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 stefanoskarakasis/Product-Marketing-Skills --skill value-prop-statementsgit clone --depth 1 https://github.com/stefanoskarakasis/Product-Marketing-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/stefanoskarakasis/product-marketing-skills/value-prop-statements)<a href="https://agentmods.dev/skills/stefanoskarakasis/product-marketing-skills/value-prop-statements"><img src="https://agentmods.dev/badge/skills/stefanoskarakasis/product-marketing-skills/value-prop-statements/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/stefanoskarakasis/product-marketing-skills/value-prop-statements"><img src="https://agentmods.dev/badge/skills/stefanoskarakasis/product-marketing-skills/value-prop-statements.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 | $0.00077 | $0.02366 |
| Opus 5.5 | $0.00031 | $0.00946 |
| Sonnet 5.5 | $0.00015 | $0.00473 |
| Haiku 4.5 | $0.00008 | $0.00237 |
Grade A, and why
value-prop-statements 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 8d 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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Value Prop Statements
How This Works
positioning-messaging builds the canonical positioning statement once,
through a full 6-phase Dunford process — that's expensive, deliberate,
and shouldn't be re-run every time a new segment or channel needs copy.
This skill is the fast sibling: it takes that already-set positioning
(or brain Section 3's alternatives-anchored gap statement) as fixed
input and rapidly produces multiple segment- or channel-specific value
proposition statements from it — the growth team's tool for cranking out
testable copy variants without re-litigating positioning each time.
Every statement it produces must trace back to the canonical positioning
— if a variant contradicts or drifts from it, that's a signal to escalate
back to positioning-messaging, not something this skill quietly
resolves on its own.
Step 0 — Load the canonical positioning statement (brain Section 1
product context, Section 3 alternatives/gap statement, or a
freshly-pasted positioning statement from positioning-messaging output)
and brain Section 2 (ICP) if present.
Step 1 — Confirm the source positioning. Ask for one if none exists. If what's given is only a vague product description, ask for the missing piece (the alternative or the differentiator). This skill fans out a positioning the user supplies; it doesn't invent one.
Step 2 — Identify target segments/channels for this batch (from
buyer-personas output if a recent session exists, brain ICP, or direct
ask).
Step 3 — Generate one value-prop statement per segment/channel: benefit, feature/capability that makes it possible, audience-specific language.
Step 4 — Trace-check every statement back to the canonical positioning — flag any that drift or contradict it rather than silently delivering them.
Step 5 — Learning Close: log the session to /context/skill-sessions.md.
Trigger
- When: You already have a set positioning statement and need it translated into segment-specific, channel-specific, or persona-specific value-prop copy for marketing, sales, or onboarding.
What ships with it
1 file 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.
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.
- 8d ago Changed · -2 lines · -9 tokens per session 453c54629fdc
- 16d ago Changed 87c3e0186474
- 17d ago Changed · -4 lines d65ba05bd85a
- 1mo ago Changed · +2 lines 2863f7a5acec
- 1mo ago First seen · 248 lines · 86 tokens per session scan A 1358c6464109
value-prop-statements is a skill published in the GitHub repository stefanoskarakasis/Product-Marketing-Skills (8 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 2,366 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-04.
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