/blog-discourse

name: blog-discourse
description: >
  Research what people are actually saying about a topic in the last 30 days
  across Reddit, X / Twitter, YouTube, Hacker News, dev.to, Medium, and other
  public discourse platforms. API-free; uses WebSearch with platform-targeted
  site operators plus recency filters. Produces DISCOURSE.md (a structured
  brief) and JSON output the writer can consume. Complements blog-researcher
  (which focuses on authority sources) with a recency-and-engagement lens.
  Not for verifying statistics (blog-factcheck) or gathering authority sources.
  Use when user says "blog discourse", "discourse research", "what are
  people saying about", "research what people are saying", "voice of
  customer", "social listening", "30-day research", "trend research",
  "what's the discussion on", "real-time research", "practitioner discourse",
  "/blog discourse".
user-invocable: true
argument-hint: "<topic> [--days 30|90] [--input results.json] [--output DISCOURSE.md] [--format markdown|json] [--decomposition questions.txt]"
license: MIT

Blog Discourse: Real Discourse Research, API-Free

Bundled paths below use host Markdown substitution of `${CLAUDE_SKILL_DIR}`; it is not an exported shell variable. Resolve them before execution, quote paths, and refuse nonabsolute overrides. Pass `blog_reference_root` resolved from `${CLAUDE_SKILL_DIR}/../blog/references`, `blog_template_root` from `${CLAUDE_SKILL_DIR}/../blog/templates`, and needed sibling roots to agents. Read the main reference `orchestration-details.md` before loading project context; pass only its helper-fenced output to downstream agents.

Produces DISCOURSE.md: a structured brief of what practitioners said about <topic> on the public web in the last 30 days. It is the recency + engagement lens that `blog-researcher` (authority-first) lacks, asking what practitioners and customers are actually saying about this topic right now.

Adapted from the methodology of `last30days-skill` (Matt Van Horn, MIT, https://github.com/mvanhorn/last30days-skill). The upstream uses platform APIs; this sub-skill uses WebSearch with platform-targeted site operators. No API keys required.

Commands

| Command | Purpose | |---|---| | `/blog discourse <topic>` | Produce a discourse brief at project-root `DISCOURSE.md` | | `/blog discourse <topic> --days 90` | Widen the freshness window from 30 to 90 days | | `/blog discourse <topic> --input results.json` | Skip search; build the brief from a pre-gathered results file. The flag name matches `scripts/discourse_research.py --input` directly. | | `/blog discourse <topic> --output path.md` | Write markdown to a chosen output path and print structured JSON without markdown to stdout. | | `/blog discourse <topic> --format json` | Print the full JSON brief to stdout when no `--output` path is used. | | `/blog discourse <topic> --decomposition questions.txt` | Pass newline-delimited decomposition questions into the helper. |

Workflow

Phase 0: Topic Pre-Flight (mandatory)

Before any search, run the four keyword-trap checks from `${CLAUDE_SKILL_DIR}/../blog/references/research-quality.md` (Class 1 demographic shopping, Class 2 numeric trap, Class 3 overly-literal phrase, Class 4 generic single-noun). If the topic matches a class:

1. Emit a single one-line note: `Pre-Flight: matched Class N. Action: <reframe or clarifying question>.` 2. If the action is a clarifying question, STOP and wait for the user. 3. If the action is a reframe, proceed with the reframed query and document the reframe in the brief.

Running discourse research on a trap topic wastes WebSearch calls and produces noise.

Phase 1: Topic Decomposition (Step 0.55)

For named-entity topics, decompose into discrete searchable queries. Use the checklist from `research-quality.md`:

• [ ] Primary entity (official statements, vendor site)
• [ ] Counter-perspective (critics, competitors, contrarians)
• [ ] Practitioner discourse (subreddits, forums, dev.to, Medium)
• [ ] Tangential entities (founder, parent org, related products)
• [ ] Time anchor (last 30 or 90 days)

Emit the decomposition at the top of the eventual brief so reviewers can see the search plan.

Phase 2: Platform-Targeted WebSearch

For each decomposed query, run WebSearch with platform-targeted site operators. Compose 4 to 8 searches total per topic. Use these operators (the agent picks the relevant subset for the topic class):

| Platform | Operator | When to use | |---|---|---| | Reddit | `site:reddit.com/r/<sub>` or `site:reddit.com` | Always (when a relevant sub is known or discoverable) | | Hacker News | `site:news.ycombinator.com` | Tech, dev tools, startup topics | | X / Twitter | `site:x.com` or `site:twitter.com` | Public discourse, influencer takes | | YouTube | `site:youtube.com` | Walkthroughs, reactions, demos | | dev.to | `site:dev.to` | Developer practitioner content | | Medium | `site:medium.com` | Long-form practitioner commentary | | GitHub | `site:github.com` (for issues / discussions) | Open-source projects | | StackOverflow | `site:stackoverflow.com` | Concrete how-to problems | | Substack | `site:substack.com` | Newsletter-form essays |

Always include a recency filter when the platform supports it (Google's `after:YYYY-MM-DD` and `before:YYYY-MM-DD`). For `--days 30`, set `after:` to today minus 30 days. For `--days 90`, today minus 90 days.

Phase 3: Result Collection

For each WebSearch result, capture (into a temporary results JSON file the script can consume):

{
  "platform": "reddit",
  "url": "https://reddit.com/r/xxx/comments/yyy",
  "title": "Original post title as visible in SERP",
  "snippet": "SERP snippet text",
  "date": "YYYY-MM-DD or null",
  "engagement_proxy": "upvote/comment count visible in snippet, or null"
}

Write to a secure temp file (do NOT use a predictable `/tmp/<topic>.json` path; topic names can be sensitive). Create with restrictive permissions:

RESULTS_JSON=$(python3