Agent skill

Revenue Operations

by cbrock84 in cbrock84/headcount

Runs the mechanics of the revenue engine — lead lifecycle definitions, routing, CRM hygiene, forecasting process, pipeline reporting, and the marketing-to-sales handoff.

MITAuto-check passedData & Analytics

Install Revenue Operations

skills CLI
$ npx skills add cbrock84/headcount --skill revenue-operations -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install cbrock84/headcount revenue-operations --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/cbrock84/headcount.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/revenue/skills/revenue-operations .claude/skills/revenue-operations && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
revenue-operations
GitHub stars
2k
Token cost
~1.2k tokens
SKILL.md length
698 words
Files
2 (incl. references)
Skills in repo
178
Repo updated
First seen
Licence
MIT

At a glance

Runs the mechanics of the revenue engine — lead lifecycle definitions, routing, CRM hygiene, forecasting process, pipeline reporting, and the marketing-to-sales handoff.

  • Tasks that involve Forecasting and time series
  • SKILL.md covers Definitions before dashboards, The handoff, Lead scoring and Forecasting, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Revenue Operations is an agent skill from cbrock84/headcount. Runs the mechanics of the revenue engine — lead lifecycle definitions, routing, CRM hygiene, forecasting process, pipeline reporting, and the marketing-to-sales handoff. Use this to fix a broken handoff, define lifecycle stages, improve forecast accuracy, clean up CRM data, design territory or routing rules, or diagnose why pipeline numbers are not trusted.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/sources.md`).

It sits in Data & Analytics, covering Forecasting and time series. The repository describes itself as: An agent organization structured as a company — 15+ departments, 125+ skills, each independently installable, citing the standards and regulators that settle the question. Runs… The licence is MIT.

When your agent uses it

  • Tasks that involve Forecasting and time series

Example prompts

  • “/revenue-operations”

What it can do on your machine

Read from SKILL.md and the folder at commit 98d1c17. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Revenue Operations loads about 1.2k tokens when it runs, and up to ~2.2k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 698 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from cbrock84/headcount at commit 98d1c17, republished under its MIT licence (© cbrock84). 698 words, ~1,211 tokens.

Download SKILL.mdSave it as .claude/skills/revenue-operations/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
revenue-operations
description
Runs the mechanics of the revenue engine — lead lifecycle definitions, routing, CRM hygiene, forecasting process, pipeline reporting, and the marketing-to-sales handoff. Use this to fix a broken handoff, define lifecycle stages, improve forecast accuracy, clean up CRM data, design territory or routing rules, or diagnose why pipeline numbers are not trusted.

Revenue operations

Definitions before dashboards

Most revenue reporting arguments are definitional. Write down and get agreement on, in one place:

  • What each lifecycle stage means and the observable event that moves a record into it.
  • What makes a lead qualified — and by whose judgment.
  • When an opportunity is created, and what evidence is required.
  • What each pipeline stage requires to be entered, stated as a buyer action rather than a seller feeling. "Prospect has confirmed budget" is observable; "showing strong interest" is not.
  • What closed-lost means versus stalled, and when a stalled deal exits the pipeline automatically.

Without these, every number is negotiable and forecasting is a genre of fiction.

The handoff

Where most revenue leaks. Specify: the exact criteria for passing a lead, the SLA for first contact, what context transfers with it, and the route back when it is rejected — including the reason, recorded.

A rejection loop with no recorded reason means marketing keeps sending the same unqualified leads, and both sides believe the other is the problem.

Lead scoring

Scoring exists to route attention, not to produce a number. If sellers do not change what they work on because of the score, it is decoration.

Score on two independent dimensions and keep them separate:

  • Fit — do they look like a customer? Company size, industry, geography, role and seniority, technology in use. Static, knowable before any engagement.
  • Intent — are they acting like a buyer now? Pricing page visits, repeat sessions, demo request, content depth, response to outreach. Dynamic, and it decays.

Collapsing the two into one score is the standard mistake: a perfect-fit account with no activity and a poor-fit account browsing aggressively land on the same number and get treated identically, which is wrong in both directions.

Build the model from closed-won and closed-lost history, not intuition. Look at what actually separated the two, and be prepared for the finding that a favored attribute has no predictive value.

Decay intent scores over time and recalibrate on a schedule. A scoring model built once and never revisited drifts as the market and the product change, and nobody notices because it keeps producing numbers.

Forecasting

Forecast accuracy comes from process, not optimism.

  • Stage-based probabilities derived from your own historical conversion, recalculated periodically — not from defaults.
  • Commit, best case, and pipeline reported separately.
  • Every forecasted deal has a date and a next step. A deal with neither is not in the forecast.
  • Track forecast accuracy itself, by rep. It is the only way to know whose numbers to trust and it improves quickly once measured.
Show full SKILL.md (273 more words)Show less

CRM hygiene

Data quality decays continuously. Required fields at stage gates, validation at entry, scheduled duplicate merges, and automatic aging of stale records. Rely on discipline alone and the data will be unusable within two quarters.

Never require a field whose value is not used in a decision. Every unnecessary field trains sellers to enter garbage in all of them.

Sources

references/sources.md in this skill lists the outside authorities that settle the questions here — what each one is authoritative for, and what you may do with it. Check them before answering on anything they cover, and cite what you used. Most are free to read and not free to reproduce; the use note on each is binding.

Tooling

CRM: Salesforce, HubSpot, Pipedrive, Zoho CRM, and similar. The CRM is the record for pipeline. Anything that disagrees with it is a report, not a number.

Enrichment and routing: Clay, ZoomInfo, Apollo, Chili Piper, and similar.

Forecasting and conversation data: Clari, Gong, and similar — useful once you have enough deals for a pattern to mean anything.

Never

  • Change a definition without restating history on the new one. A metric that moved because you redefined it is not a result.
  • Report a forecast number you cannot trace back to named deals.
  • Let two systems each keep their own version of the same field. Pick the source of truth and make the other read from it.
  • Score leads with a model you cannot explain to the reps who have to work them.

Return contract

State the definitional gaps found, the process change proposed, what it costs sellers in time, and the metric that will show it worked.

© cbrock84, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in plugins/revenue/skills/revenue-operations of cbrock84/headcount.

  • SKILL.md
  • references/sources.md

Open the folder on GitHubat commit 98d1c17

Compare with similar skills

Revenue Operations next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Revenue Operations compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Revenue Operations this skillcbrock84/headcount2k—~1.2kAutomated safety check: PassMIT
Pp Coppermvanhorn/printing-press-library2.1k—~4.6kAutomated safety check: NotesApache-2.0
ForecastTheCraigHewitt/skills159—~5.2kAutomated safety check: PassMIT
Shopify Inventory Managementnexscope-ai/eCommerce-Skills1.1k—~480Automated safety check: PassMIT
TimesFM Forecastinggoogle-research/timesfm34k—~4.7kAutomated safety check: PassApache-2.0
StatsmodelszLanqing/codex-claude-academic-skills4.7k15 repos~4.9kAutomated safety check: PassBSD-3-Clause

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Questions about Revenue Operations

What does Revenue Operations do?

Runs the mechanics of the revenue engine — lead lifecycle definitions, routing, CRM hygiene, forecasting process, pipeline reporting, and the marketing-to-sales handoff. Revenue Operations is an agent skill from cbrock84/headcount. Runs the mechanics of the revenue engine — lead lifecycle definitions, routing, CRM hygiene, forecasting process, pipeline reporting, and the marketing-to-sales handoff.

When should I use Revenue Operations?

Revenue Operations fits situations like: tasks that involve Forecasting and time series.

How do I install Revenue Operations in Claude Code?

Run `npx skills add cbrock84/headcount --skill revenue-operations -a claude-code`. Or copy the skill folder (plugins/revenue/skills/revenue-operations in cbrock84/headcount) into .claude/skills/revenue-operations in your project. Claude Code loads it when a task matches its description.

How do I install Revenue Operations in Codex?

Run `npx skills add cbrock84/headcount --skill revenue-operations -a codex`. Or copy the skill folder (plugins/revenue/skills/revenue-operations in cbrock84/headcount) into .agents/skills/revenue-operations in your project. Codex loads it when a task matches its description.

Can I use Revenue Operations in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add cbrock84/headcount --skill revenue-operations -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/revenue-operations, .gemini/skills/revenue-operations, .github/skills/revenue-operations and .opencode/skills/revenue-operations in your project.

What does Revenue Operations need to run?

SKILL.md names no scripts, command-line tools or credentials: Revenue Operations is instructions for the agent only.

Does Revenue Operations access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Revenue Operations safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Revenue Operations use?

Revenue Operations is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Revenue Operations use?

About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 946 tokens, read only when the agent opens those files.

What are the alternatives to Revenue Operations?

Skills that share tags, products or a category with Revenue Operations: Pp Copper (mvanhorn/printing-press-library, 2.1k stars), Forecast (TheCraigHewitt/skills, 159 stars), Shopify Inventory Management (nexscope-ai/eCommerce-Skills, 1.1k stars) and TimesFM Forecasting (google-research/timesfm, 34k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Revenue Operations?

cbrock84 (a GitHub user) maintains it in cbrock84/headcount, which has 2,022 GitHub stars. The repository holds 178 skills in this directory. The repository was last updated on September 17, 2026.

Source: cbrock84/headcount on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.