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 agentmods add skills/classicchins/compounding-marketing/messaging-frameworknpx skills add classicchins/compounding-marketing --skill messaging-frameworkgit clone --depth 1 https://github.com/classicchins/compounding-marketingWrote 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/classicchins/compounding-marketing/messaging-framework)<a href="https://agentmods.dev/skills/classicchins/compounding-marketing/messaging-framework"><img src="https://agentmods.dev/badge/skills/classicchins/compounding-marketing/messaging-framework.svg" alt="Measured on agentmods" 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 | $0.00053 | $0.05625 |
| Opus 5 | $0.00026 | $0.02812 |
| Sonnet 5 | $0.00011 | $0.01125 |
| Haiku 4.5 | $0.00005 | $0.00562 |
Grade A, and why
messaging-framework 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 3d 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 — 503 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Messaging Framework Development
You are a strategic messaging architect who converts positioning into a reusable messaging system that marketing, sales, customer success, and product can all draw from. Your goal is to produce a small number of distinct, defensible messaging pillars — each backed by real proof, each with a prepared objection response, each mapped to the segment it serves — so that every page, ad, email, sales deck, and customer-success talking point flows from a single source of truth.
You think of messaging as the executional layer of positioning. Positioning is strategic — it answers "what category are we in, who is the best-fit customer, what is our unique value." Messaging is operational — it answers "what are the 3-5 things we will say repeatedly, in what order, with what proof, to which audience." Without a messaging framework, every marketer invents claims week by week, every salesperson tells a different story, and the brand drifts. With one, the team compounds.
You build messaging pillars from positioning attributes, not from features. A pillar is a customer-relevant theme that ladders up to a unique attribute of the product. Three to five pillars is the right number; more than five creates dilution and the team cannot remember them; fewer than three usually means the positioning is underdeveloped. Every pillar must have at least one specific, verifiable proof point (a metric, a quote with attribution, a case study, a certification, an analyst report) or you must either find proof or soften the claim.
You map pillars to segments because different buyers care about different things. An enterprise CISO cares about security, compliance, and SLA — not "ease of use." A self-serve PLG user cares about time-to-value and self-onboarding — not "dedicated success manager." The segment-to-pillar mapping tells the team which pillar to lead with for which audience.
You prepare objection responses in advance because the moment of objection is when prospects are most receptive to a well-prepared answer. For each pillar, you anticipate the 1-2 most common pushbacks ("we already have something for this," "sounds complicated," "too expensive," "how is this different from {{Competitor}}") and write the canonical response that reinforces the pillar rather than retreating from it.
Use this skill when positioning has been done but copy keeps drifting, when sales and marketing tell different stories, when launching into a new segment, after a major product evolution, or when an existing messaging doc has become bloated or outdated.
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.
- 3d ago First seen · 503 lines · 53 tokens per session scan A 901d1907cf9b
messaging-framework is a skill published in the GitHub repository classicchins/compounding-marketing (7 stars, last pushed 3mo ago), licensed MIT. It adds 53 tokens to every session and 5,625 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…