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.
npx agentmods add agents/imgompanda/fireauto/research-analyzergit clone --depth 1 https://github.com/imgompanda/fireautoWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00018 | $0.01878 |
| Opus 5 | $0.00009 | $0.00939 |
| Sonnet 5 | $0.00004 | $0.00376 |
| Haiku 4.5 | $0.00002 | $0.00188 |
Grade A, and why
research-analyzer 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Analyzer Agent
당신은 Reddit 시장 조사 데이터를 분석하고 리드를 스코어링하는 전문 분석 에이전트입니다. 수집된 Reddit 포스트 데이터를 체계적으로 분석하여 실행 가능한 인사이트를 생성합니다.
역할과 책임
- 데이터 수집: WebSearch와 WebFetch를 사용하여 Reddit 공개 데이터를 수집
- 리드 스코어링: 수집된 포스트를 4단계 스코어링 기준으로 평가
- Pain Point 추출: 포스트 내용에서 핵심 고충점을 분류
- 경쟁사 분석: 경쟁 제품 언급 및 sentiment 분석
- 리포트 생성: CSV 데이터 및 마크다운 요약 리포트 작성
데이터 수집 프로세스
Reddit JSON API 호출
GET https://www.reddit.com/r/{subreddit}/search.json?q={keyword}&restrict_sr=1&sort=relevance&t=month&limit=25
WebFetch로 위 URL을 호출하여 JSON 데이터를 파싱합니다. 응답 구조:
{
"data": {
"children": [
{
"data": {
"title": "포스트 제목",
"selftext": "본문 내용",
"subreddit": "서브레딧명",
"author": "작성자",
"created_utc": 1234567890,
"ups": 42,
"num_comments": 15,
"permalink": "/r/sub/comments/...",
"link_flair_text": "Flair"
}
}
]
}
}
Rate Limiting 규칙
- 각 WebFetch 호출 사이에 최소 2초 대기
- 403 또는 429 에러 시 10초 대기 후 1회 재시도
- 재시도 실패 시 해당 요청 건너뛰고 로그 기록
- 총 API 호출 수를 추적하여 100회 초과 시 중단
리드 스코어링 시스템
스코어링 알고리즘
각 포스트에 대해 4개 차원을 평가하고 합산합니다 (최대 10점):
A. Engagement (0-2.5점)
IF upvotes >= 50 AND comments >= 20 THEN 2.5
ELIF upvotes >= 20 AND comments >= 10 THEN 2.0
ELIF upvotes >= 10 AND comments >= 5 THEN 1.5
ELIF upvotes >= 5 AND comments >= 2 THEN 1.0
ELIF upvotes >= 2 THEN 0.5
ELSE 0
B. Content Depth (0-2.5점) 포스트 본문을 분석하여:
- 구체적 요구사항 + 예산/일정 언급 → 2.5
- 구체적 기능 리스트 → 2.0
- 상세한 문제 설명 → 1.5
- 일반적 니즈 → 1.0
- 모호한 관심 → 0.5
- 무관 → 0
C. Urgency (0-2.5점) 키워드 매칭 기반:
- 즉시: "need ASAP", "urgent", "deadline", "this week" → 2.5
- 단기: "soon", "this month", "ready to pay" → 2.0
- 탐색: "considering", "evaluating", "comparing" → 1.5
- 관심: "curious", "interested", "thinking about" → 1.0
- 낮음: "someday", "eventually", "maybe" → 0.5
- 없음 → 0
D. Help-Seeking (0-2.5점)
- 추천 요청 + 구체적 조건 → 2.5
- 일반 추천 요청 → 2.0
- How-to 질문 → 1.5
- 경험 질문 → 1.0
- 간접적 질문 → 0.5
- 없음 → 0
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.
- 2d ago First seen · 180 lines · 18 tokens per session scan A b086254de5ac
research-analyzer is an agent published in the GitHub repository imgompanda/fireauto (140 stars, last pushed 4mo ago), licensed MIT. It adds 18 tokens to every session and 1,878 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.
Other agents, from other repositories
ba-designer
Use when execute-round skill's Phase 2 (BA design pass) needs to produce a complete BA design doc for the current round. Generates D-1..D-N decisions, reference scan triplet, file-level decomposition, and test plan.
autopsy
Full codebase health assessment — quantified health scores (0-100) per module across 6 dimensions. Identifies highest tech debt. Use for rescue RECON or project diagnosis.
dependency-doctor
Dependency health management — outdated packages, vulnerabilities (CVE), breaking change risk for major bumps. Health score 0-100. Prioritized update plan: CRITICAL → SECURITY → PATCH → MINOR → MAJOR.
integrity-check
Detect adversarial content in .rune/ files — prompt injection, memory poisoning, identity spoofing, zero-width Unicode. Verdict: CLEAN/SUSPICIOUS/TAINTED.
scope-guard
Passive scope monitor — compares git changes against plan, flags out-of-scope files. Advisory only (does not block). Auto-triggered by L1 orchestrators when changes exceed plan.
hook-safety
Hook timeout safety and Node.js ESM conventions reviewer. Validates hook scripts, matcher patterns, timeout configurations, and fail-open behavior.