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/infraspecdev/tesseract/researchnpx skills add infraspecdev/tesseract --skill researchgit clone --depth 1 https://github.com/infraspecdev/tesseractWhat 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 | $0.00028 | $0.00975 |
| Opus 5 | $0.00014 | $0.00487 |
| Sonnet 5 | $0.00006 | $0.00195 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
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 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Skill
Overview
Research a technical topic and produce a well-sourced document with direct quotes, industry references, and a clear recommendation.
When to Use
- Comparing architectural approaches (monorepo vs multi-repo, REST vs gRPC, etc.)
- Evaluating tools or technologies for adoption
- Building evidence-based ADRs or decision documents
- Answering "what do experts recommend for X?" questions
- Any time the user needs citations and industry backing for a decision
Input
The user provides a topic or question, optionally with:
- Context about their team/project
- Specific concerns to address
- Where to save the output
If not specified, save to docs/ in the current repo.
Workflow
digraph research_flow {
rankdir=TB;
node [shape=box];
input [label="1. Clarify Topic & Scope"];
research [label="2. Research (Parallel Agents)"];
synthesize [label="3. Synthesize Findings"];
write [label="4. Write Document"];
review [label="5. Show Summary to User"];
input -> research;
research -> synthesize;
synthesize -> write;
write -> review;
}
Phase 1: Clarify Topic & Scope
Ask the user (if not already clear):
- What decision or question are they trying to answer?
- Who is the audience? (teammates, leadership, future self)
- Any constraints or preferences to bias toward?
- Where should the doc be saved?
Skip if the user already provided enough context.
Phase 2: Research (Use Parallel Agents)
Launch parallel Task agents to maximize coverage:
- Agent 1: Official sources — Documentation from the primary tools/frameworks involved (e.g., Terraform docs, Atmos docs, AWS docs)
- Agent 2: Industry voices — Blog posts, conference talks, and recommendations from recognized companies and engineers
- Agent 3: Community experience — GitHub discussions, Stack Overflow, Reddit, real-world case studies and post-mortems
Each agent should return:
- Direct quotes with attribution
- Source URLs
- Key data points (scale thresholds, timelines, costs)
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 · 142 lines · 28 tokens per session scan A acee50815931
research is a skill published in the GitHub repository infraspecdev/tesseract (5 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 975 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.
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