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 skills/psylch/tech-research-skill/tech-researchnpx skills add psylch/tech-research-skill --skill tech-researchgit clone --depth 1 https://github.com/psylch/tech-research-skillWrote 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/skills/psylch/tech-research-skill/tech-research)<a href="https://agentmods.dev/skills/psylch/tech-research-skill/tech-research"><img src="https://agentmods.dev/badge/skills/psylch/tech-research-skill/tech-research.svg" alt="Measured on agentmods" 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 | $0.00122 | $0.04586 |
| Opus 5 | $0.00061 | $0.02293 |
| Sonnet 5 | $0.00024 | $0.00917 |
| Haiku 4.5 | $0.00012 | $0.00459 |
Grade A, and why
tech-research 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 4d 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 — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tech Research
Orchestrate multi-source technical research by dispatching parallel subagents to gather intelligence from X/Twitter (via Grok), GitHub repositories (via DeepWiki), and the web (via WebSearch). Synthesize all findings into a single actionable report.
Architecture: The main agent orchestrates research using one of two modes — lightweight (Task Subagents) or heavyweight (Agent Teammates) — chosen based on research complexity.
Language
Match user's language: Respond in the same language the user uses. If the user writes in Chinese, the entire research report should be in Chinese. If in English, report in English.
Research Mode Selection
Before dispatching any agents, determine the appropriate mode:
| Signal | → Mode |
|---|---|
| Single topic, multiple data sources (Grok + DeepWiki + WebSearch) | Light → Task Subagents |
| Multiple independent topics/competitors needing cross-comparison | Heavy → Agent Teammates |
| Research may produce follow-up questions requiring dynamic re-scoping | Heavy → Agent Teammates |
| Agent count ≥ 4 | Heavy → Agent Teammates |
Light Mode (default for single-topic research)
Dispatch up to 3 Task Subagents (Task with subagent_type: "general-purpose"). Each handles one data source independently. The main agent synthesizes results after all return.
Heavy Mode (for multi-topic / competitive research)
Use TeamCreate to create a research team → TaskCreate for each research task → spawn Agent Teammates (via Task with team_name and name parameters) → coordinate via SendMessage. Teammates can:
- Communicate to avoid duplication ("I found Project A uses the same approach as B — focus on their differentiators")
- Share discoveries across tasks ("The blog post I found compares all 3 frameworks, sending you the link")
- Dynamically adjust scope based on what others have found
When to Use
- Evaluating a technology, library, or framework for adoption
- Comparing alternatives (e.g., "Zustand vs Jotai vs Redux")
- Investigating a GitHub repo's architecture and community reception
- Gauging developer sentiment on a new API, tool, or announcement
- Cross-language research (Chinese/Japanese developer communities)
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 326 lines · 122 tokens per session scan A 2e6022f9e8c5
tech-research is a skill published in the GitHub repository psylch/tech-research-skill (2 stars, last pushed 4mo ago), licensed MIT. It adds 122 tokens to every session and 4,586 once invoked, about $0.0006 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.
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