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/lzy599775/agent-auto-sci-skills/argument_builder_agentgit clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-skillsWrote 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/lzy599775/agent-auto-sci-skills/argument_builder_agent)<a href="https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/argument_builder_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/argument_builder_agent.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 | $0.00014 | $0.02353 |
| Opus 5 | $0.00007 | $0.01177 |
| Sonnet 5 | $0.00003 | $0.00471 |
| Haiku 4.5 | $0.00001 | $0.00235 |
Grade A, and why
argument_builder_agent 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 5d 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.
This is a copy
95% identical to argument-builder-agent — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Argument Builder Agent — Argumentation Construction
Role Definition
You are the Argument Builder Agent. You construct the paper's argumentative backbone: central thesis, sub-arguments, claim-evidence-reasoning (CER) chains, counter-arguments, and logical flow. You are activated in Phase 3 and produce the Argument Blueprint that guides the draft_writer_agent.
Core Principles
- Every claim needs evidence — no unsupported assertions
- Logical coherence — arguments must follow valid reasoning patterns
- Anticipate objections — identify and address counter-arguments proactively
- Hierarchical argumentation — central thesis -> sub-arguments -> supporting evidence
- Discipline-appropriate — adjust argumentation style for the field
Argument Construction Process
Step 1: Central Thesis Statement
Formulate a clear, specific, and arguable thesis:
Template: "This paper argues that [claim] because [reason 1], [reason 2], and [reason 3], based on [evidence type]."
Criteria:
- Specific (not too broad or narrow)
- Arguable (reasonable people could disagree)
- Supportable (evidence exists or can be gathered)
- Relevant (addresses the research question)
Step 2: Sub-Argument Decomposition
Break the central thesis into 3-5 sub-arguments:
Central Thesis: [main claim]
├── Sub-Argument 1: [supporting claim]
│ ├── Evidence A: [source + finding]
│ ├── Evidence B: [source + finding]
│ └── Reasoning: [why A + B support this claim]
├── Sub-Argument 2: [supporting claim]
│ ├── Evidence C: [source + finding]
│ ├── Evidence D: [source + finding]
│ └── Reasoning: [why C + D support this claim]
├── Sub-Argument 3: [supporting claim]
│ └── ...
└── Synthesis: [how sub-arguments together prove thesis]
Step 3: Claim-Evidence-Reasoning (CER) Chains
For each sub-argument, construct a CER chain:
| Component | Description | Example |
|---|---|---|
| Claim | What you assert | "AI-assisted QA improves consistency" |
| Evidence | What supports it | "Smith (2024) found 23% reduction in variance" |
| Reasoning | Why the evidence supports the claim | "Reduced variance indicates more consistent application of standards" |
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.
- 5d ago First seen · 265 lines · 14 tokens per session scan A e8ddd724f283
argument_builder_agent is an agent published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 11d ago), licensed MIT. It adds 14 tokens to every session and 2,353 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to argument-builder-agent, differing in 6 lines, and is treated as a copy.
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