rootcause-mcp: Skill for Claude Code

.claude/skills/pubmed-research-chronicle/SKILL.md

pubmed-research-chronicle is a skill for Claude Code from u9401066/rootcause-mcp. It costs 82 tokens per session (1,785 once invoked), scanned A, original, Apache-2.0.

A tool for keeping a versioned timeline of how a research topic develops. It is designed for biomedical research sources such as PubMed, a database of scientific papers, and can track later changes to the same topic.

In plain words
What is it for?
It is for building research timelines, comparing topics, finding milestones, checking updates since an earlier revision, and writing a narrative summary.
Why use it?
It preserves earlier research summaries so you can see what changed, identify milestones, compare topics, and follow the field over time.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is u9401066/rootcause-mcp's own configuration. It tells Claude Code how to work on rootcause-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything rootcause-mcp configures →

Reuse

Borrowing it

Nothing to install: this file belongs to u9401066/rootcause-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/u9401066/rootcause-mcp/master/.claude/skills/pubmed-research-chronicle/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/u9401066/rootcause-mcp

Made for: Claude Code.

Wrote 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.

agentmods badge for pubmed-research-chronicle

README.md
[![agentmods](https://agentmods.dev/badge/skills/u9401066/rootcause-mcp/pubmed-research-chronicle/github.svg)](https://agentmods.dev/skills/u9401066/rootcause-mcp/pubmed-research-chronicle)
Your own site
<a href="https://agentmods.dev/skills/u9401066/rootcause-mcp/pubmed-research-chronicle"><img src="https://agentmods.dev/badge/skills/u9401066/rootcause-mcp/pubmed-research-chronicle/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.

agentmods 80×15 button for pubmed-research-chronicle

Your own site · 80×15
<a href="https://agentmods.dev/skills/u9401066/rootcause-mcp/pubmed-research-chronicle"><img src="https://agentmods.dev/badge/skills/u9401066/rootcause-mcp/pubmed-research-chronicle.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,785 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00082 $0.01785
Opus 5 $0.00041 $0.00892
Sonnet 5 $0.00016 $0.00357
Haiku 4.5 $0.00008 $0.00178

Measured 10d ago against content hash 8e595d2943dc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

pubmed-research-chronicle 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 10d 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.

.claude/skills/pubmed-research-chronicle/SKILL.md · 149 lines

How it starts

The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.

研究編年史指南

描述

build_research_chronicle 是研究演化的唯一入口,取代了舊的三個 timeline 工具。

和一次性快照最大的差別:chronicle 會以遞增 revision 持久化儲存。之後重跑同一個主題會產生 revision N+1,就能做版本比對,回答「上次之後改變了什麼」。

主軸是時序(線性),分支 (lineage) 是次要組織維度。兩者都是同一份 snapshot 的投影,所以 output="timeline"output="tree" 永遠不會互相矛盾。


快速決策樹

使用者問研究演化?
├── 「這個領域怎麼走到今天」 → build_research_chronicle(topic="...")
├── 「把我剛剛找的整理成脈絡」 → build_research_chronicle(pmids="last", topic="...")
├── 「上次之後有什麼新的」 → read_research_chronicle(action="diff", chronicle_id="...", from_revision=N)
├── 「哪些是里程碑 / 領域分佈」 → read_research_chronicle(action="milestones", chronicle_id="...")
├── 「A 和 B 兩個主題比較」 → read_research_chronicle(action="compare", topics="A,B")
└── 「幫我寫成一段敘述」 → read_research_chronicle(action="narrate", chronicle_id="...", mode="full")

建立與更新

# 建立(重跑同一主題會自動產生 revision N+1)
build_research_chronicle(topic="remimazolam ICU sedation")

# 從上一輪搜尋結果建立
build_research_chronicle(pmids="last", topic="My Reading List")

# 明確接續某個 chronicle
build_research_chronicle(topic="remimazolam", chronicle_id="remimazolam-9f2b1c4d")

回傳的 summary 開頭就是時序主軸 (Chronological Spine),下面才是研究分支。 Chronicle ID 會出現在 summary 裡,後續 read_research_chronicle 都要用它。

輸出格式 (output)

格式 用途
summary 預設。緊湊 Markdown,含時序主軸
timeline 時序投影 JSON
tree 研究脈絡樹 JSON(分支式演化)
graph 型別化 provenance graph(證據溯源)
evidence 去重後的證據表
milestones 里程碑分佈與證據品質統計
mermaid / mindmap 可直接在 VS Code / GitHub 預覽
narrative 有證據支撐的敘述
json 完整 snapshot

不論 output 選哪個,完整 snapshot、所有投影、證據表、里程碑分析與 audit 一律寫入 artifact。


讀取與比對

# 列出已儲存的 chronicles
read_research_chronicle(action="list")

# 讀取某個版本(預設最新)
read_research_chronicle(chronicle_id="remimazolam-9f2b1c4d", output="tree")
read_research_chronicle(chronicle_id="remimazolam-9f2b1c4d", revision=2, output="timeline")

# 版本比對:新增/退場/更新的 entries、證據與分支變化
read_research_chronicle(action="diff", chronicle_id="remimazolam-9f2b1c4d", from_revision=1)

# 里程碑分佈(讀已存證據,不重跑搜尋)
read_research_chronicle(action="milestones", chronicle_id="remimazolam-9f2b1c4d")

# 主題比較(含共用證據分析,最多 5 個)
read_research_chronicle(action="compare", topics="remimazolam,propofol,dexmedetomidine")

Read the full file on GitHub · 149 lines

Changes

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.

  1. 10d ago First seen · 149 lines · 82 tokens per session scan A 8e595d2943dc

Subscribe to this mod's changes

pubmed-research-chronicle is a skill published in the GitHub repository u9401066/rootcause-mcp (0 stars, last pushed 8d ago), licensed Apache-2.0. It adds 82 tokens to every session and 1,785 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

detecting-pv-signals

Computes disproportionality signals — PRR, ROR, EBGM, and IC (BCPNN) — over FAERS / OpenFDA drug-event data to flag potential safety signals. Use when the user wants to mine spontaneous-report data for drug-reaction associations, build a 2x2 contingency table, compute a Proportional Reporting Ratio or Reporting Odds…

maziyarpanahi/openmed · 218 tokens

exporting-to-fhir

Convert OpenMed NER output (entities from openmed.analyzetext) into FHIR R4 resources — Condition, MedicationStatement, Observation — using OpenMed's built-in FHIR R4 export helpers in openmed.clinical.exporters. Covers the verified CodeableConcept builder (coding, codeableconcept, systemuri), deterministic fullUrl…

maziyarpanahi/openmed · 163 tokens

annotating-variants

Annotates VCF variants and normalizes HGVS nomenclature with public, license-free annotators (Ensembl VEP REST, VEP/SnpEff/ANNOVAR offline) and links variants to gnomAD population frequencies and the clinical context OpenMed extracts. Use when the user wants to predict variant consequences, map HGVS to genomic…

maziyarpanahi/openmed · 179 tokens

assembling-fhir-bundles

Package multiple FHIR R4 resources produced from OpenMed output into a single valid transaction Bundle ready to POST to an EHR, using OpenMed's verified bundle assembler openmed.clinical.exporters.fhir.tobundle. Covers deterministic urn:uuid fullUrls, automatic in-Bundle reference rewriting, request blocks…

maziyarpanahi/openmed · 131 tokens

auditing-part11-trails

Generates and verifies 21 CFR Part 11-style audit trails — who/what/when, electronic signatures, and tamper-evidence — for OpenMed pipelines in GxP and clinical-trial (GCP) settings. Use when the user runs OpenMed in a regulated/validated environment and needs an attributable, time-stamped, tamper-evident record of…

maziyarpanahi/openmed · 222 tokens

batch-processing-clinical-text

Run large-scale batch NER, PII extraction, or de-identification over many clinical notes on-device with OpenMed, with sharding, checkpointing, resumability, and append-only JSONL output. Use when the user needs to process a corpus or folder of notes, de-identify a dataset, run NER over thousands of documents, build a…

maziyarpanahi/openmed · 161 tokens