Agent skill

Community Signals

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

Extract leads from developer forums (Hacker News, Reddit) by detecting intent signals — alternative seeking, competitor pain, scaling challenges, DIY solutions, and migration intent.

MITAuto-check: notesMarketing & SEO

Install Community Signals

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill community-signals -a claude-code

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

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills community-signals --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/lead-generation/packs/lead-gen-devtools/community-signals .claude/skills/community-signals && 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
community-signals
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3.4k tokens
SKILL.md length
1,612 words
Files
2 (incl. scripts)
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Extract leads from developer forums (Hacker News, Reddit) by detecting intent signals — alternative seeking, competitor pain, scaling challenges, DIY solutions, and migration intent.

  • Works in 4 steps: Collect Context → Generate Search Queries → Execute Scan → …
  • Tasks that involve Lead generation
  • SKILL.md covers When to Use, Prerequisites, Phase 1: Collect Context and Phase 2: Generate Search Queries, plus 6 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Community Signals is an agent skill from gooseworks-ai/goose-skills. Extract leads from developer forums (Hacker News, Reddit) by detecting intent signals — alternative seeking, competitor pain, scaling challenges, DIY solutions, and migration intent. Scores users by intent strength and cross-platform presence.

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/community_signals.py`).

It sits in Marketing & SEO, covering Lead generation. It works with Reddit. 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 Lead generation

Example prompts

  • “/community-signals”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash, Read, Write, Edit, Grep, Glob, WebSearch

Workflow steps

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

  1. Collect Context
  2. Generate Search Queries
  3. Execute Scan
  4. Analyze & Recommend

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 these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • WebSearch

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    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

Community Signals loads about 3.4k tokens when it runs. Until then it costs about 65 tokens; SKILL.md has 1,612 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~65
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:25
    - Apify API token in `.env` (for Reddit scraping)
  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Write, Edit, Grep, Glob, WebSearch

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); the scripts in this folder are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,612 words, ~3,425 tokens.

Download SKILL.mdSave it as .claude/skills/community-signals/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
community-signals
description
Extract leads from developer forums (Hacker News, Reddit) by detecting intent signals — alternative seeking, competitor pain, scaling challenges, DIY solutions, and migration intent. Scores users by intent strength and cross-platform presence.
allowed-tools
Bash, Read, Write, Edit, Grep, Glob, WebSearch
user-invocable
true
argument-hint
queries-json-path

Community Signals

Extract high-intent leads from developer community forums by detecting buying signals in public discussions. Currently supports Hacker News and Reddit.

When to Use

  • User wants to find leads from developer communities or forums
  • User wants to identify people publicly expressing pain with competitors
  • User wants to find people asking "what tool should I use for X"
  • User mentions Hacker News, Reddit, Stack Overflow, or developer forums as lead sources
  • User describes prospects who discuss tools, complain about solutions, or ask for recommendations in public forums
  • User wants to find developers who built DIY/hacky solutions for problems the user's product solves

Prerequisites

  • Python 3.9+ with requests and optionally python-dotenv
  • Apify API token in .env (for Reddit scraping)
  • No auth needed for Hacker News (free Algolia API)
  • Working directory: the project root containing this skill

Phase 1: Collect Context

Step 1: Gather Product & ICP Information

Ask the user for the following. Do NOT proceed without this — the entire query generation depends on it.

"To find the right leads from developer communities, I need to understand:

  1. What does your product do? (one-liner)
  2. Who are your competitors? (list the main ones)
  3. What specific problems does your product solve? (the pain points)
  4. Who is your ideal buyer? (role, company type, tech stack)
  5. Any specific technologies or keywords associated with your space?"

If the user has already provided this context (e.g., from running the github-repo-signals skill), use that — don't ask again.

Phase 2: Generate Search Queries

Step 2: Generate Queries Across 9 Categories

Based on the user's product info, generate 3-5 search queries per category. These are the fixed categories — do not skip any:

Category 1: Alternative Seeking (intent score: 9) People actively looking to switch tools.

  • Pattern: "[competitor] alternative", "alternative to [competitor]", "looking for [product type]"
  • Example: "twilio alternative", "alternative to agora", "looking for video SDK"

Category 2: Competitor Pain (intent score: 8) People frustrated with a specific competitor.

  • Pattern: "[competitor] issues", "frustrated with [competitor]", "[competitor] doesn't support"
  • Example: "twilio video quality issues", "frustrated with agora pricing", "vonage api unreliable"

Category 3: Problem Space Questions (intent score: 6) People trying to solve the exact problem the product addresses.

  • Pattern: "how to [thing product does]", "best way to [problem]", "recommendations for [category]"
  • Example: "how to add video calling to app", "best webrtc framework", "real-time communication SDK"

Category 4: Tool Comparison (intent score: 8) People actively comparing options — in buying mode.

  • Pattern: "[competitor A] vs [competitor B]", "comparing [tools]", "which [product type] should I use"
  • Example: "twilio vs agora", "comparing video APIs", "which webrtc platform"

Category 5: DIY / Built Own Solution (intent score: 9) People who built a custom solution — validated the need, would pay for a proper product.

  • Pattern: "I built my own [thing]", "Show HN: [thing product replaces]", "custom [solution type]"
  • Example: "I built my own video conferencing", "Show HN: open source video call", "custom webrtc server"

Category 6: Scaling Challenges (intent score: 7) People hitting limits that the product solves.

  • Pattern: "[problem] at scale", "scaling [thing]", "[thing] breaks with many users"
  • Example: "webrtc scaling issues", "video calls lagging with 50+ participants", "scaling real-time communication"

Category 7: Migration Intent (intent score: 9) People who have already decided to leave — looking for where to go.

  • Pattern: "migrating from [competitor]", "moving away from [competitor]", "switching from [competitor]"
  • Example: "migrating from twilio video", "moving away from agora", "switching video API providers"

Category 8: Budget / Pricing Pain (intent score: 7) Cost is the trigger — open to cheaper or better-value alternatives.

  • Pattern: "[competitor] too expensive", "[competitor] pricing", "cheaper alternative to [competitor]"
  • Example: "twilio too expensive", "agora pricing 2026", "cheaper video API"

Category 9: Feature Gap Complaints (intent score: 7) Needs something their current tool doesn't do — and the user's product does.

  • Pattern: "does [competitor] support [feature]", "[competitor] missing [feature]", "wish [competitor] had"
  • Example: "does twilio support recording", "agora missing breakout rooms", "wish vonage had better docs"
Step 3: Discover Relevant Subreddits

Do a web search to find subreddits where the user's ICP is active. Search for:

  • "[product category] subreddit"
  • "[technology] subreddit"
  • "[competitor name] subreddit"

Common developer subreddits to consider (pick the relevant ones):

  • r/programming, r/webdev, r/devops, r/selfhosted
  • r/kubernetes, r/aws, r/googlecloud, r/azure
  • r/node, r/python, r/golang, r/rust
  • r/startups, r/SaaS, r/entrepreneur
  • r/sysadmin, r/networking
  • Technology-specific: r/VOIP, r/machinelearning, r/dataengineering, etc.

Select 5-10 subreddits most relevant to the user's space.

Step 4: Present Queries for Review

Present ALL generated queries to the user in a structured table:

Category                  | Queries
--------------------------|------------------------------------------
Alternative Seeking       | "twilio alternative", "agora alternative", ...
Competitor Pain           | "twilio issues", "frustrated with agora", ...
...                       | ...

Subreddits to scan: r/webdev, r/VOIP, r/programming, ...

Ask:

"Here are the search queries I've generated. Would you like to:

  1. Run with these as-is
  2. Add or remove specific queries
  3. Add or remove subreddits

Estimated cost: HN is free. Reddit via Apify will cost approximately $[estimate based on query count x ~$0.05 per query]."

Wait for user approval before proceeding.

Step 5: Save Queries File

Once approved, save the queries as a JSON file:

bash
cat > ${CLAUDE_SKILL_DIR}/../.tmp/community_queries.json << 'QUERIESEOF'
{
    "product": "Product Name",
    "queries": [
        {"category": "alternative_seeking", "query": "twilio alternative"},
        {"category": "alternative_seeking", "query": "agora alternative"},
        {"category": "competitor_pain", "query": "twilio video quality issues"}
    ],
    "subreddits": ["r/webdev", "r/VOIP", "r/programming"]
}
QUERIESEOF

Phase 3: Execute Scan

Step 6: Verify Environment
bash
python3 -c "import requests; print('OK')"
Step 7: Run the Tool
bash
python3 ${CLAUDE_SKILL_DIR}/scripts/community_signals.py \
    --queries ${CLAUDE_SKILL_DIR}/../.tmp/community_queries.json \
    --days 30 \
    --max-reddit-posts 50 \
    --max-reddit-comments 20 \
    --output ${CLAUDE_SKILL_DIR}/../.tmp/community_signals.csv

The tool will:

  1. Search Hacker News (stories + comments) for all queries — free
  2. Search Reddit via Apify for all queries + scan subreddits — pay per result
  3. Filter to last 30 days
  4. Deduplicate users across platforms
  5. Score by intent strength, signal count, category diversity, and cross-platform presence
  6. Fetch HN user profiles (karma, bio) — free
  7. Export two CSV files: _users.csv and _signals.csv

Optional flags:

  • --skip-reddit — only search HN (free, for testing)
  • --skip-hn — only search Reddit
  • --days 7 — narrower time window for very fresh signals

Phase 4: Analyze & Recommend

Show full SKILL.md (712 more words)Show less
Step 9: Analyze the Results

Read the output CSV files and present a structured briefing:

9a. Overall Stats

  • Total signals found (HN + Reddit)
  • Unique users
  • Split by platform (HN vs Reddit)
  • Cross-platform matches (same username on both)

9b. Signal Category Breakdown

  • How many signals per category
  • Which categories produced the most results
  • Which categories had the highest-engagement posts (upvotes, comments)

9c. Top Subreddits Discovered

  • Which subreddits appeared most frequently
  • This tells the user where their prospects hang out — valuable for community marketing, not just outreach

9d. Highest-Intent Users

  • List top 15-20 users by composite score
  • For each: username, platform, categories they appeared in, sample post/comment, engagement
  • Flag cross-platform users prominently

9e. Common Themes

  • What are people specifically asking for or complaining about?
  • Any patterns in the pain points that the user's product addresses?
  • Any surprising findings (e.g., a competitor getting mentioned negatively much more than others)?
Step 10: Recommend Next Steps

Based on findings + user's product context:

  1. If strong signals found (>50 high-intent users):

    • Recommend enriching top users via SixtyFour
    • For HN users: use their HN bio/karma + username for enrichment context
    • For Reddit users: username is the only identifier — enrichment hit rates may be lower
    • Suggest starting with HN users (more likely to have real names in bio)
  2. If cross-platform matches found:

    • These are highest priority — someone active on both HN and Reddit in your space is deeply engaged
    • Recommend enriching these first
  3. If specific subreddits emerged as hotspots:

    • Recommend ongoing monitoring of those subreddits
    • Suggest the user consider community engagement (commenting, answering questions) in those subreddits
  4. If "alternative seeking" or "migration intent" signals dominate:

    • These are the most time-sensitive leads — they're actively evaluating RIGHT NOW
    • Recommend immediate outreach
  5. If "DIY / built own" signals found:

    • These are the highest-quality leads — they've validated the need
    • Recommend personalized outreach referencing their project
  6. Always include:

    • Cost estimate for enrichment
    • Suggested outreach angle per signal category
    • Reminder that community forum users respond better to helpful engagement than cold outreach
Step 11: Ask for Go-Ahead

"Would you like me to:

  1. Enrich the top [N] users via SixtyFour (estimated cost: $X)
  2. Run a deeper scan on the hotspot subreddits
  3. Export this data for manual review first
  4. Combine these results with GitHub signals data (if available)"

Wait for user confirmation.

Output Schema

community_signals_users.csv — One row per unique user across all platforms

ColumnDescription
usernameForum username
platformhackernews or reddit
composite_scoreOverall lead score (intent + diversity + cross-platform)
intent_scoreSum of category-weighted intent scores
signal_countNumber of matching posts/comments
categoriesWhich signal categories they appeared in
platforms_activeWhich platforms they were found on
subredditsReddit subreddits they posted in
hn_karmaHN karma score (HN users only)
hn_bioHN profile bio (HN users only)
total_engagementSum of upvotes + comments across their signals
first_seenEarliest matching post/comment
latest_seenMost recent matching post/comment
sample_urlLink to one of their matching posts

community_signals_signals.csv — One row per matching post/comment

ColumnDescription
platformhackernews or reddit
authorUsername
categorySignal category code
category_labelHuman-readable category name
content_typestory, comment, or post
titlePost/story title
textPost/comment body (truncated)
subredditReddit subreddit (if applicable)
scoreUpvotes
num_commentsComment count
created_atDate posted
query_matchedWhich search query found this
urlPermalink to the post/comment

Scoring System

Intent scores by category:

CategoryScore per Signal
Alternative Seeking9
DIY / Built Own9
Migration Intent9
Competitor Pain8
Tool Comparison8
Scaling Challenge7
Budget / Pricing7
Feature Gap7
Problem Space6

Composite score bonuses:

  • +2 per unique category the user appeared in (diversity)
  • +10 if user found on multiple platforms (cross-platform)
  • +2 per signal (capped at +10)

Cost Estimates

PlatformCostNotes
Hacker NewsFreeAlgolia API, 10k req/hr
Reddit (Apify)~$0.004/result + $0.04/runPay per result
Typical run (45 queries)~$5-10 totalHN free + Reddit ~$5-10

Limitations

  • Reddit comments: Can't search comments directly — finds posts first, then fetches comments on those posts. Some comment-only discussions may be missed.
  • Reddit date filter: No native date range parameter in Apify actor. Filtering happens in post-processing using created_at timestamps.
  • User identity: Forum usernames are pseudonymous. Enrichment hit rates will be lower than GitHub (where people often use real names). HN users are more identifiable (many put real names in bio).
  • Rate limits: HN Algolia: 10k req/hr. Apify: depends on plan.

© 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 (scripts) in skills/lead-generation/packs/lead-gen-devtools/community-signals of gooseworks-ai/goose-skills.

  • SKILL.md
  • scripts/community_signals.py

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

Community Signals 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.

Community Signals compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Community Signals this skillgooseworks-ai/goose-skills1.2k1 repos~3.4kAutomated safety check: NotesMIT
Google Social Media Finderbrowser-act/skills6.1k—~1.7kAutomated safety check: PassMIT
Fireauto Research Guideimgompanda/fireauto140—~524Automated safety check: PassMIT
Apify Multi-Platform Scraperapify/agent-skills2.4k2 repos~1.4kAutomated safety check: NotesNone
Reddit Lead Discoverylignertys/reddit-research-skills14—~3.8kAutomated safety check: PassMIT
Bright Data MCPbrightdata/skills2641 repos~3.7kAutomated safety check: PassMIT

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Works with

Categories

Questions about Community Signals

What does Community Signals do?

Extract leads from developer forums (Hacker News, Reddit) by detecting intent signals — alternative seeking, competitor pain, scaling challenges, DIY solutions, and migration intent. Community Signals is an agent skill from gooseworks-ai/goose-skills. Extract leads from developer forums (Hacker News, Reddit) by detecting intent signals — alternative seeking, competitor pain, scaling challenges, DIY solutions, and migration intent.

When should I use Community Signals?

Community Signals fits situations like: tasks that involve Lead generation.

How do I install Community Signals in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill community-signals -a claude-code`. Or copy the skill folder (skills/lead-generation/packs/lead-gen-devtools/community-signals in gooseworks-ai/goose-skills) into .claude/skills/community-signals in your project. Claude Code loads it when a task matches its description.

How do I install Community Signals in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill community-signals -a codex`. Or copy the skill folder (skills/lead-generation/packs/lead-gen-devtools/community-signals in gooseworks-ai/goose-skills) into .agents/skills/community-signals in your project. Codex loads it when a task matches its description.

Can I use Community Signals 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 community-signals -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/community-signals, .gemini/skills/community-signals, .github/skills/community-signals and .opencode/skills/community-signals in your project.

What does Community Signals need to run?

Going by SKILL.md and its folder, Community Signals needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash, Read, Write, Edit, Grep, Glob, WebSearch.

Does Community Signals 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 Community Signals safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file; pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Community Signals use?

Community Signals 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 Community Signals use?

About 3.4k tokens (SKILL.md is roughly 14k 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 Community Signals?

Skills that share tags, products or a category with Community Signals: Google Social Media Finder (browser-act/skills, 6.1k stars), Fireauto Research Guide (imgompanda/fireauto, 140 stars), Apify Multi-Platform Scraper (apify/agent-skills, 2.4k stars) and Reddit Lead Discovery (lignertys/reddit-research-skills, 14 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Community Signals?

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.