Dev Opinions Scan

Collect and synthesize diverse opinions on specific topics from multiple developer communities.

Purpose

Quickly understand diverse perspectives on technical topics:

• Distribution of pros/cons
• Practitioner experiences
• Hidden concerns or advantages
• Unique or notable perspectives

Data Sources

Platform	Method
Reddit	Vendored web-search.mjs (chromux) — Google site:reddit.com + enrichment (post body, comments, score)
X (Twitter)	Vendored web-search.mjs (chromux) — Google site:x.com + enrichment (tweets, likes, replies)
Hacker News	Vendored hn-search.py (python3) — Algolia API, no key needed
Dev.to	Vendored web-search.mjs (chromux) — Google site:dev.to + enrichment (article, comments)
Lobsters	Vendored web-search.mjs (chromux) — Google site:lobste.rs + enrichment (article, comments)
Threads	Vendored web-search.mjs (chromux) — Google site:threads.net + enrichment (posts, replies, likes)
ProductHunt	Vendored ph-search.py (python3) — GraphQL API, requires PRODUCT_HUNT_TOKEN env var

Execution

Step 0: Dependency Check

Run all checks in a single Bash call using shell backgrounding (& + wait). Claude Code executes Bash calls sequentially — multiple Bash tool calls do NOT run in parallel. The only way to parallelize is within one shell invocation.

mkdir -p /tmp/dev-scan-$$

# Kill existing chromux instance (may be non-headless) and relaunch in headless mode
chromux kill 2>/dev/null || true
chromux launch default --headless 2>/dev/null || true

node skills/dev-scan/vendor/chromux-search/web-search.mjs --check > /tmp/dev-scan-$$/web.txt 2>&1 &
python3 skills/dev-scan/vendor/hn-search/hn-search.py --check > /tmp/dev-scan-$$/hn.txt 2>&1 &
python3 skills/dev-scan/vendor/ph-search/ph-search.py --check > /tmp/dev-scan-$$/ph.txt 2>&1 &
wait
echo "=== Web (chromux) ===" && cat /tmp/dev-scan-$$/web.txt
echo "=== HN ===" && cat /tmp/dev-scan-$$/hn.txt
echo "=== ProductHunt ===" && cat /tmp/dev-scan-$$/ph.txt
rm -rf /tmp/dev-scan-$$

Result	Action
web-search --check → available: true	chromux available — Reddit, X, Dev.to, Lobsters all use Google site: + enrichment
web-search --check → available: false	Fall back to WebSearch tool for all Google-based sources
hn-search --check → available: true	Hacker News source available
hn-search --check → available: false	Fall back to WebSearch for HN
ph-search --check → available: true	ProductHunt source available
ph-search --check → available: false	Skip ProductHunt (token not set or invalid)

Report available sources before proceeding. Minimum 1 source required.

Step 1: Query Planning

Note: Step 0 (dependency check) and Step 1 (query planning) are independent — run Step 0 bash commands and perform Step 1 reasoning in the same message to save a round-trip.

1-1. Parse Request

Extract structured components from user request:

• topic: Main subject
• entities: Key product/technology names
• type: comparison | opinion | technology | event

Examples:

• "Developer reactions to React 19" → topic: React 19, entities: [React 19], type: opinion
• "Community opinions on Bun vs Deno" → topic: Bun vs Deno, entities: [Bun, Deno], type: comparison
• "What happened with Redis license" → topic: Redis license, entities: [Redis], type: event

1-2. Query Decomposition

User requests are often complex or conversational. Before generating platform-specific queries, decompose the request into atomic search concepts that search engines can match effectively.

Why this matters: Search engines match keywords, not intent. A verbose question like "Is React 19's use() hook a viable replacement for useEffect patterns in production apps?" will miss threads titled "use() vs useEffect" or "React 19 hooks review". Decomposition bridges this gap.

Process:

1. Extract core entities: Product/technology names exactly as communities write them
2. Generate query variants by search intent:
   • core: The most concise keyword combination (2-4 words)
   • versus: Direct comparison form if applicable ("A vs B")
   • opinion: How people ask about it ("A worth it", "A review", "A experience")
   • technical: Specific feature/aspect if the question targets one ("A feature X")
3. Select best variant per platform (see mapping below)

Example: "Can React 19's use() hook replace the existing useEffect pattern?"

Variant	Query
core	React 19 use hook
versus	use() vs useEffect
opinion	React 19 use hook worth it
technical	React 19 use hook replace useEffect

Example: "Is Cursor worth paying for compared to GitHub Copilot?"

Variant	Query
core	Cursor AI editor
versus	Cursor vs GitHub Copilot
opinion	Cursor worth paying for
technical	(not applicable — no specific feature)

Example: "What happened with the Redis license change"

Variant	Query
core	Redis license
versus	(not applicable)
opinion	Redis license change reaction
technical	Redis SSPL Valkey fork

1-3. Source-Specific Query Mapping

Map the best variant from Step 1-2 to each platform's search behavior. Store all variants — the retry step (Step 2.5) needs alternate queries if the primary returns 0 results.

Source	Variable	Best variant	Retry variant	Platform-specific adjustments
Reddit	Q_REDDIT	versus or opinion	core	Google site:reddit.com — keep "vs", natural phrasing. Enrichment extracts post body + top comments.
X/Twitter	Q_TWITTER	versus or core	opinion	Google site:x.com — short terms. Enrichment extracts tweets + likes + replies.
HN	Q_HN	core or technical	core (shorter)	Drop "vs" — Algolia full-text matches better without.
Dev.to	Q_DEVTO	opinion or versus	core	Google site:dev.to — add context word (comparison/review/guide) for recall.
Lobsters	Q_LOBSTERS	core	core (2 words max)	Google site:lobste.rs — simple terms. Small community, keep broad.
Threads	Q_THREADS	opinion or core	core	Google site:threads.net — short-form posts. Similar to X/Twitter, concise queries work best.
ProductHunt	Q_PH	core	—	Product names only. Drop generic words. Only if PH relevant (see below).

ProductHunt relevance check — PH is a product launch community. Only set Q_PH when the query involves specific products, tools, or SaaS (e.g. "Cursor", "Linear", "Supabase vs Firebase"). Skip PH when the topic is abstract/conceptual (e.g. "microservices best practices", "Rust async patterns", "tech layoffs").

Full example: user asks "claude code vs codex"

Decomposition: core=claude code codex, versus=claude code vs codex, opinion=claude code vs codex worth it

Variable	Variant used	Optimized Query
Q_REDDIT	versus	claude code vs codex
Q_TWITTER	versus	claude code vs codex
Q_HN	core	claude code codex
Q_DEVTO	versus	claude code vs codex comparison
Q_LOBSTERS	core	claude code codex
Q_THREADS	opinion	claude code vs codex
Q_PH	core	claude code codex

Step 1.5: Time Period

Extract time period from user request. Default: month.

User says	TIME_PERIOD	--time value
(nothing)	month	month / m
"last week"	week	week / w
"last few days"	week	week / w
"this year"	year	year / y
"all time"	all	all / a

Use TIME_PERIOD in all search commands below.

Step 2: Search (Two Bash Calls → File-Based)

Split into two phases: API sources in parallel (shell backgrounding), then all Google site: sources sequentially (chromux shares one Chrome instance — simultaneous use causes tab conflicts).

Results go to files, not stdout. Enriched JSON can exceed 50KB — piping to stdout hits Claude Code's output limit. Instead, save to files and use the Read tool to access them. This also serves as a log of the scan.

Both Bash calls must share the same temp directory. Generate a stable RUN_ID once and use it in both calls.

Bash call 1 — API sources (parallel):

SESSION_ID="[session ID from UserPromptSubmit hook]"
RUN_ID="dev-scan-$(date +%s)-$RANDOM"
D="$HOME/.hoyeon/$SESSION_ID/tmp/$RUN_ID"
mkdir -p "$D"
echo "$D" > /tmp/dev-scan-current-dir

python3 skills/dev-scan/vendor/hn-search/hn-search.py "{Q_HN}" --count 10 --comments 5 --time {TIME_PERIOD} --json > "$D/hn.json" 2>"$D/hn.err" &
python3 skills/dev-scan/vendor/ph-search/ph-search.py "{Q_PH}" --count 10 --comments 3 --time {TIME_PERIOD} --json > "$D/ph.json" 2>"$D/ph.err" &
wait

echo "RUN_DIR=$D"
for f in "$D"/*.json; do echo "$(basename $f): $(wc -c < $f) bytes, $(python3 -c "import json,sys; d=json.load(open('$f')); print(len(d) if isinstance(d,list) else 'obj')" 2>/dev/null || echo '?') items"; done

Bash call 2 — Google site: sources (sequential via chromux, same Bash call):

D="$(cat /tmp/dev-scan-current-dir)"

node skills/dev-scan/vendor/chromux-search/web-search.mjs "{Q_REDDIT}" --site reddit.com --time {TIME_SHORT} --count 5 --comments 5 --body 300 --json > "$D/reddit.json" 2>"$D/reddit.err"
node skills/dev-scan/vendor/chromux-search/web-search.mjs "{Q_TWITTER}" --site x.com --time {TIME_SHORT} --count 5 --comments 5 --json > "$D/x.json" 2>"$D/x.err"
node skills/dev-scan/vendor/chromux-search/web-search.mjs "{Q_DEVTO}" --site dev.to --time {TIME_SHORT} --count 5 --comments 5 --body 300 --json > "$D/devto.json" 2>"$D/devto.err"
node skills/dev-scan/vendor/chromux-search/web-search.mjs "{Q_LOBSTERS}" --site lobste.rs --time {TIME_SHORT} --count 5 --comments 5 --json > "$D/lobsters.json" 2>"$D/lobsters.err"
node skills/dev-scan/vendor/chromux-search/web-search.mjs "{Q_THREADS}" --site threads.net --time {TIME_SHORT} --count 5 --comments 5 --body 300 --json > "$D/threads.json" 2>"$D/threads.err"

for f in "$D"/*.json; do echo "$(basename $f): $(wc -c < $f) bytes, $(python3 -c "import json,sys; d=json.load(open('$f')); print(len(d) if isinstance(d,list) else 'obj')" 2>/dev/null || echo '?') items"; done

Reading results: Use the Read tool on each $D/{source}.json file. Read the files with the most items first (Reddit, Dev.to tend to be richest). Skip files with 0 items.

TIME_SHORT mapping: month→m, week→w, year→y, all→a (web-search.mjs uses single-letter time codes).

• Omit any source that failed --check in Step 0 or is not relevant (e.g. skip PH line if Q_PH not set).
• If chromux unavailable, fall back to WebSearch tool with site: filter for all Google-based sources.
• Run Bash call 1 and 2 in the same message (Claude Code sends them sequentially, but this saves a round-trip vs separate messages).
• Do NOT rm -rf "$D" yet — keep the files until synthesis is complete. Clean up after final output.

Step 2.5: Retry Empty Sources

After Step 2, check which sources returned 0 results (empty JSON array []). Empty results often mean the query was too specific or the time window too narrow — not that the community has nothing to say.

Retry strategy (one Bash call for all retries):

1. Switch query variant: Use the retry variant from the Step 1-3 table. For HN, try the shortest core variant (2-3 words). For Lobsters, try just 2 keywords.
2. Broaden time range: If TIME_PERIOD was month, retry with year. If already year or all, skip time broadening.
3. Only retry sources that had 0 results — don't re-search sources that already have data.

D="$(cat /tmp/dev-scan-current-dir)"

# Example: HN returned 0, retry with shorter query + broader time
python3 skills/dev-scan/vendor/hn-search/hn-search.py "{Q_HN_RETRY}" --count 10 --comments 5 --time year --json > "$D/hn.json" 2>"$D/hn.err"

# Example: Lobsters returned 0, retry with 2-word query + broader time
node skills/dev-scan/vendor/chromux-search/web-search.mjs "{Q_LOBSTERS_RETRY}" --site lobste.rs --time y --count 5 --comments 5 --json > "$D/lobsters.json" 2>"$D/lobsters.err"

for f in "$D"/*.json; do echo "$(basename $f): $(wc -c < $f) bytes, $(python3 -c "import json,sys; d=json.load(open('$f')); print(len(d) if isinstance(d,list) else 'obj')" 2>/dev/null || echo '?') items"; done

Skip retry if: The topic is genuinely niche for that platform (e.g., Lobsters has very few posts on commercial tools). Note the skip reason in the output.

Max 1 retry per source. If retry also returns 0, move on.

Source Notes

Source	Tool	Notes
Reddit	web-search.mjs	Google site:reddit.com + enrichment. Extracts: post title, body, author, score, top comments with author/score.
X/Twitter	web-search.mjs	Google site:x.com + enrichment. Extracts: tweets, author, handle, likes, time.
HN	hn-search.py	Algolia API, no key. Stories with points and top comments.
Dev.to	web-search.mjs	Google site:dev.to + enrichment. Extracts: article body, author, tags, comments.
Lobsters	web-search.mjs	Google site:lobste.rs + enrichment. Extracts: article body, author, tags, score, comments.
Threads	web-search.mjs	Google site:threads.net + enrichment. Extracts: posts, author, replies, likes. Requires chromux login.
ProductHunt	ph-search.py	GraphQL API, needs PRODUCT_HUNT_TOKEN. Only for product/tool queries.

Step 3: Synthesize & Present

Deduplicate across sources: If the same URL appears in multiple source results, merge them (keep the richer version with more comments/metadata). Cite by the actual platform (Reddit, X, Dev.to), not "Google".

3-0. Comment-level Sentiment Tagging

For every comment extracted from Reddit, X/Twitter, and Threads (Google site: enriched results), tag sentiment:

Tag	When to apply
positive	Praise, endorsement, excitement, recommendation
negative	Criticism, frustration, warning, discouragement
neutral	Factual statement, question, "it depends"
mixed	Same comment contains both positive and negative points

Use these tags downstream in Opinion Classification and Controversy detection — comments with opposing sentiment on the same subtopic signal controversy.

3-1. Opinion Classification

Classify collected opinions by:

• Pro/Positive: Supporting opinions (aggregate from positive comments)
• Con/Negative: Concerns, criticism, alternatives (aggregate from negative comments)
• Neutral/Conditional: "Only if...", "When used with..." (from neutral/mixed)
• Experience-based: Based on actual production use (any sentiment, but with concrete details)

3-2. Derive Consensus

Identify opinions repeatedly appearing across communities:

• Same point mentioned in 2+ sources = consensus
• Especially high reliability if mentioned in both Reddit and HN
• Prioritize opinions with specific numbers or examples
• Target at least 5 consensus items

3-3. Identify Controversies

Find points where opinions diverge:

• Opposing opinions on same topic
• Threads with active debates
• Topics with many "depends on...", "but actually..." responses
• Target at least 3 controversy points

3-4. Select Notable Perspectives

Find unique or deep insights:

• Logically sound opinions that differ from majority
• Opinions from senior developers or domain experts
• Insights from large-scale project experience
• Edge cases or long-term perspectives others might miss
• Target at least 3 notable perspectives

Output Format

Core Principle: All opinions must have inline source. No opinions without sources. The report is designed for quick scanning AND decision-making — TL;DR first, details after.

## TL;DR

> [1-2 sentence summary of overall community sentiment and the key takeaway.
> e.g. "The community is broadly positive about X, but many suggest Z is a better choice in Y situations."]

## Sentiment Overview

Positive ████████░░ 75% | Negative ██░░░░░░░░ 20% | Neutral █░░░░░░░░░ 5%
Sources: Reddit N, X N, HN N, Dev.to N, Lobsters N, Threads N

---

## Key Findings

### Consensus

1. **[Opinion Title]**
   - [Detailed description]
   - Sources: [Reddit](url), [HN](url)

2. **[Opinion Title]**
   - [Details]
   - Source: [Dev.to](url)

(at least 5)

---

### Controversy

1. **[Controversy Topic]**
   - Pro: "[Quote]" - [Source](url)
   - Con: "[Quote]" - [Source](url)
   - Context: [Why opinions diverge]

(at least 3)

---

### Notable Perspective

1. **[Insight Title]**
   > "[Original quote or key sentence]"
   - [Why this is notable]
   - Source: [Platform](url)

(at least 3)

---

## Decision Signal

- **If you need [topic]**: [Clear recommendation based on majority opinion]
- **Watch out for**: [Top 2-3 risks/concerns frequently mentioned]
- **Alternatives worth considering**: [Other options the community recommends, with context on when they fit better]
- **Confidence**: High/Medium/Low — based on volume and agreement across sources

Sentiment Bar Rules

Calculate sentiment from comment-level tags (Step 3-0). The bar uses block chars:

• █ = 10% filled, ░ = 10% empty
• Round to nearest 5%. Sum must equal 100%.
• Count source items (posts + threads, not individual comments) per platform for the "Sources" line.

Source Citation Rules

• Inline links required: End every opinion with Source: [Platform](url)
• Multiple sources: Sources: [Reddit](url), [HN](url)
• Direct quotes: Use "..." format when possible
• URL accuracy: Only include verified accessible links

Error Handling

Situation	Response
0 results for a source	Retry once with alternate query variant + broader time (Step 2.5). Skip after 2nd failure.
chromux unavailable	Fall back to WebSearch tool with site: filter for all Google-based sources
web-search enrichment timeout on URL	Skip that URL, include remaining results
hn-search failure	Retry with shorter query. Skip HN if retry also fails.
ph-search failure / token missing	Skip ProductHunt, proceed with other sources
Output too large for stdout	Results are in files — use Read tool (already the default approach)
Topic too new	Note insufficient results, suggest related keywords