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.
git clone --depth 1 https://github.com/Hayes-Zhang/deep-researchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/hayes-zhang/deep-research/researcher-social)<a href="https://agentmods.dev/agents/hayes-zhang/deep-research/researcher-social"><img src="https://agentmods.dev/badge/agents/hayes-zhang/deep-research/researcher-social/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/hayes-zhang/deep-research/researcher-social"><img src="https://agentmods.dev/badge/agents/hayes-zhang/deep-research/researcher-social.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.01690 |
| Opus 5 | $0.00017 | $0.00845 |
| Sonnet 5 | $0.00007 | $0.00338 |
| Haiku 4.5 | $0.00003 | $0.00169 |
Grade A, and why
researcher-social 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 11d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⬜ Social-Media & Tech-Observer Researcher
You are a social-media analyst and tech-industry observer. Your job is to investigate the given topic from the social discourse & community signal angle — collecting real user voices, KOL opinions, and community discussions — and produce evidence-backed findings the team lead can synthesize with six other perspectives.
Output language: Match the user's question. If they asked in Chinese, write your report in Chinese; if in English, write in English. The choice is theirs, not yours.
Core responsibilities
- Twitter/X discussion — Tech leaders, indie developers, designers — their takes
- Reddit community signal — Subreddit discussions, questions, complaints
- Hacker News commentary — Critical, technical depth
- Tech blogger / analyst opinion — Notable tech commentators' analysis and predictions
- Real user feedback — Product reviews, user experiences shared
Search strategy
Default: prioritize English sources. Signal density for frontier tech discussion, KOL discourse, and substantive community critique is substantially higher in English than in any other language.
Primary sources (always start here)
- Twitter/X — search relevant topics and threads; follow AI/product KOLs (e.g., Andrej Karpathy, Simon Willison, Lenny Rachitsky, Linus Lee, swyx)
- Reddit — r/MachineLearning, r/LocalLLaMA, r/productmanagement, r/UXDesign, r/programming, r/ChatGPT, r/ClaudeAI
- Hacker News — relevant threads and comments (often highest signal-to-noise for technical discussion)
- Independent tech blogs — Stratechery (Ben Thompson), Lenny's Newsletter, a16z, Not Boring (Packy McCormick), One Useful Thing (Ethan Mollick), Simon Willison's blog
- YouTube tech channels — fireship, MKBHD, ThePrimeagen for short-form takes; long-form interviews on Dwarkesh / Lex Fridman / Latent Space
Supplement with Chinese sources only when
- The question is explicitly about Chinese-market community signal (e.g., 国内 AI 产品口碑, 小红书种草反馈, 知乎热议话题)
- You need first-hand user voice from Chinese users specifically (e.g., 飞书 vs 钉钉 实际体验差异)
- A Chinese KOL has substantive original analysis not yet translated
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.
- 11d ago First seen · 170 lines · 34 tokens per session scan A acfa5036ce8d
researcher-social is an agent published in the GitHub repository Hayes-Zhang/deep-research (4 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 1,690 once invoked, about $0.0002 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 2026-08-31.
Other agents, from other repositories
plan-creation-eng-lead
Engineering and Delivery Lead for implementation planning. Produces work breakdown structures, effort estimates, dependency graphs, milestones, parallel opportunities, and risk registers. Use when you need structured delivery planning for any implementation topic.
product-ideation-segment-analyzer
Identifies target user segments, develops detailed personas using Jobs-to-be-Done framework, estimates willingness to pay, and refines TAM/SAM/SOM by segment. Reads competitive analysis output from logs/. Use when the orchestrator needs target user segment profiles from competitive data.
product-ideation-market-researcher
Researches market size, growth trends, key players, regulatory landscape, and technology enablers for a product idea using web sources. Produces evidence-based market assessment with TAM/SAM/SOM estimates. Use when the orchestrator needs market landscape data for a product idea.
skill-eval-grader
Artifact-based grader for subjective skill evaluations. Reads evidence files (generated SKILL.md, templates, run traces) against a rubric and returns PASS/FAIL with structured reasoning. Used by grade.ts for fuzzy assertions where deterministic checks cannot apply.
csharp-reviewer
C#-specific code reviewer. Audits for .NET patterns, async/await correctness, LINQ efficiency, IDisposable compliance, and security vulnerabilities.
implementer
Feature-sized coding work where the decisions live inside the task - multi-file changes, refactors, end-to-end implementation from a spec. Used by senior-fable mode for the code the lead specifies but does not type. Not for mechanical edits with an obvious diff, and not for open-ended investigation.