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 bestagentkits/agency-skills --skill claude-skills-business-growth-sales-engineer-sales-engineergit clone --depth 1 https://github.com/bestagentkits/agency-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/bestagentkits/agency-skills/claude-skills-business-growth-sales-engineer-sales-engineer)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/claude-skills-business-growth-sales-engineer-sales-engineer"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/claude-skills-business-growth-sales-engineer-sales-engineer/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/bestagentkits/agency-skills/claude-skills-business-growth-sales-engineer-sales-engineer"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/claude-skills-business-growth-sales-engineer-sales-engineer.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 | $0.00132 | $0.02031 |
| Opus 5 | $0.00066 | $0.01015 |
| Sonnet 5 | $0.00026 | $0.00406 |
| Haiku 4.5 | $0.00013 | $0.00203 |
Grade A, and why
claude-skills-business-growth-sales-engineer-sales-engineer 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sales Engineer Skill
5-Phase Workflow
Phase 1: Discovery & Research
Objective: Understand customer requirements, technical environment, and business drivers.
Checklist:
- Conduct technical discovery calls with stakeholders
- Map customer's current architecture and pain points
- Identify integration requirements and constraints
- Document security and compliance requirements
- Assess competitive landscape for this opportunity
Tools: Run rfp_response_analyzer.py to score initial requirement alignment.
python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json --format json > phase1_rfp_results.json
Output: Technical discovery document, requirement map, initial coverage assessment.
Validation checkpoint: Coverage score must be >50% and must-have gaps ≤3 before proceeding to Phase 2. Check with:
python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json --format json | python -c "import sys,json; r=json.load(sys.stdin); print('PROCEED' if r['coverage_score']>50 and r['must_have_gaps']<=3 else 'REVIEW')"
Phase 2: Solution Design
Objective: Design a solution architecture that addresses customer requirements.
Checklist:
- Map product capabilities to customer requirements
- Design integration architecture
- Identify customization needs and development effort
- Build competitive differentiation strategy
- Create solution architecture diagrams
Tools: Run competitive_matrix_builder.py using Phase 1 data to identify differentiators and vulnerabilities.
python scripts/competitive_matrix_builder.py competitive_data.json --format json > phase2_competitive.json
python -c "import json; d=json.load(open('phase2_competitive.json')); print('Differentiators:', d['differentiators']); print('Vulnerabilities:', d['vulnerabilities'])"
Output: Solution architecture, competitive positioning, technical differentiation strategy.
What ships with it
12 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.
- agents/openai.yaml 242 B
- assets/demo_script_template.md 7.6 KB
- assets/expected_output.json 14 KB
- assets/poc_scorecard_template.md 5.6 KB
- assets/sample_rfp_data.json 7.5 KB
- assets/technical_proposal_template.md 6.7 KB
- references/competitive-positioning-framework.md 9.2 KB
- references/poc-best-practices.md 11 KB
- references/rfp-response-guide.md 7.2 KB
- scripts/competitive_matrix_builder.py 17 KB runs code
- scripts/poc_planner.py 26 KB runs code
- scripts/rfp_response_analyzer.py 19 KB runs code
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 · 226 lines · 132 tokens per session scan A 682e6c239c04
claude-skills-business-growth-sales-engineer-sales-engineer is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 132 tokens to every session and 2,031 once invoked, about $0.0007 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 2026-09-03.
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