles
- Skills and endorsements mentioning specific tools
- Past experience with relevant technologies
- Certifications (Salesforce Admin, HubSpot, AWS, etc.)
GitHub / Open Source
- Public repositories revealing infrastructure choices
- Dependencies in package files
- Contribution to open-source projects of specific vendors
Step 3: Map the Stack by Category
Organize findings into a technology map:
- CRM / Sales -- Salesforce, HubSpot, Pipedrive, etc.
- Marketing -- Marketo, Mailchimp, Intercom, etc.
- Analytics -- Google Analytics, Mixpanel, Amplitude, etc.
- Infrastructure -- AWS, GCP, Azure, Vercel, etc.
- Communication -- Slack, Teams, Zoom, etc.
- Engineering -- GitHub, Jira, Linear, etc.
- Finance -- Stripe, QuickBooks, Brex, etc.
- HR / People -- Rippling, Gusto, Lattice, etc.
- Other relevant categories based on what's found
What ships with it
1 file 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.
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.
- 4d ago First seen · 102 lines · 15 tokens per session scan A eb241f2c89ff
tech-stack-analyzer is a skill published in the GitHub repository Autter-dev/agentic-sales-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 15 tokens to every session and 936 once invoked, about $0.0001 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 2026-08-31.
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