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 cass-2003/local-workflow-skill --skill commercial-policygit clone --depth 1 https://github.com/cass-2003/local-workflow-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/cass-2003/local-workflow-skill/commercial-policy)<a href="https://agentmods.dev/skills/cass-2003/local-workflow-skill/commercial-policy"><img src="https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/commercial-policy/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/cass-2003/local-workflow-skill/commercial-policy"><img src="https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/commercial-policy.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 | $0.00142 | $0.03244 |
| Opus 5 | $0.00071 | $0.01622 |
| Sonnet 5 | $0.00028 | $0.00649 |
| Haiku 4.5 | $0.00014 | $0.00324 |
Grade A, and why
commercial-policy 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- commercial-policy — 95% identical, 7 lines differ
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
commercial-policy
Purpose
Design the rules of engagement that govern discounting off list price — the artifact that Deal Desk and AEs operate under. Three deterministic tools:
discount_matrix_builder.py— builds a 4-dimensional matrix (ARR band × term length × payment terms × strategic value tier), each cell carrying an approved discount band backed by current win-rate + NRR data, plus an approver tier (AE / Manager / Director / VP / CFO).exception_router.py— when an asks-for-discount lands outside the matrix, routes it through the named approver chain, attaches required compensating commitments (multi-year prepay + named expansion path + reference commitment + MSA tightening), produces machine-readable audit-trail metadata, and flags precedent risk if 3+ similar exceptions have landed in the trailing quarter.policy_linter.py— lints the matrix for governance defects: approver inversion, band inversion, margin-floor violation, coverage gaps, cliff edges, undefined strategic tiers, inconsistent margin floors, thin data backing.
The output is the policy itself (matrix + exception flow + lint report), not a per-deal application of it.
When to use
- A new Head of Commercial or Head of Deal Desk is writing the company's first formal commercial policy
- The existing matrix is older than 6 months and discount drift is showing in margin reviews
- Reps are citing "Maria approved 28% on Acme last quarter" as precedent and you need to break the precedent loop
- Q-over-Q exception count is rising and you suspect the matrix bands are mispriced
- CFO has tightened the margin floor and the matrix needs to be rebuilt against the new constraint
- A board / exec is asking "why do we discount this much?" and you need a data-backed defensible policy
Do NOT use this skill to:
- Approve a specific deal — that's
commercial/skills/deal-desk - Set the pricing model + list price — that's
commercial/skills/pricing-strategist - Author a proposal / SOW / MSA prose — that's
business-growth/contract-and-proposal-writer - Make the strategic "when do we hire a VP Sales" call — that's
c-level-advisor/cro-advisor
What ships with it
7 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.
- assets/policy_design_template.md 6.6 KB
- references/discount_governance_canon.md 8.1 KB
- references/policy_anti_patterns.md 11 KB
- references/policy_design_canon.md 7.4 KB
- scripts/discount_matrix_builder.py 14 KB runs code
- scripts/exception_router.py 10 KB runs code
- scripts/policy_linter.py 17 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.
- 6d ago First seen · 148 lines · 142 tokens per session scan A 3300e2b8ac28
commercial-policy is a skill published in the GitHub repository cass-2003/local-workflow-skill (12 stars, last pushed 2mo ago), licensed MIT. It adds 142 tokens to every session and 3,244 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.
Other skills, from other repositories
audit-support
Support SOX 404 compliance with control testing methodology, sample selection, and documentation standards. Use when generating testing workpapers, selecting audit samples, classifying control deficiencies, or preparing for internal or external audits.
kyc-doc-parse
Parse an investor or client onboarding packet into structured KYC fields — identity, ownership, control, source of funds, and document inventory. Use as the first step of KYC screening; output feeds the rules engine.
fiscaliste
Fiscaliste IA pour la fiscalité personnelle des particuliers français : optimisation et déclaration de l'impôt sur le revenu, IFI, revenus du capital, revenus fonciers, equity salarial, crypto-actifs et PER. Couvre le calcul de l'IR (barème, quotient familial, décote, PAS, CEHR, revenus exceptionnels), la déclaration…
regulatory-analysis
Analyzes documents and processes against FINRA, SEC, Federal Reserve, and CFPB regulatory frameworks. Identifies compliance gaps, classifies findings by severity, and recommends remediation. Use when performing compliance audits, regulatory reviews, gap analyses, or verifying policy adherence to financial regulations.
subcontractor-payment-tracker
Track subcontractor payments, lien waivers, and compliance. Manage payment schedules and documentation.
tax-optimizer
Tax optimization strategies including tax-loss harvesting, wash sale rule navigation, asset location optimization, Roth conversion analysis, and charitable giving strategies.