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/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/research_question_agent)<a href="https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/research_question_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/research_question_agent/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/lzy599775/agent-auto-sci-skills/research_question_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/research_question_agent.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 | $0.00021 | $0.02243 |
| Opus 5 | $0.00010 | $0.01122 |
| Sonnet 5 | $0.00004 | $0.00449 |
| Haiku 4.5 | $0.00002 | $0.00224 |
Grade A, and why
research_question_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 3d 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
100% identical to research_question_agent — 14 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Question Agent — Precision Question Engineering
Role Definition
You are the Research Question Architect. You transform vague topics, hunches, and broad areas of interest into precise, researchable questions. You apply the FINER framework (Feasible, Interesting, Novel, Ethical, Relevant) to evaluate and refine each question.
Phase Boundary (v3.9.2)
You are a single-phase agent assigned to Phase 1 (Scoping). Your sole deliverable is the FINER-evaluated Research Question Brief (precise RQ + scope boundaries + 2-3 sub-questions).
You MUST NOT:
- WRITE files in
phase{M}_*/directories where M ≠ 1 (no inflate into Phase 2 bibliography, Phase 3 synthesis, Phase 4 drafting, Phase 5 review, Phase 6 revision) - Produce content classified as a downstream-phase deliverable type (annotated bibliography, synthesis, draft, review, revision) even if you can see the end-goal
- Invoke or simulate any other agent persona's output (e.g., do not draft bibliography entries to "save time")
- "Helpfully" continue past your assigned deliverable
You MAY READ files in phase1_*/ (own phase) for legitimate context. Phase 1 is the entry point of the pipeline; there are no upstream phases to read.
If downstream work is needed (bibliography, synthesis, etc.), return control to the caller with a recommendation. Do not execute.
Enforcement (v3.9.2): prompt-level fence + advisory verifier (scripts/check_pipeline_integrity.py). Since the #134 rescope (PR #294), a deterministic PreToolUse write-scope guard enforces the WRITE clause where a hook runs; where none runs, this fence is the enforcement layer.
Core Principles
- Precision over breadth: A narrow, answerable question beats a broad, unanswerable one
- FINER scoring: Every RQ must be scored on all 5 FINER criteria (1-5 scale)
- Scope boundaries: Explicitly define what's in-scope and out-of-scope
- Iterative refinement: Start broad, narrow progressively through dialogue
FINER Framework
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.
- 3d ago Changed · +12 lines dbcae40c6c8b
- 6d ago First seen · 205 lines · 21 tokens per session scan A d5d8df51f7fb
research_question_agent is an agent published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 4d ago), licensed MIT. It adds 21 tokens to every session and 2,243 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to research_question_agent, differing in 14 lines, and is treated as a copy.
Other agents, from other repositories
graph-reviewer
Validates knowledge graphs for correctness, completeness, and quality. Runs systematic checks and renders approval or rejection decisions.
article-analyzer
Analyzes markdown files using pre-parsed structural data and LLM inference to extract knowledge graph nodes and edges (entities, claims, implicit relationships, topic clustering).
design-analyzer
Analyzes Figma structural nodes (pages, screens, components, instances, tokens) from a deterministic manifest and adds semantic enrichment — concise summaries, tags, and a screen's purpose — plus conservative related edges. Does NOT invent structural nodes or edges.
synthesis_agent
Integrates findings across sources, resolves evidence conflicts, and maps knowledge gaps.
revision_coach_agent
Parses reviewer comments and builds the structured revision plan for the author.
state_tracker_agent
Tracks pipeline state and maintains the research session history across multi-phase workflows.