cm-compound

cm-compound is a command for Claude Code from classicchins/compounding-marketing. It costs 0 tokens per session (1,928 once invoked), scanned A, original, MIT.

A command that saves one evidence-based learning entry in a category file for future marketing work.

In plain words
What is it for?
Use it after a meaningful result to record the required learning fields, validate them against the shared schema, and append the entry to the appropriate file.
Why use it?
It keeps useful findings from campaigns, experiments, research, and surprises from being forgotten or replaced by unsupported opinions.

Command for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the compounding-marketing plugin — 39 skills, 16 commands shipped together

Good fit Use it after a meaningful result to record the required learning fields, validate them against the shared schema, and append the entry to the appropriate file.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/classicchins/compounding-marketing/cm-compound
Install

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.

Clone the repo
git clone --depth 1 https://github.com/classicchins/compounding-marketing

Made for: Claude Code.

Or install compounding-marketing, the plugin that ships this one along with the rest of its 39 skills, 16 commands.

Wrote 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.

agentmods badge for cm-compound

README.md
[![agentmods](https://agentmods.dev/badge/commands/classicchins/compounding-marketing/cm-compound/github.svg)](https://agentmods.dev/commands/classicchins/compounding-marketing/cm-compound)
Your own site
<a href="https://agentmods.dev/commands/classicchins/compounding-marketing/cm-compound"><img src="https://agentmods.dev/badge/commands/classicchins/compounding-marketing/cm-compound/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.

agentmods 80×15 button for cm-compound

Your own site · 80×15
<a href="https://agentmods.dev/commands/classicchins/compounding-marketing/cm-compound"><img src="https://agentmods.dev/badge/commands/classicchins/compounding-marketing/cm-compound.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,928 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.01928
Opus 5.5 $0.00000 $0.00771
Sonnet 5.5 $0.00000 $0.00386
Haiku 4.5 $0.00000 $0.00193

Measured 2mo ago against content hash 6efb0f41365f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

cm-compound 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 2mo 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.

commands/cm-compound.md · 154 lines

How it starts

The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/cm-compound — Capture a Learning

Append a single, schema-valid learning entry to .agents/learnings/<category>.md so future marketing work in this category starts from prior evidence, not a blank page.

This command is the write side of the compounding loop. The read side is the Prior Learnings Consulted section that wired skills run before producing output. The schema both sides obey is defined in skills/_LEARNINGS_SCHEMA.md — that file is the source of truth; this command must conform to it.

When to use

Run /cm-compound immediately after:

  • A campaign, page, launch, or experiment produced a clear result (good or bad).
  • An A/B test reached significance — whether it won or lost. Losing tests are often higher-signal than winning ones.
  • A round of customer research surfaced a non-obvious pattern.
  • A surprise occurred — something behaved opposite to expectation.

Do not run it for routine deliveries that taught nothing new, or for opinions without evidence. The bar is captured evidence, not vibes.

What it does

  1. Walks the user through the six required fields for a learning entry.
  2. Validates each field against the schema (_LEARNINGS_SCHEMA.md §"Required entry structure").
  3. Resolves the correct category file (.agents/learnings/<category>.md), creating it with valid frontmatter if it does not exist.
  4. Writes the new entry above existing entries (reverse-chronological).
  5. Updates frontmatter (last_updated, entries_count).
  6. Reports what was captured and which wired skills will now consume it.

Process

Step 1: Confirm there is a real learning to capture

Ask the user:

  • What worked, what didn't, what surprised you, what would you do differently?

If the answer is generic ("the campaign went well") or non-falsifiable ("the brand feels stronger"), stop. Ask for the specific outcome, number, or quote that makes this a learning rather than an impression. A learning without evidence is noise.

Read the full file on GitHub · 154 lines

Changes

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.

  1. 2mo ago First seen · 154 lines · 0 tokens per session scan A 6efb0f41365f

Subscribe to this mod's changes

cm-compound is a command published in the GitHub repository classicchins/compounding-marketing (8 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,928 tokens. 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.

Related

cm-compound compared with commands from other repositories

cm-compound next to 6 commands from other repositories
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cm-compoundclassicchins/compounding-marketingCommand85mo agoMITA0Claude Code
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