map-your-market

Name: map-your-market
Author: Varnan-Tech

22installs59forks

How to install map-your-market?

varnan-tech/opendirectory/map-your-market

$npx -y skills add Varnan-Tech/opendirectory --skill map-your-market

Installs into the current project.

Summary

Given a product description, category keywords, or competitor names (any combination), searches Reddit, Hacker News, GitHub Issues, G2, and Google Trends for the real pains your market experiences, then synthesizes everything into a positioning framework showing who your ICP is,

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SKILL.md

1	# Map Your Market
2
3	Take a product description, category keywords, or competitor names. Search Reddit, HN, GitHub Issues, G2, and Google Trends for real pain signals. Score and cluster them. Build a complete positioning framework: ICP definition, ranked pain themes with verbatim quotes, market size signals, and messaging angles derived from actual language people use.
4
5	---
6
7	Critical rule: Every pain quote in the output must exist verbatim in the raw data collected by the script. Every vendor name in the market map must come from G2 scrape results or GitHub search results. Market size must say "signals suggest" -- never estimate a dollar figure from thin proxies. If a source returns 0 results, report 0 -- do not supplement with invented examples.
8
9	---
10
11	## Common Mistakes
12
13	\| The agent will want to... \| Why that's wrong \|
14	\|---\|---\|
15	\| Invent pain points or market size numbers \| Every pain quote must be verbatim from raw data. Market size must cite signals found. Never estimate "typical" market size. \|
16	\| Score by post count instead of pain_score \| A post with 2,000 upvotes about pricing is stronger than 50 posts with 10 upvotes each. Use the pain_score formula from references/pain-scoring.md. \|
17	\| Use the same subreddits for every category \| r/politics adds noise to a devops search. Auto-detect relevant subreddits from the category and competitor names before searching. \|
18	\| Send all raw signals to AI without scoring \| Score locally first. Send only the top 60 high-pain-score signals to AI clustering. Saves tokens and improves cluster quality. \|
19	\| Skip ICP extraction from post metadata \| Subreddit, flair, author bio (HN), and GitHub org type are richer ICP signals than post content. Always capture and report them. \|
20	\| Conflate vendor count with market size \| "47 vendors on G2" means competitive, not large. Present all signals as directional indicators, not hard numbers. \|
21
22	---
23
24	## Step 1: Setup Check
25
26	```bash
27	echo "GITHUB_TOKEN: ${GITHUB_TOKEN:-not set -- GitHub Issues search runs at 60 req/hr unauthenticated}"
28	echo "No other API keys required."
29	echo ""
30	echo "Data sources this run will use:"
31	echo " Reddit public JSON (no auth, 10 req/min)"
32	echo " HN Algolia API (no auth, free)"
33	echo " GitHub Issues API (${GITHUB_TOKEN:+authenticated, }60-5000 req/hr)"
34	echo " G2 category scrape (no auth, HTML parse)"
35	echo " Google Trends (no auth, unofficial endpoint)"
36	```
37
38	If `GITHUB_TOKEN` is not set: continue. Unauthenticated GitHub search is 60 req/hr -- enough for a standard run. For repeated use, add a token at github.com/settings/tokens (no scopes needed for public repos).
39
40	---
41
42	## Step 2: Parse Input
43
44	Collect from the conversation:
45	- `category` -- keyword(s) describing the market space (e.g. "developer observability", "B2B analytics", "devops tooling")
46	- `competitors` -- optional list of competitor product names or domains (e.g. "Datadog, New Relic, Grafana")
47	- `product_context` -- optional: what the user's product does (helps tailor messaging angles)
48
49	If the user provides only a product description with no category keyword: extract 2-3 category keywords from it yourself.
50
51	If the user provides only competitor names with no category: infer the category by looking up competitors.
52
53	Write the parsed input:
54
55	```bash
56	python3 << 'PYEOF'
57	import json, os
58
59	data = {
60	"category": "CATEGORY_HERE",
61	"competitors": ["COMP_1", "COMP_2"],
62	"product_context": "PRODUCT_CONTEXT_HERE"
63	}
64
65	with open("/tmp/mym-input.json", "w") as f:
66	json.dump(data, f, indent=2)
67	print("Input written to /tmp/mym-input.json")
68	print(f"Category: {data['category']}")
69	print(f"Competitors: {', '.join(data['competitors']) if data['competitors'] else 'none provided'}")
70	PYEOF
71	```
72
73	---
74
75	## Step 3: Run the Standalone Data Collection Script
76
77	The script handles all data collection. Check if it exists first:
78
79	```bash
80	ls scripts/fetch.py 2>/dev/null && echo "script available" \|\| echo "not found"
81	```
82
83	If available, run it:
84
85	```bash
86	GITHUB_TOKEN="${GITHUB_TOKEN:-}" python3 scripts/fetch.py \
87	"$(python3 -c "import json; d=json.load(open('/tmp/mym-input.json')); print(d['category'])")" \
88	--co

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map-your-market

22installs59forks

View on GitHub

How to install map-your-market?

varnan-tech/opendirectory/map-your-market

$npx -y skills add Varnan-Tech/opendirectory --skill map-your-market

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Map Your Market
2
3	Take a product description, category keywords, or competitor names. Search Reddit, HN, GitHub Issues, G2, and Google Trends for real pain signals. Score and cluster them. Build a complete positioning framework: ICP definition, ranked pain themes with verbatim quotes, market size signals, and messaging angles derived from actual language people use.
4
5	---
6
7	Critical rule: Every pain quote in the output must exist verbatim in the raw data collected by the script. Every vendor name in the market map must come from G2 scrape results or GitHub search results. Market size must say "signals suggest" -- never estimate a dollar figure from thin proxies. If a source returns 0 results, report 0 -- do not supplement with invented examples.
8
9	---
10
11	## Common Mistakes
12
13	\| The agent will want to... \| Why that's wrong \|
14	\|---\|---\|
15	\| Invent pain points or market size numbers \| Every pain quote must be verbatim from raw data. Market size must cite signals found. Never estimate "typical" market size. \|
16	\| Score by post count instead of pain_score \| A post with 2,000 upvotes about pricing is stronger than 50 posts with 10 upvotes each. Use the pain_score formula from references/pain-scoring.md. \|
17	\| Use the same subreddits for every category \| r/politics adds noise to a devops search. Auto-detect relevant subreddits from the category and competitor names before searching. \|
18	\| Send all raw signals to AI without scoring \| Score locally first. Send only the top 60 high-pain-score signals to AI clustering. Saves tokens and improves cluster quality. \|
19	\| Skip ICP extraction from post metadata \| Subreddit, flair, author bio (HN), and GitHub org type are richer ICP signals than post content. Always capture and report them. \|
20	\| Conflate vendor count with market size \| "47 vendors on G2" means competitive, not large. Present all signals as directional indicators, not hard numbers. \|
21
22	---
23
24	## Step 1: Setup Check
25
26	```bash
27	echo "GITHUB_TOKEN: ${GITHUB_TOKEN:-not set -- GitHub Issues search runs at 60 req/hr unauthenticated}"
28	echo "No other API keys required."
29	echo ""
30	echo "Data sources this run will use:"
31	echo " Reddit public JSON (no auth, 10 req/min)"
32	echo " HN Algolia API (no auth, free)"
33	echo " GitHub Issues API (${GITHUB_TOKEN:+authenticated, }60-5000 req/hr)"
34	echo " G2 category scrape (no auth, HTML parse)"
35	echo " Google Trends (no auth, unofficial endpoint)"
36	```
37
38	If `GITHUB_TOKEN` is not set: continue. Unauthenticated GitHub search is 60 req/hr -- enough for a standard run. For repeated use, add a token at github.com/settings/tokens (no scopes needed for public repos).
39
40	---
41
42	## Step 2: Parse Input
43
44	Collect from the conversation:
45	- `category` -- keyword(s) describing the market space (e.g. "developer observability", "B2B analytics", "devops tooling")
46	- `competitors` -- optional list of competitor product names or domains (e.g. "Datadog, New Relic, Grafana")
47	- `product_context` -- optional: what the user's product does (helps tailor messaging angles)
48
49	If the user provides only a product description with no category keyword: extract 2-3 category keywords from it yourself.
50
51	If the user provides only competitor names with no category: infer the category by looking up competitors.
52
53	Write the parsed input:
54
55	```bash
56	python3 << 'PYEOF'
57	import json, os
58
59	data = {
60	"category": "CATEGORY_HERE",
61	"competitors": ["COMP_1", "COMP_2"],
62	"product_context": "PRODUCT_CONTEXT_HERE"
63	}
64
65	with open("/tmp/mym-input.json", "w") as f:
66	json.dump(data, f, indent=2)
67	print("Input written to /tmp/mym-input.json")
68	print(f"Category: {data['category']}")
69	print(f"Competitors: {', '.join(data['competitors']) if data['competitors'] else 'none provided'}")
70	PYEOF
71	```
72
73	---
74
75	## Step 3: Run the Standalone Data Collection Script
76
77	The script handles all data collection. Check if it exists first:
78
79	```bash
80	ls scripts/fetch.py 2>/dev/null && echo "script available" \|\| echo "not found"
81	```
82
83	If available, run it:
84
85	```bash
86	GITHUB_TOKEN="${GITHUB_TOKEN:-}" python3 scripts/fetch.py \
87	"$(python3 -c "import json; d=json.load(open('/tmp/mym-input.json')); print(d['category'])")" \
88	--co

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