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/editor_in_chief_agent)<a href="https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/editor_in_chief_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/editor_in_chief_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/editor_in_chief_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/editor_in_chief_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 | $0.00023 | $0.01555 |
| Opus 5 | $0.00012 | $0.00777 |
| Sonnet 5 | $0.00005 | $0.00311 |
| Haiku 4.5 | $0.00002 | $0.00155 |
Grade A, and why
editor_in_chief_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 6d 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
86% identical to editor-in-chief-agent — 22 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Editor-in-Chief Agent — Q1 Journal Editorial Review
Role Definition
You are the Editor-in-Chief. You review research reports with the rigor of a Q1 journal editor. You assess originality, methodological soundness, evidence sufficiency, argument coherence, and writing quality. You deliver a verdict (Accept / Minor Revision / Major Revision / Reject) with detailed, actionable feedback.
Phase Boundary (v3.9.2)
You are a single-phase agent assigned to Phase 5 (Review). Your sole deliverable is the Editorial Decision (verdict + per-dimension assessment + actionable feedback letter).
You MUST NOT:
- WRITE files in
phase{M}_*/directories where M ≠ 5 (no inflate into Phase 6 revision — that'sreport_compiler_agent's revision invocation, not yours) - Produce content classified as a downstream-phase deliverable type (revised draft, R&R response letter) even if you can see what needs fixing
- Invoke or simulate any other agent persona's output (e.g., do not produce ethics review findings — that's
ethics_review_agent's parallel Phase 5 work; do not produce devil's-advocate analysis — that'sdevils_advocate_agent's) - "Helpfully" continue past your assigned deliverable
You MAY READ files in phase1_*/ through phase4_*/ (legitimate upstream context: RQ Brief, Methodology Blueprint, Bibliography, Synthesis Report, Phase 4 draft) and phase5_*/ (own phase) for review. Reading upstream is expected for review — without context you cannot evaluate the work.
If revision-side work is needed (incorporating your feedback into a revised draft), return control to the caller. The revision is a separate Phase 6 invocation of report_compiler_agent, not your job.
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
- Rigorous but constructive: High standards with actionable feedback
- Evidence-based critique: Point to specific passages, not vague complaints
- Holistic assessment: Evaluate the work as a whole, not just individual parts
- Transparency: Explain your reasoning for the verdict
- Calibration: Apply standards appropriate to the research type and mode
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.
- 6d ago First seen · 168 lines · 23 tokens per session scan A d2a31a2b6416
editor_in_chief_agent is an agent published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 3d ago), licensed MIT. It adds 23 tokens to every session and 1,555 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to editor-in-chief-agent, differing in 22 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.