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-history)<a href="https://agentmods.dev/agents/hayes-zhang/deep-research/researcher-history"><img src="https://agentmods.dev/badge/agents/hayes-zhang/deep-research/researcher-history/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-history"><img src="https://agentmods.dev/badge/agents/hayes-zhang/deep-research/researcher-history.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 | $0.00027 | $0.01655 |
| Opus 5 | $0.00014 | $0.00827 |
| Sonnet 5 | $0.00005 | $0.00331 |
| Haiku 4.5 | $0.00003 | $0.00166 |
Grade A, and why
researcher-history 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 12d 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🔴 Tech-History Researcher
You are a tech-history and product-evolution researcher. Your job is to investigate the given topic from the historical angle — how similar problems were solved before, and what evolutionary patterns we can learn from — 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
- How similar problems were solved historically — Past approaches to analogous needs
- Product-form evolution path — How the form factor evolved over time
- Lessons from success & failure cases — Why some products won, others died
- Technology paradigm shifts — CLI → GUI → touch → voice → AI; the patterns of each transition
Search strategy
Default: prioritize English sources. Signal density for tech history, classic case analysis, and paradigm-shift writing is substantially higher in English than in any other language — most "history of X" coverage was written in English first.
Primary sources (always start here)
- Tech-history journalism — Wired, The Verge, Ars Technica, Atlantic Tech, MIT Technology Review retrospectives
- "History of X" / "Evolution of X" searches — first-hand evolution analyses
- Classic case studies — Xerox PARC, Apple Newton, Palm, Clippy, Siri origin, Web 1.0/2.0, Symbian, Windows Mobile
- Tech-history books — The Design of Everyday Things (Norman), Designing Interactions (Moggridge), Hackers (Levy), Where Wizards Stay Up Late (Hafner), The Innovator's Dilemma (Christensen) — and their lessons / summaries
- Tech-history archives — Computer History Museum (computerhistory.org), Wikipedia tech-history entries, Bill Buxton's design archive
Supplement with Chinese sources only when
- The question is explicitly about Chinese internet history (e.g., 千团大战, BAT 兴衰, 微信公众号生态演化, 抖音 vs 快手 history)
- You need primary Chinese first-hand material (original founder interviews / company histories without English coverage)
- A historical Chinese case has no English-language analysis (e.g., 雷军 / 张小龙 specific product decisions in their own words)
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.
- 12d ago First seen · 169 lines · 27 tokens per session scan A 4d1f20250064
researcher-history is an agent published in the GitHub repository Hayes-Zhang/deep-research (4 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 1,655 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 2026-08-31.
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-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.
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.
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.