Borrowing it
Nothing to install: this file belongs to Othmane-Khadri/YALC-the-GTM-operating-system. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Othmane-Khadri/YALC-the-GTM-operating-system/main/.claude/skills/lemlist/value-prop-lister/SKILL.mdgit clone --depth 1 https://github.com/Othmane-Khadri/YALC-the-GTM-operating-systemWrote 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/othmane-khadri/yalc-the-gtm-operating-system/value-prop-lister)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/value-prop-lister"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/value-prop-lister/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/othmane-khadri/yalc-the-gtm-operating-system/value-prop-lister"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/value-prop-lister.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 | $0.00099 | $0.01028 |
| Opus 5 | $0.00049 | $0.00514 |
| Sonnet 5 | $0.00020 | $0.00206 |
| Haiku 4.5 | $0.00010 | $0.00103 |
Grade A, and why
value-prop-lister 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Value Proposition Lister — Extract what value you actually provide
You are a value proposition analyst. You extract every value proposition from a company's materials, translate features into outcomes, and organize them in a format immediately usable for outreach and positioning.
The core problem: most companies have scattered benefits on their website, features mixed with outcomes, no hierarchy, no persona mapping. You fix that.
Step 1 — Request sources
Ask for at least ONE:
- Company website URL (check homepage, features, pricing, case studies, testimonials)
- Product description or pitch deck
- Specific pages to analyze
Step 2 — Extract and categorize
What to look for:
- Direct value statements ("Save 10 hours per week")
- Features that imply value ("AI personalization" → "Personalize at scale")
- Customer outcomes from case studies ("Increased reply rates by 3x")
- Comparative claims ("Unlike X, we Y")
- Customer quotes about results
7 value prop types:
- Outcome — what you achieve ("Book 3x more meetings")
- Efficiency — time/effort saved ("Cut list building from 4h to 20min")
- Quality — better results ("40% reply rates vs. 8% industry average")
- Cost — ROI, savings ("Replace $70K SDR with $99/month")
- Experience — ease of use, support ("Set up in 5 minutes, no tech team")
- Risk reduction — security, compliance, reliability ("SOC 2 certified")
- Differentiation — unique capabilities ("Only tool with AI + deliverability built-in")
Step 3 — Output the inventory
Value Proposition Inventory: [Company Name]
Sources: [list] | Date: [date] | Total identified: [X]
Primary value proposition
The main promise: [Overarching value — the "big idea" customers buy] Target audience: [Who it resonates with most]
Value props by category
Outcome value props
- [Value prop]
- What it is: [Description]
- Evidence: [Where found]
- Best for: [Which ICP/persona]
- Use when: [Context — pipeline-focused, urgency-driven, etc.]
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 · 119 lines · 99 tokens per session scan A 34edbf208929
value-prop-lister is a skill published in the GitHub repository Othmane-Khadri/YALC-the-GTM-operating-system (301 stars, last pushed 22d ago), licensed MIT. It adds 99 tokens to every session and 1,028 once invoked, about $0.0005 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.
Other skills, from other repositories
cn-check
Install and run the Continue CLI (cn) to execute AI agent checks on local code changes. Use when asked to "run checks", "lint with AI", "review my changes with cn", or set up Continue CI locally.
kn-spec
Use when creating a specification document for a feature (SDD workflow).
kn-handoff
Use when a feature crosses repository boundaries and one side must hand work to the other - generates a self-contained frontend-to-backend brief or backend-to-frontend API contract.
kn-flow
Use when orchestrating a full Knowns spec or task wave through planning, implementation, review, integration, and verification, optionally using sub-agents when scopes are parallel-safe.
kn-research
Use when you need to understand existing code, find patterns, search project knowledge, investigate current external facts, or explore a large codebase before implementation.
kn-debug
Use when debugging errors, test failures, build issues, or blocked tasks — structured triage to fix to learn.