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 deal-deskgit 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/deal-desk)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/deal-desk"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/deal-desk/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/deal-desk"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/deal-desk.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 | $0.00164 | $0.02726 |
| Opus 5 | $0.00082 | $0.01363 |
| Sonnet 5 | $0.00033 | $0.00545 |
| Haiku 4.5 | $0.00016 | $0.00273 |
Grade A, and why
deal-desk 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.
This is a copy
92% identical to deal-desk — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
deal-desk
Per-deal review and discount-approval routing. Scores deal margin + risk, routes discount approval to the right human, redlines T&Cs against commercial policy. Never auto-approves. Every output is a score plus a routing recommendation to a named human approver.
Purpose
Deal Desk / RevOps / sales leadership live at the moment between sales-team-asks-for-discount and CFO/CRO/legal-signs. This skill quantifies the asks and routes them.
Three deterministic tools:
deal_scorer.py— Scores a deal 0-100 across 5 dimensions (margin, risk, strategic value, commercial fit, term shape) and assigns one of four verdicts: APPROVE / REVIEW / ESCALATE / DECLINE — each tied to a named approver chain.discount_approval_router.py— Maps a discount-percent + deal-size + tier to a named approver chain (AE → Manager → Director → VP → CFO/CRO) with estimated cycle days. Honors industry-tuned policy bands.terms_redliner.py— Detects 10 founder/seller-killer patterns in deal terms (uncapped indemnity, MFN, perpetual license-back, missing DPA, NET-60+, broad non-solicit, etc.) with severity + standard counter + named legal/commercial approver.
When to use
Invoke this skill when:
- Sales has flagged a discount request above AE authority.
- A customer has returned a redlined MSA and you need triage before routing to legal.
- The deal needs CFO sign-off and you want a defensible margin breakdown.
- An RFP response requires multi-year terms and you need to score the shape.
- A renewal expansion is bundled with a discount and you need to verify policy fit.
- You're building a deal-desk approval queue and need consistent routing.
Do NOT use this skill to: author the proposal (use business-growth/contract-and-proposal-writer), redesign the discount matrix (use the commercial-policy sibling skill), or do deep legal redline of full contract text (use c-level-advisor/skills/general-counsel-advisor).
Workflow
- Intake the deal — Sales/AE fills
assets/deal_intake_template.mdwith ARR, term, discount, payment terms, customer tier, strategic flags, and any customer-flagged term redlines (20-min fill-out). - Score margin + risk — Run
deal_scorer.py --input deal.json --profile {saas|enterprise-software|services|marketplace}. Read the composite + per-dimension breakdown + verdict. - Route the discount — Run
discount_approval_router.py --input deal.json --profile <same>. Get the named approver chain + estimated cycle days. Modifiers (enterprise floor, SMB fast-lane) are surfaced explicitly. - Flag the redlines — Run
terms_redliner.py --input deal_terms.json. Get ranked CRITICAL/HIGH/MEDIUM/LOW findings with the counter-language and the approver who must sign each. - Assemble the packet — Combine the three outputs into a deal-desk review packet. Always include the named approver chain. The packet is a recommendation, not an approval.
What ships with it
8 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 189 B
- assets/deal_intake_template.md 4.8 KB
- references/contract_landmines.md 9.0 KB
- references/deal_desk_canon.md 5.1 KB
- references/discount_economics.md 6.1 KB
- scripts/deal_scorer.py 13 KB runs code
- scripts/discount_approval_router.py 8.9 KB runs code
- scripts/terms_redliner.py 11 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 · 139 lines · 164 tokens per session scan A 39dcfb0d5098
deal-desk is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 164 tokens to every session and 2,726 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to deal-desk, differing in 7 lines, and is treated as a copy.
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