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 SymbolicLight-AGI/contractual-skill --skill deal-closer-playbookgit clone --depth 1 https://github.com/SymbolicLight-AGI/contractual-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/symboliclight-agi/contractual-skill/deal-closer-playbook)<a href="https://agentmods.dev/skills/symboliclight-agi/contractual-skill/deal-closer-playbook"><img src="https://agentmods.dev/badge/skills/symboliclight-agi/contractual-skill/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/symboliclight-agi/contractual-skill/deal-closer-playbook"><img src="https://agentmods.dev/badge/skills/symboliclight-agi/contractual-skill/deal-closer-playbook.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 | $0.00044 | $0.01157 |
| Opus 5 | $0.00022 | $0.00579 |
| Sonnet 5 | $0.00009 | $0.00231 |
| Haiku 4.5 | $0.00004 | $0.00116 |
Grade A, and why
deal-closer-playbook-contractual 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 12d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deal Closer Playbook Contract
When To Use
Use this Skill when a sales team needs a structured closing strategy for a deal in progress, from discovery through negotiation.
Do not use it to make binding customer commitments, approve discounts, alter contract terms, send external messages, or claim current company research without tool evidence.
Goal
Produce a tactical deal playbook that helps the rep advance the deal. The playbook should connect deal context, company intelligence, stakeholder mapping, risk assessment, objection responses, competitive positioning, next-best actions, and mutual close plan.
Audience
- Account executives and sales managers.
- Revenue leaders reviewing deal quality.
- Customer success, solutions, legal, procurement, or executive sponsors involved in closing.
Inputs
Required:
- Company name.
- Product or service being sold and pricing model.
- Current deal stage.
- Primary contact name and title.
- Deal size.
- Target close date.
Highly valuable:
- Known objections, competitors, champion, economic buyer, technical evaluator, blockers, interaction history, procurement process, security/legal review status, decision criteria, and timeline pressures.
Privacy:
- Do not expose customer personal data beyond what is necessary.
- Do not include confidential pricing or contract details unless supplied for the task.
- Use placeholders for synthetic tasks.
If required inputs are missing, mark them [UNKNOWN], ask for missing items when needed, and avoid overconfident recommendations.
Context
Use provided deal context first. Use web research only when the task explicitly allows it and a browsing/search tool is available. If web research is unavailable, state that company intelligence is based only on provided materials.
Workflow
- Collect deal context and mark missing fields as
[UNKNOWN]. - Research or summarize company context only from allowed sources.
- Map buying committee roles: champion, economic buyer, technical evaluator, user buyer, coach, blocker, procurement/legal, and executive sponsor.
- Assess deal risks: qualification gaps, urgency, competition, blocker influence, procurement/legal/security risk, and close-date realism.
- Build objection response matrix for known and anticipated objections.
- Build competitive positioning using only supplied or sourced information.
- Design closing strategy based on deal stage.
- Build mutual close plan with milestones, owner, date, dependency, and risk.
- Generate proposal talking points and negotiation guidance.
- Produce the deal playbook with next actions and handoffs.
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.
- 12d ago First seen · 159 lines · 44 tokens per session scan A 89231cac036f
deal-closer-playbook-contractual is a skill published in the GitHub repository SymbolicLight-AGI/contractual-skill (21 stars, last pushed 3mo ago), licensed MIT. It adds 44 tokens to every session and 1,157 once invoked, about $0.0002 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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