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 OneWave-AI/claude-skills --skill deal-closer-playbookgit clone --depth 1 https://github.com/OneWave-AI/claude-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/onewave-ai/claude-skills/deal-closer-playbook)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/deal-closer-playbook"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/deal-closer-playbook/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/onewave-ai/claude-skills/deal-closer-playbook"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/deal-closer-playbook.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 | $0.00057 | $0.01013 |
| Opus 5 | $0.00028 | $0.00507 |
| Sonnet 5 | $0.00011 | $0.00203 |
| Haiku 4.5 | $0.00006 | $0.00101 |
Grade A, and why
deal-closer-playbook 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deal Closer Playbook
Take a deal in progress -- at any stage from discovery to negotiation -- and produce a tactical closing playbook combining company research, stakeholder mapping, competitive intelligence, and deal mechanics into a single actionable document.
Contents
references/intelligence-gathering.md-- context intake, company research, buying-committee map, MEDDIC and velocity risk scoringreferences/playbook-strategy.md-- objection matrix, competitive positioning, stage-specific closing strategy, mutual close plan, proposal talking pointsreferences/output-template.md-- fulldeal-playbook.mdstructure to write
Workflow
Operate in two phases: gather intelligence, then generate the playbook. Be thorough but fast. Tie every section to a specific action.
- Collect deal context. Gather the required, valuable, and nice-to-have inputs. Ask for anything missing; mark unavailable items
[UNKNOWN]and work around them. Seereferences/intelligence-gathering.md. - Research the company. Use WebSearch for current intelligence: overview, last-90-days news, financial signals, leadership/hiring, tech-stack signals, industry context. See
references/intelligence-gathering.md. - Map the buying committee. Identify and profile each role (champion, economic buyer, technical evaluator, user buyer, coach, blocker, procurement/legal, executive sponsor). Flag unknown stakeholders as discovery gaps. See
references/intelligence-gathering.md. - Assess deal risk. Score MEDDIC qualification and the velocity risk checklist. See
references/intelligence-gathering.md. - Build the objection response matrix. Address every raised objection plus likely unraised ones for the stage and context. See
references/playbook-strategy.md. - Build competitive positioning. For each competitor (or the status quo), document their pitch, where they win, where you win, landmine questions, and traps to avoid. See
references/playbook-strategy.md. - Design the stage-appropriate closing strategy. Match tactics to discovery/demo, evaluation/proposal, or negotiation/close. See
references/playbook-strategy.md. - Build the mutual close plan. Create the shared buyer-seller timeline to signed contract. See
references/playbook-strategy.md. - Generate proposal talking points. Draft the opening, value prop, proof, differentiation, and the ask. See
references/playbook-strategy.md. - Write the deal playbook. Output the complete document to
deal-playbook.mdusingreferences/output-template.md.
What ships with it
3 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.
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 · 50 lines · 57 tokens per session scan A 533013471e77
deal-closer-playbook is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 1,013 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 2026-09-03.
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