Agent skill

Hiring Signal Outreach

by gooseworks-ai in gooseworks-ai/goose-skills

End-to-end hiring signal composite. An agent skill from gooseworks-ai/goose-skills.

MITAuto-check passedBusiness, Finance & HR

Install Hiring Signal Outreach

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill hiring-signal-outreach -a claude-code

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

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills hiring-signal-outreach --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/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/outreach/composites/hiring-signal-outreach .claude/skills/hiring-signal-outreach && 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
hiring-signal-outreach
GitHub stars
1.2k
Used in
1 other repo
Token cost
~5.1k tokens
SKILL.md length
1,642 words
Files
2
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

End-to-end hiring signal composite. An agent skill from gooseworks-ai/goose-skills.

  • Works in 6 steps: Configuration (One-Time Setup) → Detect Hiring Signals → Qualify & Prioritize → …
  • Tasks that involve Recruiting and HR
  • SKILL.md covers When to Auto-Load, Step 0: Configuration…, Step 1: Detect Hiring Signals and Step 2: Qualify & Prioritize, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Hiring Signal Outreach is an agent skill from gooseworks-ai/goose-skills. End-to-end hiring signal composite. Takes any set of companies, detects job postings that your product augments or replaces, finds relevant people (the hiring manager, buyers, champions, users), and drafts personalized outreach using the job role as the hook. Tool-agnostic — works with any company source, job board, contact finder, and outreach platform.

Its SKILL.md is about 5.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).

It sits in Business, Finance & HR, covering Recruiting and HR and Cold outreach. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • Tasks that involve Recruiting and HR
  • Tasks that involve Cold outreach

Example prompts

  • “/hiring-signal-outreach”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Configuration (One-Time Setup)
  2. Detect Hiring Signals
  3. Qualify & Prioritize
  4. Find Relevant People
  5. Draft Personalized Emails
  6. Handoff to Outreach

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. 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

Hiring Signal Outreach loads about 5.1k tokens when it runs. Until then it costs about 95 tokens; SKILL.md has 1,642 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
~5.1k

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 gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,642 words, ~5,105 tokens.

Download SKILL.mdSave it as .claude/skills/hiring-signal-outreach/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
hiring-signal-outreach
description
End-to-end hiring signal composite. Takes any set of companies, detects job postings that your product augments or replaces, finds relevant people (the hiring manager, buyers, champions, users), and drafts personalized outreach using the job role as the hook. Tool-agnostic — works with any company source, job board, contact finder, and outreach platform.
version
1.0.0
tags
outreach
graph.provides
companies-with-hiring-signals, contact-list, personalized-email-sequences
graph.requires
company-list, your-company-context
graph.capabilities
web-search, job-search, contact-finding, email-drafting

Hiring Signal Outreach

Detects job postings at target companies where the role being hired for is one your product augments, replaces, or directly supports. Finds the right people to contact (not just the person being hired — the hiring manager, budget holder, and potential champions), then drafts personalized outreach using the job posting as the hook.

Why hiring signals work: When a company posts a job, they've already acknowledged the problem your product solves. They've budgeted for it (headcount is budget). They're actively evaluating how to solve it. Your email arrives at exactly the moment they're thinking about this problem — and you're offering a faster, cheaper, or complementary solution.

When to Auto-Load

Load this composite when:

  • User says "check if any of these companies are hiring for roles we replace", "job posting signals", "hiring signal outreach"
  • User has a list of companies and wants to find those hiring for relevant roles
  • An upstream workflow (TAM Pulse, company monitoring) triggers a hiring signal check

Step 0: Configuration (One-Time Setup)

On first run for a client/user, collect and store these preferences. Skip on subsequent runs.

Role Mapping (Critical — This Defines What Signals Matter)
QuestionPurposeStored As
What does your product do? (1-2 sentences)Match against job descriptionscompany_description
What job roles does your product replace?Strongest signal — they're hiring for what you automateroles_replaced
What job roles does your product augment?Good signal — your product makes this person more effectiveroles_augmented
What job roles buy your product?Contact finding — who holds the budgetbuyer_titles
What job roles champion your product?Contact finding — who feels the pain dailychampion_titles
What job roles use your product?Contact finding — who would operate ituser_titles
What keywords in a job description indicate relevance?Filters out false positivesjd_keywords

Example for an AI calling product:

roles_replaced: ["BDC Representative", "Call Center Agent", "Appointment Setter"]
roles_augmented: ["Sales Manager", "BDC Manager", "Service Advisor"]
buyer_titles: ["VP Sales", "Director of Operations", "General Manager", "COO"]
champion_titles: ["BDC Manager", "Sales Manager", "Fixed Ops Director"]
user_titles: ["BDC Rep", "Service Advisor", "Sales Consultant"]
jd_keywords: ["inbound calls", "outbound calls", "appointment setting", "customer follow-up"]
Signal Detection Config
QuestionOptionsStored As
How should we find job postings?LinkedIn Jobs / Indeed / Apollo / Google Jobs / Web searchjob_search_tool
How far back should we look?7 / 14 / 30 dayslookback_days
Contact Finding Config
QuestionOptionsStored As
How should we find contacts at these companies?Apollo / LinkedIn / Clearbit / Web searchcontact_tool
Outreach Config
QuestionOptionsStored As
Where do you want outreach sent?Smartlead / Instantly / Outreach.io / Lemlist / CSV exportoutreach_tool
Email or multi-channel?Email only / Email + LinkedInoutreach_channels
Your Company Context
QuestionPurposeStored As
What problem do you solve?Email hookpain_point
Name 2-3 proof points (customers, metrics, results)Email credibilityproof_points
What's the cost comparison vs. a full-time hire?ROI angle for outreachcost_comparison
How fast can you deploy vs. a new hire?Speed angledeployment_speed

Store config in: clients/<client-name>/config/signal-outreach.json or equivalent.


Step 1: Detect Hiring Signals

Purpose: For each company in the input list, find active job postings that match roles your product replaces or augments.

Input Contract
companies: [
  {
    name: string          # Required
    domain: string        # Required
    industry?: string     # Optional
    size?: string         # Optional
  }
]
roles_replaced: string[]          # From config
roles_augmented: string[]         # From config
jd_keywords: string[]             # From config
lookback_days: integer             # From config (default: 14)
Process

For each company (or in batches):

  1. Search for job postings using the configured job_search_tool:

    • LinkedIn Jobs: Search company_name + role_title for each role in roles_replaced and roles_augmented
    • Indeed: Same search pattern
    • Apollo: Company enrichment → job postings
    • Google Jobs: site:linkedin.com/jobs OR site:indeed.com "{company}" "{role}"
    • Web search: "{company_name}" AND ("hiring" OR "job" OR "careers") AND ("{role_1}" OR "{role_2}")
  2. For each job posting found, extract:

    • Job title
    • Location (remote/onsite/hybrid)
    • Posted date
    • Job description summary (key responsibilities)
    • Source URL
  3. Classify each posting:

    • Replaces: The job title matches roles_replaced. Your product could eliminate or reduce the need for this hire. This is the strongest signal.
    • Augments: The job title matches roles_augmented. Your product makes this person more effective — they'd want it as a tool. Good signal.
    • Keyword match: Title doesn't match but the JD contains jd_keywords. Weaker signal — verify relevance.
  4. Filter: Drop companies with no matching job postings. Drop postings older than lookback_days.

Output Contract
companies_hiring: [
  {
    company: {
      name: string
      domain: string
      industry: string
    }
    job_postings: [
      {
        title: string
        location: string
        posted_date: string
        description_summary: string     # 2-3 sentence summary of the role
        source_url: string
        signal_type: "replaces" | "augments" | "keyword_match"
        relevance_reasoning: string     # Why this posting matters for your product
      }
    ]
    posting_count: integer
    strongest_signal: "replaces" | "augments" | "keyword_match"
  }
]
Human Checkpoint
Found hiring signals at X of Y companies:

| Company | Postings | Strongest Signal | Top Role | Posted |
|---------|----------|-----------------|----------|--------|
| Acme Corp | 3 | Replaces | BDC Representative | 3 days ago |
| Beta Inc | 1 | Augments | Sales Manager | 1 week ago |
| ...     | ...      | ...             | ...      | ...    |

Signal breakdown: X "replaces" (strongest), Y "augments", Z "keyword match"

Proceed with qualification? (Y/n)

Step 2: Qualify & Prioritize

Purpose: Rank companies by outreach priority based on signal strength, relevance, and timing. Pure LLM reasoning — inherently tool-agnostic.

Input Contract
companies_hiring: [...]           # From Step 1 output
your_company: {
  description: string
  pain_point: string
  proof_points: string[]
  cost_comparison: string
  deployment_speed: string
}
Process

For each company, evaluate:

CriterionWeightHow to Assess
Signal typeHighest"Replaces" > "Augments" > "Keyword match"
Posting volumeHighMultiple relevant postings = scaling that function = bigger need
RecencyHighPosted <7 days ago = actively evaluating. 14+ days = may have candidates already
Role seniorityMediumHiring a VP of the function you sell into = strategic buy. Hiring an individual contributor = operational buy. Both are good, different approach.
Industry fitMediumIs their industry one where your product has proven results?
Scoring
  • Tier 1 (Act Today): "Replaces" signal + posted within 7 days. They're literally budgeting for what you sell.
  • Tier 2 (Act This Week): "Replaces" signal 7-14 days old, OR "Augments" signal <7 days with multiple postings.
  • Tier 3 (Queue): "Augments" or "keyword match" signals. Worth reaching out but lower urgency.
  • Drop: Keyword match only with weak relevance after reviewing the JD.

For each qualified company, generate:

  • Relevance reasoning: Why this hiring pattern matters for your product
  • Outreach angle: The specific connection between their job posting and your product
    • "Replaces" → "Before you fill that role, consider what [product] does instead"
    • "Augments" → "Your new [role] will need tools like [product] to hit the ground running"
  • Recommended framing: Replace (you don't need to hire for this), Complement (your new hire will be 3x more effective with this), or Scale (you need 5 of these people — or 1 person + our product)
Output Contract
qualified_companies: [
  {
    ...company_hiring_fields,
    priority_tier: "tier_1" | "tier_2" | "tier_3"
    relevance_reasoning: string
    outreach_angle: string
    recommended_framing: "replace" | "complement" | "scale"
  }
]
dropped_companies: [
  { name: string, drop_reason: string }
]
Human Checkpoint
## Qualification Results

### Tier 1 — Act Today (X companies)
| Company | Signal | Top Role | Framing | Angle |
|---------|--------|----------|---------|-------|
| Acme Corp | Replaces | BDC Rep (x3) | Replace | Before you hire 3 BDC reps... |

### Tier 2 — Act This Week (X companies)
| ... |

### Tier 3 — Queue (X companies)
| ... |

### Dropped (X companies)
| Company | Reason |
|---------|--------|
| ...     | ...    |

Approve this list before we find contacts?

Step 3: Find Relevant People

Purpose: For each qualified company, find the right people to contact. Unlike the funding composite, here we also identify who posted the job (the hiring manager) — they're often the best first contact.

Input Contract
qualified_companies: [...]        # From Step 2 output
buyer_titles: string[]            # From config
champion_titles: string[]         # From config
user_titles: string[]             # From config
max_contacts_per_company: integer  # Default: 3-5
Show full SKILL.md (730 more words)Show less
Process

For each qualified company, use the configured contact_tool:

  1. First: Identify the hiring manager. The person who posted or owns the job posting is the most relevant contact.

    • Check the job posting source for the hiring manager's name
    • If not listed, search for people at the company with titles one level above the posted role
    • The hiring manager is often the strongest contact because they own the problem your product solves
  2. Second: Find buyer-level contacts. People with buyer_titles at this company — they control budget.

  3. Third: Find champions. People with champion_titles — they feel the pain daily and can advocate internally.

  4. Classify each contact:

    • Hiring manager — Posted the role. Directly owns the problem. Best for "replace" framing.
    • Buyer — Controls budget. Best for ROI/cost-comparison framing.
    • Champion — Lives the pain. Best for "complement" or "make your life easier" framing.
    • User — Would operate the product. Best for bottom-up adoption.
  5. Cap at max_contacts_per_company. Prioritize: hiring manager > buyer > champion > user.

Output Contract
contacts: [
  {
    person: {
      full_name: string
      first_name: string
      last_name: string
      title: string
      email: string | null
      linkedin_url: string | null
      role_type: "hiring_manager" | "buyer" | "champion" | "user"
    }
    company: {
      name: string
      domain: string
      priority_tier: string
      outreach_angle: string
      recommended_framing: string
    }
    job_context: {
      relevant_posting_title: string        # The job posting that triggered this signal
      signal_type: string                    # "replaces" or "augments"
      posting_url: string
      description_summary: string
    }
  }
]
Human Checkpoint
## Contacts Found

### Acme Corp (Tier 1 — Hiring 3x BDC Reps, "Replace" framing)
| Name | Title | Role Type | Email | LinkedIn |
|------|-------|-----------|-------|----------|
| Sarah Chen | VP Sales | Hiring Manager | sarah@acme.com | ... |
| Mike Johnson | COO | Buyer | mike@acme.com | ... |
| Lisa Park | BDC Manager | Champion | lisa@acme.com | ... |

Relevant posting: "BDC Representative" (posted 3 days ago)

### Beta Inc (Tier 2 — Hiring Sales Manager, "Complement" framing)
| ... |

Total: X contacts across Y companies

Approve before we draft emails?

Step 4: Draft Personalized Emails

Purpose: For each contact, draft a personalized email sequence using three layers of personalization: the job posting context, your company's value, and the prospect's company context. Pure LLM reasoning — inherently tool-agnostic.

Input Contract
contacts: [...]                   # From Step 3 output (includes job_context per contact)
your_company: {
  description: string
  pain_point: string
  proof_points: string[]
  cost_comparison: string         # e.g. "4x cheaper than a full-time hire"
  deployment_speed: string        # e.g. "Live in 2 weeks vs. 3-month hiring cycle"
}
sequence_config: {
  touches: integer                # Default: 3
  timing: integer[]               # Default: [1, 5, 12]
  personalization_tier: 1 | 2 | 3
  tone: string
  cta: string
}
Process
  1. Select framework based on framing:

    • "Replace" framing → Signal-Proof-Ask (reference the job posting, show what your product does instead, soft ask)
    • "Complement" framing → BAB (before: your new hire struggles with X / after: with our tool they're 3x faster / bridge: here's how)
    • "Scale" framing → PAS (problem: you need 5 people for this / agitate: that's $500K/year in salary / solve: or 1 person + our product)
  2. Build three layers of personalization per contact:

    LayerSourceUsed In
    Job posting contextStep 1 — the specific role, responsibilities, what the JD saysSubject line + hook
    Your company contextConfig — what you do, proof points, cost comparisonBody — proof + offer
    Prospect company contextStep 2 — industry, what they do, why they're hiringBody — relevance framing
  3. Adapt email angle by role_type:

    Role TypeEmail AngleExample Hook
    Hiring manager"Before you fill that role""I saw you're hiring a BDC Rep — before you finalize that, worth seeing what [product] does instead."
    BuyerCost/ROI comparison"You're budgeting for 3 BDC reps ($180K/year). [Product] handles the same workload for a fraction of that."
    Champion"This will make your life easier""When your new BDC rep starts, they'll need tools to be effective from day one. That's what [product] does."
    User"You'll want this on your desk""If you're scaling the BDC function at [company], [product] handles [task] so you can focus on [higher-value work]."
  4. Follow email-drafting skill rules:

    • Touch 1: 50-90 words. Hook with job posting signal.
    • Touch 2: 30-50 words. Different proof point or cost/speed comparison.
    • Touch 3: 20-40 words. Social proof or breakup.
    • All hard rules apply.
Output Contract
email_sequences: [
  {
    contact: { full_name, email, title, role_type, company_name }
    job_context: { posting_title, signal_type }
    sequence: [
      {
        touch_number: integer
        send_day: integer
        subject: string
        body: string
        framework: string
        personalization_layers: {
          job_posting: string      # What from the JD was referenced
          company_context: string  # What proof/value was used
          prospect_context: string # What about their company was referenced
        }
        word_count: integer
      }
    ]
  }
]
Human Checkpoint

Present 3-5 sample sequences showing each role_type and framing:

## Sample Emails for Review

### Hiring Manager: Sarah Chen, VP Sales @ Acme Corp
Signal: Hiring 3x BDC Representatives | Framing: Replace

**Touch 1 — Day 1**
Subject: Before you fill those BDC roles
> Hi Sarah — I noticed Acme is hiring three BDC reps. Before you go through
> a 3-month hiring cycle, worth seeing what companies like [peer] are doing
> instead...
> [full email]

### Buyer: Mike Johnson, COO @ Acme Corp
Signal: Same | Framing: ROI

**Touch 1 — Day 1**
Subject: $180K/year in BDC hires — or this
> Hi Mike — Acme's hiring 3 BDC reps. At ~$60K each fully loaded, that's
> $180K/year. [Product] handles the same call volume for...
> [full email]

---

Approve these samples? I'll generate the rest in the same style.

Step 5: Handoff to Outreach

Identical to funding-signal-outreach Step 5. Package contacts + email sequences for the configured outreach tool. See that composite for the full handoff process.

Output Contract
campaign_package: {
  tool: string
  file_path: string
  contact_count: integer
  sequence_touches: integer
  estimated_send_days: integer
  next_action: string
}
Human Checkpoint
## Campaign Ready

Tool: [configured tool]
Signal type: Hiring signal
Contacts: X people across Y companies
Sequence: 3 touches over 12 days

Ready to launch?

Execution Summary

StepTool DependencyHuman CheckpointTypical Time
0. ConfigNoneFirst run only5 min (once)
1. DetectConfigurable (LinkedIn Jobs, Indeed, web search)Review companies with postings2-5 min
2. QualifyNone (LLM reasoning)Approve tier rankings2-3 min
3. Find PeopleConfigurable (Apollo, LinkedIn, etc.)Approve contact list2-3 min
4. Draft EmailsNone (LLM reasoning)Review samples, iterate5-10 min
5. HandoffConfigurable (Smartlead, CSV, etc.)Final launch approval1 min

Total human review time: ~15-20 minutes


Tips

  • "Replaces" signals are gold. If they're hiring for what your product does, you have the strongest possible outreach angle. Prioritize these.
  • Time the outreach to the posting age. Day 1-7: "Before you start interviewing." Day 7-14: "While you're evaluating candidates." Day 14+: "Before you extend an offer."
  • Don't say "you don't need to hire." Instead frame it as "your team gets this capability faster" or "complement your new hire with this." Less threatening to the hiring manager who already committed to the req.
  • Multiple postings for the same role = scaling signal. If they're hiring 3x BDC reps, the pain is 3x bigger and the cost comparison is 3x more compelling.
  • The hiring manager is your best first contact because they own the problem. But cc'ing or separately reaching the buyer (their boss) with an ROI angle creates a pincer effect.

© gooseworks-ai, 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 in skills/outreach/composites/hiring-signal-outreach of gooseworks-ai/goose-skills.

  • SKILL.md
  • skill.meta.json

Open the folder on GitHubat commit c650c6d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Hiring Signal Outreach 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.

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Outreach Messagemohitagw15856/pm-claude-skills1.4k—~827Automated safety check: PassMIT

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Questions about Hiring Signal Outreach

What does Hiring Signal Outreach do?

End-to-end hiring signal composite. An agent skill from gooseworks-ai/goose-skills. Hiring Signal Outreach is an agent skill from gooseworks-ai/goose-skills. End-to-end hiring signal composite.

When should I use Hiring Signal Outreach?

Hiring Signal Outreach fits situations like: tasks that involve Recruiting and HR; tasks that involve Cold outreach.

How do I install Hiring Signal Outreach in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill hiring-signal-outreach -a claude-code`. Or copy the skill folder (skills/outreach/composites/hiring-signal-outreach in gooseworks-ai/goose-skills) into .claude/skills/hiring-signal-outreach in your project. Claude Code loads it when a task matches its description.

How do I install Hiring Signal Outreach in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill hiring-signal-outreach -a codex`. Or copy the skill folder (skills/outreach/composites/hiring-signal-outreach in gooseworks-ai/goose-skills) into .agents/skills/hiring-signal-outreach in your project. Codex loads it when a task matches its description.

Can I use Hiring Signal Outreach 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 gooseworks-ai/goose-skills --skill hiring-signal-outreach -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hiring-signal-outreach, .gemini/skills/hiring-signal-outreach, .github/skills/hiring-signal-outreach and .opencode/skills/hiring-signal-outreach in your project.

What does Hiring Signal Outreach need to run?

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

Does Hiring Signal Outreach 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 Hiring Signal Outreach 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 Hiring Signal Outreach use?

Hiring Signal Outreach 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 Hiring Signal Outreach use?

About 5.1k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Hiring Signal Outreach?

Skills that share tags, products or a category with Hiring Signal Outreach: Outreach (andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-, 502 stars), Sourcing Outreach (shawnpang/startup-founder-skills, 343 stars), Networking Outreach (mohitagw15856/pm-claude-skills, 1.4k stars) and LinkedIn MCP Usage Rules (stickerdaniel/linkedin-mcp-server, 3.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hiring Signal Outreach?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.