Signal To Campaign logo

Signal To Campaign

Organization
swan-gtm
signal-to-campaign

Use this skill when a team has an offer, target market, or observed account event and needs to turn it into a launch-ready signal-led campaign. Produces a signal thesis, evidence-backed account cohort, buyer map, channel-ready drafts, and a QA gate. Trigger on “build a campaign around this signal,” “find companies showing this problem,” “who should we contact now,” or “turn this market event into pipeline.”

Overview

Publisherswan-gtm
Repositorygtm-skills
Skill namesignal-to-campaign
Stars
216
Forks
103
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by swan-gtm on GitHub. Read the source before you install it.

Installation

Install the Signal To Campaign 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/swan-gtm/gtm-skills/tree/main/skills/bojan-berisavljevic/signal-to-campaign
  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/swan-gtm/gtm-skills.git /tmp/gtm-skills
mkdir -p .claude/skills
cp -r /tmp/gtm-skills/skills/bojan-berisavljevic/signal-to-campaign .claude/skills/signal-to-campaign
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Signal To Campaign 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 Signal To Campaign 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 Signal To Campaign 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.

Use this play before enriching contacts or writing campaign copy. It turns an observable market event into a small, defensible campaign package staged for human approval.

Prove the signal carries pain

Translate the offer into the operational problem it solves. Generate several candidate signals, then score each on:

  • Specificity: does it point to this problem or merely describe the company?
  • Observability: can every account be tied to evidence another operator can inspect?
  • Timing: does it explain why action makes sense now?
  • Ownership: is there a recognisable buyer who owns the implied problem?
  • Offer fit: does the offer resolve the situation without a logical leap?
  • Noise: what common cases would create a false positive?

Select one primary signal and one backup. A visible event is not automatically a buying signal. Funding, generic hiring, technology use, and broad growth become useful only when combined with current evidence of the relevant workflow, constraint, or complexity.

Build the evidence layer

Create an account record for every candidate with:

  • normalised company identity and domain;
  • source and exact evidence excerpt;
  • observation date or verified current status;
  • the operational implication;
  • the likely pain owner;
  • a false-positive note;
  • a decision: keep, investigate, or remove.

Reject accounts that cannot survive the sentence: “This company belongs because the evidence shows ___, which likely creates ___ for ___.” Do not enrich people yet. Replace weak rows instead of padding the cohort to hit a volume target.

Map buyers after the account passes

For each kept account, identify the role that owns the affected process, the role that feels the consequence, and any likely blocker. Titles are clues, not proof of ownership. Map one to three contacts per account, deduplicate against existing relationships, and record why each person belongs in the motion.

Enrich contact details only after the account and persona gates pass. Preserve strong accounts with missing contact data in a separate research queue rather than weakening the campaign.

Turn evidence into a campaign

Write the thesis in one sentence:

Companies showing [observable situation] are likely dealing with [operational consequence]; [offer] helps them reach [credible outcome].

Turn raw evidence into a short human-readable context line. React to the business situation; do not exhibit scraped facts or announce surveillance. Draft one core message per persona, one proof point that matches the account’s scale, and one low-friction ask. Adapt that thesis to each channel instead of copying the same message everywhere.

Before launch, produce:

  1. the signal thesis and false-positive definition;
  2. the kept account cohort with evidence;
  3. the buyer map and inclusion reasoning;
  4. staged channel drafts with required variables;
  5. a QA report and a small learning cohort.

What good looks like

A strong campaign can defend every account, every contact, and every sentence. The signal is current and pain-bearing, not merely easy to collect. Company concentration is controlled, evidence is traceable, variables render naturally, and the first cohort is small enough to learn from replies before scaling.

The expert tell is resistance: actively look for evidence that the signal does not imply pain. Mediocre work celebrates a data match, enriches hundreds of contacts, then writes “personalisation” from one keyword. Strong work removes false positives before spending enrichment effort and can explain what a positive reply would validate about the thesis.

Rules

  • MUST retain source evidence and a reason for inclusion for every account.
  • MUST define false positives before building the full cohort.
  • MUST separate fit, signal strength, and contactability; one cannot substitute for another.
  • MUST stage outreach for human approval and define the reply-learning goal.
  • NEVER invent urgency, pain, observations, proof, or customer outcomes.
  • NEVER enrich at scale before the account-level gate passes.
  • NEVER launch just to satisfy a lead-count target.

Frequently asked questions

What does the Signal To Campaign AI skill do?

Use this skill when a team has an offer, target market, or observed account event and needs to turn it into a launch-ready signal-led campaign. Produces a signal thesis, evidence-backed account cohort, buyer map, channel-ready drafts, and a QA gate. Trigger on “build a campaign around this signal,” “find companies showing this problem,” “who should we contact now,” or “turn this market event into pipeline.”

Why use Signal To Campaign on TypingMind?

Because you install it once and use it with any model. Signal To Campaign 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 Signal To Campaign in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/swan-gtm/gtm-skills/tree/main/skills/bojan-berisavljevic/signal-to-campaign. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Signal To Campaign?

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 Signal To Campaign?

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

Is the Signal To Campaign AI skill free?

Yes. It is published on GitHub by swan-gtm 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.

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