Agent skill

Deal Desk

by borghei in borghei/Claude-Skills

Deal desk: reviews, approves, and structures non-standard sales deals.

MITAuto-check passedSales & Support

Install Deal Desk

skills CLI
$ npx skills add borghei/Claude-Skills --skill deal-desk -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install borghei/Claude-Skills deal-desk --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/business-growth/deal-desk .claude/skills/deal-desk && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
deal-desk
GitHub stars
891
Token cost
~4.6k tokens
SKILL.md length
1,585 words
Files
7 (incl. scripts, references)
Skills in repo
354
Repo updated
First seen
Licence
MIT

At a glance

Deal desk: reviews, approves, and structures non-standard sales deals.

  • Works in 9 steps: Rep submits via intake form: customer +… → Deal desk triages within 4h: assigns… → Deal desk reviews within 1 business day:… → …
  • Standing up a deal desk
  • SKILL.md covers When to use this skill, What deal desk does (and…, Deal-desk charter (template) and Approval threshold matrix, plus 8 more sections
  • Runs Python scripts from its folder

What it does

Deal Desk is an agent skill from borghei/Claude-Skills. Deal desk: reviews, approves, and structures non-standard sales deals. Use when standing up a deal desk, building approval-threshold matrices, designing deal-review packets, routing deals, or auditing deals for compliance.

Its SKILL.md is about 4.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/approval-thresholds-and-routing.md`, `references/deal-desk-charter-and-process.md` and `references/discount-and-concession-playbook.md`).

It sits in Sales & Support. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.

When your agent uses it

  • Standing up a deal desk
  • Building approval-threshold matrices
  • Designing deal-review packets
  • Auditing deals for compliance

Example prompts

  • “/deal-desk”

Requirements

  • Python 3

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

  1. Rep submits via intake form: customer + ACV + requested deviation + justification
  2. Deal desk triages within 4h: assigns analyst, validates packet completeness, requests missing info
  3. Deal desk reviews within 1 business day: financial impact, strategic value, risk
  4. Deal desk recommends approve / counter / decline
  5. Route to approver(s) per matrix (auto via scripts/discount_authority_router.py)
  6. Approver decides within SLA
  7. If approved: packet signed off, conditions sent to rep with expiration
  8. If countered: deal desk works with rep on alternative structure
  9. If declined: clear reason + alternatives sent to rep + customer

What it can do on your machine

Read from SKILL.md and the folder at commit 4a698e8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Deal Desk loads about 4.6k tokens when it runs, and up to ~16k if it reads all its reference files. Until then it costs about 58 tokens; SKILL.md has 1,585 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~58
When it runs · the whole SKILL.md, loaded when a task matches
~4.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~16k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from borghei/Claude-Skills at commit 4a698e8, republished under its MIT licence (© borghei). 1,585 words, ~4,603 tokens.

Download SKILL.mdSave it as .claude/skills/deal-desk/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
deal-desk
description
Deal desk: reviews, approves, and structures non-standard sales deals. Use when standing up a deal desk, building approval-threshold matrices, designing deal-review packets, routing deals, or auditing deals for compliance.
license
MIT + Commons Clause
metadata.version
1.0.0
metadata.author
borghei
metadata.category
commercial
metadata.domain
business-growth
metadata.updated
2026-05-27
metadata.tags
deal-desk, sales-operations, discount-approval, commercial-operations, contract-review, deal-velocity, gtm

Deal Desk

End-to-end deal-desk operational practice: charter, approval thresholds, deal-review packet design, routing automation, velocity analysis, and the governance that turns "every deal is a snowflake" into "we close non-standard deals in 48 hours predictably."

This skill is provider-agnostic: works whether your CRM is Salesforce, HubSpot, Pipedrive, or homegrown. The patterns and decisions transfer.


When to use this skill

SituationSkill applies
Starting a deal-desk function from scratchYes — start with charter design
Reviewing existing deal-desk for slowness / inconsistencyYes — use scripts/deal_velocity_analyzer.py + bottleneck patterns
Defining who can approve what discount / termYes — use approval threshold matrix + scripts/discount_authority_router.py
Building the deal-review packet templateYes — see deal-review packet section + scripts/deal_review_packet.py
Approving / declining a specific dealUse the packet generator + approval router
Setting pricing strategyUse business-growth/pricing-strategy first
Forecasting / measuring pipelineUse business-growth/revenue-operations
Negotiating an individual contractPair with business-growth/contract-and-proposal-writer

What deal desk does (and doesn't)

Does:

  • Review non-standard deals: discounts beyond rep authority, custom legal terms, custom SLAs, multi-product bundles, payment terms outside policy
  • Make the approval decision (or route to the right approver)
  • Structure the deal: pricing, terms, ramp schedule, success criteria
  • Maintain the deal-desk policy — what's standard, what needs approval
  • Track deal velocity (time from request → decision → signature)
  • Produce evidence for finance / audit (every concession traceable)

Doesn't:

  • Set the published pricing (that's pricing strategy)
  • Negotiate with the customer (that's the sales rep / AE)
  • Close the sale (that's the rep + customer success)
  • Run the order-to-cash workflow (that's billing / RevOps)
  • Replace legal review (legal is one of the approvers, not the function itself)

A clean deal-desk = the lubricant. Without it, every non-standard deal turns into a multi-week negotiation among engineering / product / legal / finance / executive. With it, those people are consulted by deal desk as needed and the rep gets a yes/no in days.


Deal-desk charter (template)

Every deal desk needs a written charter. Use this template:

yaml
purpose:
  Deal Desk reviews, approves, and structures non-standard deals to enable
  sales to close faster while keeping commercial / legal / financial risk
  within company tolerance.

scope:
  In-scope:
    - All deals > $X ARR
    - All deals with discount > Y%
    - All deals with non-standard terms (custom SLAs, custom legal language,
      payment terms beyond Net 30, ramp deals, multi-year discounts > 12 months
      of standard, bundles spanning multiple product lines)
    - All renewals with > 20% expansion or > 10% contraction
    - All deals to enterprise (>1000 employees) or regulated industries
  Out-of-scope:
    - Self-serve / PLG transactions
    - Standard renewals within auto-renewal terms
    - Trial extensions < 30 days
    - Add-ons < $X per existing customer

sla:
  - Standard deal-desk review (no exec approval needed): 1 business day
  - Deal needing CFO/CRO approval: 2 business days
  - Deal needing CEO/Board approval: 5 business days
  - Legal-only review (no commercial concession): 2 business days

intake_format:
  Sales submits via [Salesforce form / CPQ tool / Slack form]. Required fields:
    - Customer name + size + industry
    - Product(s) + ACV
    - Requested deviation from standard (specific list)
    - Justification (competitor situation, customer constraint, strategic value)
    - Standard-pricing total + requested total
    - Contract length + payment terms
    - Implementation / SLA requirements

decision_inputs:
  - Customer LTV estimate
  - Strategic value (logo, reference, vertical foothold)
  - Risk (credit, compliance, integration)
  - Margin impact

outputs:
  - Approve / decline / counter
  - If approve: signed approval packet with terms, conditions, expiration date
  - If counter: list of negotiable items + non-negotiables
  - If decline: reasoning + alternatives

team:
  Deal-desk lead: <name>
  Deal-desk analysts: <names>
  Standing approvers: CRO, CFO, General Counsel, VP Product (escalation paths)
  Consulted as-needed: Engineering Lead, Security Lead, Customer Success Lead

metrics:
  - Median time-to-decision (target: 1 business day)
  - Decision distribution (% approved, % declined, % countered)
  - Discount-on-discount %  (deals where requested discount was further negotiated up)
  - Discount % vs ACV (correlation; outliers reviewed monthly)
  - Win rate of deal-desk-approved deals
  - Concession follow-through (did the customer keep their side?)

See references/deal-desk-charter-and-process.md for the full charter template, including sub-charters per region, intake form spec, and the standard SLAs.


Approval threshold matrix

The matrix defines: for each deal characteristic (discount %, contract length, custom term type), who can approve it.

Standard matrix template
Deal characteristicRepSales ManagerDirectorVP SalesCROCFOCEO
Discount 0-10%✓
Discount 10-20%✓
Discount 20-30%✓
Discount 30-40%✓
Discount 40-50%✓
Discount > 50%✓
ACV > $250k✓
ACV > $1M✓
ACV > $5M✓
Multi-year > 12mo standard✓
Non-standard payment terms✓
Custom SLA / penalties(with CCO)
Custom legal language(Legal must concur)
MSA red-line on liability cap(Legal must concur)
Most-favored-nation clause✓
Acceptance criteria / payment-on-acceptance✓
Multi-product / cross-BU bundle(each BU lead approves)
Whitelabel / OEM rights✓

Customize per company stage, ACV distribution, and authority preference (some orgs want CRO at 30%, others delegate further down).

Stacking rule

When multiple non-standard items apply, the highest required approver applies. A $1M deal at 25% discount with custom SLA needs VP Sales (ACV) AND Director (discount) AND VP Sales+CCO (custom SLA) → effectively requires VP Sales sign-off + CCO + Legal concurrence.

Use scripts/discount_authority_router.py --deal deal.yaml to compute the required approvers for any deal.

See references/approval-thresholds-and-routing.md for the full matrix design guide, regional variants, escalation paths, and routing automation patterns.


The deal-review packet

Every non-standard deal gets a packet. Without it, approvers ask the same questions repeatedly and decisions take days instead of hours.

Standard packet structure
markdown
# Deal Review: <Customer Name>

## Summary
- Customer: <name, size, industry>
- ACV: $<amount>
- Discount %: <%> (vs standard $<list-price>)
- Contract: <length>, <payment terms>
- Decision needed by: <date>

## Standard vs Requested
| Item | Standard | Requested | Delta |
|------|----------|-----------|-------|
| ACV  | $X       | $Y        | -Z%   |
| Term | 12mo     | 36mo      | +24mo |
| Payment | Net 30 | Net 60   | +30d  |
| SLA  | 99.5%    | 99.9%     | +0.4% |
| Liability cap | 1x fees | 2x fees | +1x |
| Termination for convenience | No | Yes (90d) | New |

## Justification
- Why customer wants this: <competitor situation, budget cycle, etc.>
- Why we're considering: <strategic value, logo, vertical>
- Customer leverage: <alternatives they have>

## Financial impact
- Standard ARR: $X
- Discounted ARR: $Y (Z% off)
- Net new gross margin: $A (with cost overlay)
- Projected LTV with this discount: $B
- Discount payback if customer renews: <years>

## Strategic value
- Logo value: <high/medium/low — reasoning>
- Reference value: <will they be a public ref? case study?>
- Vertical foothold: <do we want this vertical?>
- Competitive replacement: <who are we displacing?>

## Risk
- Credit risk: <score / payment history>
- Compliance risk: <regulated? data residency?>
- Technical fit risk: <integration complexity>
- Concession follow-through: <are they likely to honor commitments?>

## Required approvers (per matrix)
- [ ] Director: <name>
- [ ] VP Sales: <name>
- [ ] CFO: <name>
- [ ] Legal: <name>

## Recommendation (from deal desk)
<Approve / Counter / Decline> — with reasoning

## Conditions if approved
- Discount expires <date>
- Customer must agree to: <reference call, case study, etc.>
- Customer agrees this is single-instance (not precedent)
- Payment must close by <date>

Use scripts/deal_review_packet.py --deal deal.yaml to generate this packet from a deal spec.


Velocity analysis

A slow deal desk strangles sales. Measure and tune.

Key metrics
MetricHealthyWarning
Median time-to-decision< 1 business day> 3 days
90th percentile time-to-decision< 3 business days> 7 days
% of deals waiting on a single approver > 24h< 10%> 30%
Deals stuck > 7 days0> 5
Sales rep satisfaction with deal desk (NPS)> 50< 0
% approved (high approval rate may mean threshold too low)60-80%> 95% or < 40%
Discount-on-discount: deals where customer negotiated up after deal-desk approval< 10%> 30%

Run scripts/deal_velocity_analyzer.py --deals deals.csv to compute these from a CRM export.

Common bottlenecks
BottleneckDiagnosisFix
Single approver bottleneck (one person on everything)Routing matrix concentrated authorityDelegate; add back-ups; raise thresholds
Legal review takes a weekLegal sees every dealStandard MSA + pre-approved clause library; Legal only on deviations
Engineering needed for SLA reviewCustom SLAs every timePublish standard SLA tiers; only deviations route to eng
Approval cycle back-and-forthPacket missing key infoUse the standard packet template; reject incomplete submissions
Long executive lagExec doesn't have context for every dealWeekly deal review meeting for batch decisions on smaller items
Sales submits incomplete packetsReps don't know what to includeIntake form that enforces required fields
No SLA enforcementDeals sit in queue with no urgencyPublish + report SLA; aging dashboard visible to leadership

See references/discount-and-concession-playbook.md for the discount/concession patterns: legitimate reasons for each concession type, how to evaluate, alternatives to discounting, and how to structure performance-based discounts.


Clarify First

Before generating the deal-desk artifact, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • Task type — standing up the desk (charter + matrix) vs reviewing one deal (packet) (determines which template you produce)
  • Deal specifics: ACV + requested deviation — discount %, term, payment, custom SLA/legal (sets the Standard-vs-Requested table and which approvers the matrix requires)
  • Approval authority structure — who can approve what (Rep→Manager→Director→VP→CRO/CFO/CEO + Legal) (drives the threshold matrix and routing)
  • Strategic value + risk — logo/reference value, credit/compliance risk (drives the packet's justification and recommendation)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

End-to-end workflows

Workflow: A rep submits a non-standard deal
  1. Rep submits via intake form: customer + ACV + requested deviation + justification
  2. Deal desk triages within 4h: assigns analyst, validates packet completeness, requests missing info
  3. Deal desk reviews within 1 business day: financial impact, strategic value, risk
  4. Deal desk recommends approve / counter / decline
  5. Route to approver(s) per matrix (auto via scripts/discount_authority_router.py)
  6. Approver decides within SLA
  7. If approved: packet signed off, conditions sent to rep with expiration
  8. If countered: deal desk works with rep on alternative structure
  9. If declined: clear reason + alternatives sent to rep + customer
Show full SKILL.md (587 more words)Show less
Workflow: Stand up a deal desk from scratch
  1. Draft charter with sales, finance, legal sign-off
  2. Build the approval matrix — interview key stakeholders, document existing tribal knowledge
  3. Design intake form — CRM-integrated or Slack-bot
  4. Hire / appoint deal desk lead + analyst(s)
  5. Train sales — what triggers deal desk, what info is needed, what to expect
  6. Soft launch — manual operation for 1 month; track metrics
  7. Iterate — refine thresholds, automate routing, publish SLAs
  8. Quarterly review — metrics, threshold adjustments, charter updates
Workflow: Audit deal-desk performance
  1. Export deals from CRM for the period (CSV with deal IDs, stages, approval timestamps, discounts)
  2. Run velocity analyzer — compute medians, percentiles, aging, approver bottlenecks
  3. Sample 10-20 deals for qualitative review (was the packet complete? were conditions met?)
  4. Identify patterns — are certain reps over-discounting? are certain customers getting MFN clauses inappropriately?
  5. Propose adjustments — to charter, thresholds, intake form, training
  6. Present to leadership with metrics + recommendations
Workflow: Quarterly threshold review

Thresholds drift. Quarterly:

  1. Pull discount distribution for the quarter
  2. Identify outliers — deals where discount % was anomalous for ACV / segment
  3. Compare approval rates by threshold — if 30%+ discount deals get approved 95%+ of the time, the threshold is too low
  4. Compare win rates by discount band — does deeper discount actually improve win rate, or does it just give up margin?
  5. Adjust thresholds based on data + market shift
  6. Publish new matrix with effective date; train sales

Anti-patterns

  • Deal desk as bottleneck. SLAs published but ignored; deals stack up; sales builds workarounds. Measure + enforce SLAs.
  • Deal desk that always says yes. Approval rate > 95% means thresholds are too low — you're rubber-stamping. Tighten or raise thresholds.
  • Deal desk that always says no. Approval rate < 40% means policy is too strict OR sales doesn't understand it. Investigate root cause.
  • No deal-desk policy. Every deal evaluated case-by-case. Inconsistent decisions; legal exposure; reps gaming the system.
  • Concentrated authority. One person approves everything → bottleneck + bus factor. Delegate.
  • Pricing strategy disguised as deal-desk policy. If 80% of deals need discounting, the published price is wrong. Fix pricing.
  • Discount creep. Each deal raises the bar for the next; eventually published price is irrelevant. Track + reset.
  • Concession with no quid pro quo. Customer asks for 20% discount; you give 20% discount. Always trade: 20% for case study, 20% for 3yr contract, etc.
  • No expiration on quotes. Customer can come back in 6 months and demand the same terms. Always time-box (typically 30-60 days).
  • Single-instance language never enforced. "This is a one-time exception" → next year the customer cites it as precedent.

Tooling outputs

ScriptInputOutput
scripts/deal_review_packet.pyDeal spec YAMLMarkdown deal-review packet with summary, financials, strategic value, risk, approver list, recommendation template
scripts/discount_authority_router.pyDeal spec YAML + approval matrix YAMLRequired approver(s), routing order, escalation path, SLA-aware ordering
scripts/deal_velocity_analyzer.pyCSV of deals from CRM exportMedian / p90 time-to-decision, aging dashboard, approver bottleneck identification, discount-on-discount analysis

All scripts: stdlib only, argparse CLI, JSON or markdown output.


References


  • business-growth/pricing-strategy — sets the prices that deal desk enforces deviations from
  • business-growth/revenue-operations — measures the pipeline; deal-desk metrics flow into RevOps dashboards
  • business-growth/contract-and-proposal-writer — drafts the final contract once deal desk approves
  • business-growth/channel-economics — channel deals have their own deal-desk patterns
  • business-growth/partnerships-architect — partner-mediated deals route through both deal desk + partnerships
  • business-growth/commercial-policy — the broader governance framework deal desk enforces
  • sales-success/sales-engineer — provides technical validation in packet
  • sales-success/sales-operations — owns CRM / forecast accuracy that deal desk feeds

© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references) in business-growth/deal-desk of borghei/Claude-Skills.

  • SKILL.md
  • references/approval-thresholds-and-routing.md
  • references/deal-desk-charter-and-process.md
  • references/discount-and-concession-playbook.md
  • scripts/deal_review_packet.py
  • scripts/deal_velocity_analyzer.py
  • scripts/discount_authority_router.py

Open the folder on GitHubat commit 4a698e8

Compare with similar skills

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Categories

Questions about Deal Desk

What does Deal Desk do?

Deal desk: reviews, approves, and structures non-standard sales deals. Deal Desk is an agent skill from borghei/Claude-Skills. Deal desk: reviews, approves, and structures non-standard sales deals.

When should I use Deal Desk?

Deal Desk fits situations like: standing up a deal desk; building approval-threshold matrices; designing deal-review packets; auditing deals for compliance.

How do I install Deal Desk in Claude Code?

Run `npx skills add borghei/Claude-Skills --skill deal-desk -a claude-code`. Or copy the skill folder (business-growth/deal-desk in borghei/Claude-Skills) into .claude/skills/deal-desk in your project. Claude Code loads it when a task matches its description.

How do I install Deal Desk in Codex?

Run `npx skills add borghei/Claude-Skills --skill deal-desk -a codex`. Or copy the skill folder (business-growth/deal-desk in borghei/Claude-Skills) into .agents/skills/deal-desk in your project. Codex loads it when a task matches its description.

Can I use Deal Desk in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill deal-desk -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deal-desk, .gemini/skills/deal-desk, .github/skills/deal-desk and .opencode/skills/deal-desk in your project.

What does Deal Desk need to run?

Going by SKILL.md and its folder, Deal Desk needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Deal Desk access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Deal Desk safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Deal Desk use?

Deal Desk is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deal Desk use?

About 4.6k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.

What are the alternatives to Deal Desk?

Skills that share tags, products or a category with Deal Desk: Cold Outbound Optimizer (ericosiu/ai-marketing-skills, 3.6k stars), Doc Coauthoring (aws-samples/sample-strands-agent-with-agentcore, 195 stars), Amazon Buy Box Monitor (browser-act/skills, 6.1k stars) and Review Analysis (liangdabiao/amazon-sorftime-research-MCP-skill, 959 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deal Desk?

borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 891 GitHub stars. The repository holds 354 skills in this directory. The repository was last updated on October 7, 2026.

Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.