researcher

A research command that searches public Reddit discussions to find people who may need a product or service and assigns scores to potential leads.

In plain words
What is it for?
Use it to investigate keywords in selected subreddits, measure recent interest, track competitor mentions, and rank possible customers.
Why use it?
It replaces manual searching across many communities with a repeatable way to collect demand signals and identify promising prospects.

Command

Part of the fireauto plugin — 19 skills, 17 commands, 6 agents, 3 hooks, 1 MCP server shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/imgompanda/fireauto/researcher
Clone the repo
git clone --depth 1 https://github.com/imgompanda/fireauto

Or install fireauto, the plugin that ships this one along with the rest of its 19 skills, 17 commands, 6 agents, 3 hooks, 1 MCP server.

Per session 22 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,061 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00022 $0.03061
Opus 5 $0.00011 $0.01530
Sonnet 5 $0.00004 $0.00612
Haiku 4.5 $0.00002 $0.00306

Measured 3d ago against content hash 8c03bfcb4be0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

researcher scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugin/commands/researcher.md · 291 lines

How it starts

The opening of the file, as written. The whole thing — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.

fireauto-research: 레딧 수요조사 + 리드 스코어링 자동화

당신은 레딧 기반 시장 조사 및 리드 스코어링 전문 에이전트입니다. 외부 프록시 도구 없이 WebSearch와 WebFetch만으로 공개 Reddit 데이터를 수집하고 분석합니다.

1단계: 사용자 입력 수집

사용자에게 다음 정보를 요청하세요:

필수 입력

  1. 타겟 키워드 (쉼표로 구분)

    • 예: "SaaS boilerplate, starter kit, landing page template"
  2. 서브레딧 목록 (기본값 제공, 사용자가 추가/변경 가능)

    • 기본값:
      • r/SaaS
      • r/startups
      • r/Entrepreneur
      • r/webdev
      • r/nextjs
      • r/reactjs
      • r/indiehackers
      • r/microsaas
      • r/smallbusiness
      • r/nocode
    • 사용자의 제품/서비스에 맞게 조정
  3. ICP (Ideal Customer Profile) 정의

    • 타겟 고객의 특성 (역할, 회사 규모, 기술 수준 등)
    • 예: "비개발자 창업자, 1-5인 팀, MVP 빠르게 런칭하고 싶은 사람"

선택 입력

  • 검색 기간 (기본값: 최근 30일)
  • 최소 upvote 수 (기본값: 3)
  • 경쟁사 이름 목록 (언급 추적용)

2단계: 3-Stage 파이프라인 실행

Stage 1: Reddit 데이터 수집

각 서브레딧+키워드 조합에 대해 데이터를 수집합니다.

수집 방법:

  1. Reddit 검색 JSON API 활용

    • URL 패턴: https://www.reddit.com/r/{subreddit}/search.json?q={keyword}&restrict_sr=1&sort=relevance&t=month&limit=25
    • WebFetch로 JSON 응답 파싱
    • rate limiting 준수: 요청 간 2초 대기
  2. Reddit RSS 피드 활용 (백업)

    • URL 패턴: https://www.reddit.com/r/{subreddit}/search.rss?q={keyword}&restrict_sr=1&sort=new&t=month
    • JSON API가 차단될 경우 대체
  3. WebSearch 활용 (보조)

    • 쿼리: site:reddit.com/r/{subreddit} "{keyword}"
    • 추가 컨텍스트 및 검증용

수집 데이터 포인트:

  • 포스트 제목
  • 본문 (최대 500자)
  • 서브레딧
  • 작성자 (u/username)
  • 작성일
  • Upvotes
  • 댓글 수
  • 포스트 URL
  • Flair (태그)

중요: Rate Limiting

  • Reddit API 호출 간 최소 2초 간격 유지
  • 한 세션에서 최대 100개 요청
  • 403/429 에러 발생 시 10초 대기 후 재시도 (최대 2회)

Stage 2: AI 리드 스코어링

수집된 각 포스트에 대해 1-10점 리드 스코어를 부여합니다.

스코어링 기준 (각 항목 최대 2.5점, 총 10점):

A. Engagement 지표 (0-2.5점)
점수 기준
2.5 upvotes >= 50 AND 댓글 >= 20
2.0 upvotes >= 20 AND 댓글 >= 10
1.5 upvotes >= 10 AND 댓글 >= 5
1.0 upvotes >= 5 AND 댓글 >= 2
0.5 upvotes >= 2
0 upvotes < 2
B. Content Depth / 구체성 (0-2.5점)
점수 기준
2.5 구체적 요구사항 + 예산/일정 언급
2.0 구체적 기능 요구사항 나열
1.5 문제점을 상세히 설명
1.0 일반적 니즈 표현
0.5 모호한 관심 표현
0 관련 없음

Read the full file on GitHub · 291 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 291 lines · 22 tokens per session scan A 8c03bfcb4be0

Subscribe to this mod's changes

researcher is a command published in the GitHub repository imgompanda/fireauto (140 stars, last pushed 4mo ago), licensed MIT. It adds 22 tokens to every session and 3,061 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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