Omh Sales Pipeline Review logo

Omh Sales Pipeline Review

CommunityPopular
rlaope
omh-sales-pipeline-review

[omh] CRM pipeline or sales forecast to review: turn a supplied CRM export or pipeline snapshot into an evidence-bound pipeline health, forecast, and follow-up review. Aliases: pipeline-review, forecast-review, deal-review. Use when the user says: sales-pipeline-review, sales pipeline review, pipeline review, pipeline health, pipeline coverage, deal review, deal health, sales forecast review.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-sales-pipeline-review
Stars
3.6K
Forks
269
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Sales Pipeline Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-sales-pipeline-review
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-sales-pipeline-review .claude/skills/omh-sales-pipeline-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Sales Pipeline Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Omh Sales Pipeline Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Omh Sales Pipeline Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Sales Pipeline Review

This is an OMH sales-pipeline-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

sales-pipeline-review prepares evidence-bounded portfolio pipeline, forecast, and follow-up reviews from supplied CRM snapshots without replacing a CRM, mutating records, or claiming revenue.

Do Not Use When

  • The request is single-account discovery, qualification, buyer hypotheses, outreach drafting, or one opportunity's next step; use omh-sales-development.
  • The user wants a weekly status, release-risk, or operating review with no sales stages, forecast categories, or deal records; use omh-ops-review.
  • The user needs authoritative revenue, bookings, budget-variance, or close reporting rather than a pipeline scenario; use omh-finance-analysis.
  • The user wants generic exploration or calculation on a supplied table with no stage, forecast, or deal-health semantics; use omh-data-analysis.
  • The supplied material is qualitative customer feedback, call notes, or survey text rather than opportunity records; use omh-feedback-triage.
  • The user asks to update Salesforce or HubSpot, store or sync CRM data, send alerts or outreach, or change an opportunity; use omh-apps with explicit object, field, and authority.

Examples

Good example:

  • Prompt: Here is this week's pipeline export as of Monday 09:00; review deal health, slipped close dates, and whether the commit forecast holds up against last quarter's calls.
  • Expected behavior: Validate as-of time, currency, amount and stage semantics first, then prepare health, forecast-state, and calibration findings with owned follow-ups and proposed CRM corrections.
  • Why: The request is portfolio-level pipeline and forecast review over a supplied snapshot with a stated as-of time.

Bad example:

  • Prompt: Write discovery questions for the Northwind opportunity and draft the follow-up email.
  • Expected behavior: Route to sales-development, not sales-pipeline-review.
  • Why: A single account's discovery, qualification, and outreach draft has no portfolio, aging, or forecast-calibration objective.

Completion Checklist

  • The scope disposition is recorded before any figure: HOLD names the blocking gap, otherwise freshness, currency basis, amount and stage semantics, duplicates, and owners are confirmed from supplied data.
  • Health, forecast, and annex outputs cite supplied record references, keep stage, seller forecast, scenario, and observed commitment separate, and mark calibration or an annex unavailable or unsupported instead of filling it.
  • The handoff lists owner, due date, exit criterion, and evidence per follow-up, keeps every CRM correction proposed with an approval state, and reports mutation, storage, sync, alerts, and outreach as not_observed unless a connector result was observed.

Recovery Notes

  • If the snapshot fails scope validation, return HOLD naming the exact missing definition, conversion basis, owner, or duplicate set and ask for it; do not rank or total partial data.
  • If prior forecasts or outcomes are absent, keep calibration unavailable; if an annex has no supporting evidence, emit it as unsupported with the gap named rather than omitting it.
  • If a connector is unavailable, keep every CRM correction proposed and every alert or message unsent, and name the connector boundary as the next observable step.

When to Use

Use when a sales leader or business owner supplies a bounded CRM export or pipeline snapshot and needs recurring portfolio review: evidence scope and freshness, stage and forecast definitions, movement and aging, stale or slipped deals, exit-criteria gaps, next-step quality, concentration, forecast scenarios and prior-forecast calibration, optional won/lost or renewal-risk learning, and an owned follow-up handoff.

Strong routing signals: `sales-pipeline-review`, `sales pipeline review`, `pipeline review`, `pipeline health`, `pipeline coverage`, `deal review`, `deal health`, `sales forecast review`, `forecast call`, `forecast calibration`, `seller forecast`, `stale deals`, `slipped deals`, `renewal risk review`, `win loss review`, `파이프라인 리뷰`, `영업 예측 보정`, `딜 리뷰`

Catalog Metadata

Category: operations Phase: sales-pipeline-review Quality tier: decision-gated Reasoning demand: standard

Quality bar:

  • Separate stage, seller forecast, model-derived scenario, and observed buyer commitment in every forecast statement.
  • Cite the supplied record reference behind every exception, slip, stall, concentration, and proposed correction.
  • Emit calibration only from matched prior snapshots and observed outcomes; otherwise state that it is unavailable.

Required inputs:

  • pipeline snapshot
  • as-of time and review horizon
  • currency, amount, stage, and forecast definitions
  • prior forecast and actuals
  • decision owner

Expert clarification questions:

  • pipeline snapshot
    • English: Which CRM export or pipeline snapshot is supplied, with its opaque source reference, included motions, owners, cohort, record count, and known data-quality gaps such as duplicates or missing owners?
    • Korean: 어떤 CRM 내보내기 파일 또는 파이프라인 스냅샷이 제공되며, 출처 참조, 포함된 영업 방식, 담당자, 코호트, 레코드 수, 중복이나 담당자 누락 같은 알려진 데이터 품질 결함은 무엇인가요?

Expected outputs:

  • sales_pipeline_scope/v1
  • sales_pipeline_health/v1
  • sales_forecast_assessment/v1
  • sales_outcome_learning_annex/v1
  • sales_renewal_risk_annex/v1
  • sales_pipeline_handoff/v1

Artifact expectations:

  • prepared sales pipeline review brief when a wrapper captures it
  • durable artifacts hold bounded aggregates, opaque source references, and only the account identifiers an approved handoff needs; raw export rows and message content are not persisted by default

Safety rules:

  • Treat a missing or stale as-of time, undefined stage or forecast semantics, mixed currencies without an observed conversion basis, unknown amount meaning, duplicate opportunities, or missing owners as HOLD, never as an input to a calculation.
  • Do not claim CRM storage, sync, mutation, dashboards, alerts, outreach, booked revenue, or seller or buyer commitments; a proposed correction is not a change and a scenario is not a promise.
  • Consume the organization's supplied stage, category, probability, amount, and close-date definitions; never impose a vendor schema or a default probability table.
  • Persist bounded aggregates and opaque source references only; raw CRM exports and message content stay out of durable artifacts unless the user explicitly approves a scoped exception.

Procedure: load references/procedure.md.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Record actual tool results, or not_observed / not_available, in the record; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Reply in the user's own words and the host's own voice: the host's persona owns reply language, tone, speech level, and sentence endings, progress updates included (where it sets no language, use the one the user wrote in), and OMH shapes structure and content only; OMH's record terms (surface, lane, wrapper, handoff, evidence boundary, not_observed) stay in records and tool calls, never in the sentence the user reads unless they ask about one; and when a real unresolved user decision or a useful briefing discussion remains, offer one specific, content-linked question. Completed work needs no question; omit unrelated non-action footers and generic offers to continue. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Omh Sales Pipeline Review AI skill do?

[omh] CRM pipeline or sales forecast to review: turn a supplied CRM export or pipeline snapshot into an evidence-bound pipeline health, forecast, and follow-up review. Aliases: pipeline-review, forecast-review, deal-review. Use when the user says: sales-pipeline-review, sales pipeline review, pipeline review, pipeline health, pipeline coverage, deal review, deal health, sales forecast review.

Why use Omh Sales Pipeline Review on TypingMind?

Because you install it once and use it with any model. Omh Sales Pipeline Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Omh Sales Pipeline Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-sales-pipeline-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Omh Sales Pipeline Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Omh Sales Pipeline Review?

As many as you like. As long as a model supports skills, you can use Omh Sales Pipeline Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Omh Sales Pipeline Review AI skill free?

Yes. It is published on GitHub by rlaope under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇