docs: salvage scientific research skills

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Affaan Mustafa
2026-05-11 08:07:35 -04:00
committed by Affaan Mustafa
parent 0e12267ff2
commit df32d6bea8
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{
"name": "ecc",
"source": "./",
"description": "The most comprehensive Claude Code plugin — 53 agents, 192 skills, 69 legacy command shims, selective install profiles, and production-ready hooks for TDD, security scanning, code review, and continuous learning",
"description": "The most comprehensive Claude Code plugin — 53 agents, 195 skills, 69 legacy command shims, selective install profiles, and production-ready hooks for TDD, security scanning, code review, and continuous learning",
"version": "2.0.0-rc.1",
"author": {
"name": "Affaan Mustafa",

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@@ -1,7 +1,7 @@
{
"name": "ecc",
"version": "2.0.0-rc.1",
"description": "Battle-tested Claude Code plugin for engineering teams — 53 agents, 192 skills, 69 legacy command shims, production-ready hooks, and selective install workflows evolved through continuous real-world use",
"description": "Battle-tested Claude Code plugin for engineering teams — 53 agents, 195 skills, 69 legacy command shims, production-ready hooks, and selective install workflows evolved through continuous real-world use",
"author": {
"name": "Affaan Mustafa",
"url": "https://x.com/affaanmustafa"

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@@ -22,6 +22,11 @@
"plugin": [
"./plugins"
],
"skills": {
"paths": [
"../skills"
]
},
"agent": {
"build": {
"description": "Primary coding agent for development work",

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@@ -1,6 +1,6 @@
# Everything Claude Code (ECC) — Agent Instructions
This is a **production-ready AI coding plugin** providing 53 specialized agents, 192 skills, 69 commands, and automated hook workflows for software development.
This is a **production-ready AI coding plugin** providing 53 specialized agents, 195 skills, 69 commands, and automated hook workflows for software development.
**Version:** 2.0.0-rc.1
@@ -146,7 +146,7 @@ Troubleshoot failures: check test isolation → verify mocks → fix implementat
```
agents/ — 53 specialized subagents
skills/ — 192 workflow skills and domain knowledge
skills/ — 195 workflow skills and domain knowledge
commands/ — 69 slash commands
hooks/ — Trigger-based automations
rules/ — Always-follow guidelines (common + per-language)

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@@ -350,7 +350,7 @@ If you stacked methods, clean up in this order:
/plugin list ecc@ecc
```
**That's it!** You now have access to 53 agents, 192 skills, and 69 legacy command shims.
**That's it!** You now have access to 53 agents, 195 skills, and 69 legacy command shims.
### Dashboard GUI
@@ -1338,7 +1338,7 @@ The configuration is automatically detected from `.opencode/opencode.json`.
|---------|-------------|----------|--------|
| Agents | PASS: 53 agents | PASS: 12 agents | **Claude Code leads** |
| Commands | PASS: 69 commands | PASS: 31 commands | **Claude Code leads** |
| Skills | PASS: 192 skills | PASS: 37 skills | **Claude Code leads** |
| Skills | PASS: 195 skills | PASS: 37 skills | **Claude Code leads** |
| Hooks | PASS: 8 event types | PASS: 11 events | **OpenCode has more!** |
| Rules | PASS: 29 rules | PASS: 13 instructions | **Claude Code leads** |
| MCP Servers | PASS: 14 servers | PASS: Full | **Full parity** |
@@ -1443,7 +1443,7 @@ ECC is the **first plugin to maximize every major AI coding tool**. Here's how e
|---------|------------|------------|-----------|----------|
| **Agents** | 53 | Shared (AGENTS.md) | Shared (AGENTS.md) | 12 |
| **Commands** | 69 | Shared | Instruction-based | 31 |
| **Skills** | 192 | Shared | 10 (native format) | 37 |
| **Skills** | 195 | Shared | 10 (native format) | 37 |
| **Hook Events** | 8 types | 15 types | None yet | 11 types |
| **Hook Scripts** | 20+ scripts | 16 scripts (DRY adapter) | N/A | Plugin hooks |
| **Rules** | 34 (common + lang) | 34 (YAML frontmatter) | Instruction-based | 13 instructions |

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@@ -160,7 +160,7 @@ Copy-Item -Recurse rules/typescript "$HOME/.claude/rules/"
/plugin list ecc@ecc
```
**完成!** 你现在可以使用 53 个代理、192 个技能和 69 个命令。
**完成!** 你现在可以使用 53 个代理、195 个技能和 69 个命令。
### multi-* 命令需要额外配置

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@@ -1,6 +1,6 @@
# Everything Claude Code (ECC) — 智能体指令
这是一个**生产就绪的 AI 编码插件**,提供 53 个专业代理、192 项技能、69 条命令以及自动化钩子工作流,用于软件开发。
这是一个**生产就绪的 AI 编码插件**,提供 53 个专业代理、195 项技能、69 条命令以及自动化钩子工作流,用于软件开发。
**版本:** 2.0.0-rc.1
@@ -147,7 +147,7 @@
```
agents/ — 53 个专业子代理
skills/ — 192 个工作流技能和领域知识
skills/ — 195 个工作流技能和领域知识
commands/ — 69 个斜杠命令
hooks/ — 基于触发的自动化
rules/ — 始终遵循的指导方针(通用 + 每种语言)

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@@ -224,7 +224,7 @@ Copy-Item -Recurse rules/typescript "$HOME/.claude/rules/"
/plugin list ecc@ecc
```
**搞定!** 你现在可以使用 53 个智能体、192 项技能和 69 个命令了。
**搞定!** 你现在可以使用 53 个智能体、195 项技能和 69 个命令了。
***
@@ -1134,7 +1134,7 @@ opencode
|---------|-------------|----------|--------|
| 智能体 | PASS: 53 个 | PASS: 12 个 | **Claude Code 领先** |
| 命令 | PASS: 69 个 | PASS: 31 个 | **Claude Code 领先** |
| 技能 | PASS: 192 项 | PASS: 37 项 | **Claude Code 领先** |
| 技能 | PASS: 195 项 | PASS: 37 项 | **Claude Code 领先** |
| 钩子 | PASS: 8 种事件类型 | PASS: 11 种事件 | **OpenCode 更多!** |
| 规则 | PASS: 29 条 | PASS: 13 条指令 | **Claude Code 领先** |
| MCP 服务器 | PASS: 14 个 | PASS: 完整 | **完全对等** |
@@ -1242,7 +1242,7 @@ ECC 是**第一个最大化利用每个主要 AI 编码工具的插件**。以
|---------|------------|------------|-----------|----------|
| **智能体** | 53 | 共享 (AGENTS.md) | 共享 (AGENTS.md) | 12 |
| **命令** | 69 | 共享 | 基于指令 | 31 |
| **技能** | 192 | 共享 | 10 (原生格式) | 37 |
| **技能** | 195 | 共享 | 10 (原生格式) | 37 |
| **钩子事件** | 8 种类型 | 15 种类型 | 暂无 | 11 种类型 |
| **钩子脚本** | 20+ 个脚本 | 16 个脚本 (DRY 适配器) | N/A | 插件钩子 |
| **规则** | 34 (通用 + 语言) | 34 (YAML 前页) | 基于指令 | 13 条指令 |

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@@ -276,7 +276,10 @@
"paths": [
"skills/deep-research",
"skills/exa-search",
"skills/research-ops"
"skills/research-ops",
"skills/scientific-db-pubmed-database",
"skills/scientific-thinking-literature-review",
"skills/scientific-thinking-scholar-evaluation"
],
"targets": [
"claude",

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@@ -207,6 +207,9 @@
"skills/regex-vs-llm-structured-text/",
"skills/remotion-video-creation/",
"skills/research-ops/",
"skills/scientific-db-pubmed-database/",
"skills/scientific-thinking-literature-review/",
"skills/scientific-thinking-scholar-evaluation/",
"skills/returns-reverse-logistics/",
"skills/rust-patterns/",
"skills/rust-testing/",

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---
name: pubmed-database
description: Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.
origin: community
---
# PubMed Database
Use this skill when a task needs biomedical literature from PubMed rather than
general web search.
## When to Use
- Searching MEDLINE or life-sciences literature.
- Building PubMed queries with MeSH terms, field tags, dates, or article types.
- Looking up PMIDs, abstracts, publication metadata, or related citations.
- Running systematic-review search passes that need repeatable search strings.
- Using NCBI E-utilities directly from Python, shell, or another HTTP client.
## Query Construction
Start with the research question, split it into concepts, then combine concepts
with Boolean operators.
```text
concept_1 AND concept_2 AND filter
synonym_a OR synonym_b
NOT exclusion_term
```
Useful PubMed field tags:
- `[ti]`: title
- `[ab]`: abstract
- `[tiab]`: title or abstract
- `[au]`: author
- `[ta]`: journal title abbreviation
- `[mh]`: MeSH term
- `[majr]`: major MeSH topic
- `[pt]`: publication type
- `[dp]`: date of publication
- `[la]`: language
Examples:
```text
diabetes mellitus[mh] AND treatment[tiab] AND systematic review[pt] AND 2023:2026[dp]
(metformin[nm] OR insulin[nm]) AND diabetes mellitus, type 2[mh] AND randomized controlled trial[pt]
smith ja[au] AND cancer[tiab] AND 2026[dp] AND english[la]
```
## MeSH and Subheadings
Prefer MeSH when the concept has a stable controlled-vocabulary term. Combine
MeSH with title/abstract terms when the topic is new or terminology varies.
Correct subheading syntax puts the subheading before the field tag:
```text
diabetes mellitus, type 2/drug therapy[mh]
cardiovascular diseases/prevention & control[mh]
```
Use `[majr]` only when the topic must be central to the paper. It can improve
precision but may miss relevant work.
## Filters
Publication types:
- `clinical trial[pt]`
- `meta-analysis[pt]`
- `randomized controlled trial[pt]`
- `review[pt]`
- `systematic review[pt]`
- `guideline[pt]`
Date filters:
```text
2026[dp]
2020:2026[dp]
2026/03/15[dp]
```
Availability filters:
```text
free full text[sb]
hasabstract[text]
```
## E-utilities Workflow
NCBI E-utilities supports repeatable API workflows:
1. `esearch.fcgi`: search and return PMIDs.
2. `esummary.fcgi`: return lightweight article metadata.
3. `efetch.fcgi`: fetch abstracts or full records in XML, MEDLINE, or text.
4. `elink.fcgi`: find related articles and linked resources.
Use an email and API key for production scripts. Store API keys in environment
variables, never in committed files or command history.
```python
import os
import time
import requests
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"
def esearch(query: str, retmax: int = 20) -> list[str]:
params = {
"db": "pubmed",
"term": query,
"retmode": "json",
"retmax": retmax,
"tool": "ecc-pubmed-search",
"email": os.environ.get("NCBI_EMAIL", ""),
}
api_key = os.environ.get("NCBI_API_KEY")
if api_key:
params["api_key"] = api_key
response = requests.get(f"{BASE}/esearch.fcgi", params=params, timeout=30)
response.raise_for_status()
time.sleep(0.35)
return response.json()["esearchresult"]["idlist"]
pmids = esearch("hypertension[mh] AND randomized controlled trial[pt] AND 2024:2026[dp]")
print(pmids)
```
For batches, prefer NCBI history server parameters (`usehistory=y`,
`WebEnv`, `query_key`) instead of passing very long PMID lists through URLs.
## Output Discipline
For each search pass, record:
- exact search string
- database searched
- date searched
- filters used
- result count
- export format
- any manual exclusions
Example:
```markdown
| Database | Date searched | Query | Filters | Results |
| --- | --- | --- | --- | ---: |
| PubMed | 2026-05-11 | `sickle cell disease[mh] AND CRISPR[tiab]` | 2020:2026[dp], English | 42 |
```
## Review Checklist
- Are field tags valid PubMed tags?
- Are MeSH terms paired with free-text synonyms for newer topics?
- Is the date range explicit and appropriate?
- Does the search log include enough detail to reproduce the query?
- Are API keys loaded from the environment?
- Does HTTP code call `raise_for_status()` or otherwise handle non-200
responses before parsing?
- Are rate limits respected?
## References
- [PubMed help](https://pubmed.ncbi.nlm.nih.gov/help/)
- [NCBI E-utilities documentation](https://www.ncbi.nlm.nih.gov/books/NBK25501/)
- [NCBI API key guidance](https://support.nlm.nih.gov/kbArticle/?pn=KA-05317)
- NCBI support: <eutilities@ncbi.nlm.nih.gov>

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---
name: literature-review
description: Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
origin: community
---
# Literature Review
Use this skill when the task is to find, screen, synthesize, and cite a body of
academic or technical literature.
## When to Use
- Building a systematic, scoping, or narrative literature review.
- Synthesizing the state of the art for a research question.
- Finding gaps, contradictions, or future-work directions.
- Preparing citation-backed background sections for papers or reports.
- Comparing evidence across peer-reviewed papers, preprints, patents, and
technical reports.
## Review Types
- **Narrative review**: broad synthesis; useful for orientation.
- **Scoping review**: maps concepts, methods, and evidence gaps.
- **Systematic review**: predefined protocol, reproducible search, explicit
screening and exclusion.
- **Meta-analysis**: systematic review plus quantitative effect aggregation.
Ask the user which level of rigor is needed. If unspecified, default to a
scoping review for exploratory work and a systematic review for publication or
clinical claims.
## Workflow
### 1. Define the Question
Convert the prompt into a searchable research question.
For clinical or biomedical work, use PICO:
- Population
- Intervention or exposure
- Comparator
- Outcome
For technical work, use:
- system or domain
- method or intervention
- comparison baseline
- evaluation metric
### 2. Plan the Search
Create a search protocol before collecting sources:
- databases to search
- date range
- languages
- publication types
- inclusion criteria
- exclusion criteria
- exact search strings
Minimum useful database set:
- PubMed for biomedical and life-sciences literature.
- arXiv for CS, math, physics, quantitative biology, and preprints.
- Semantic Scholar or Crossref for broad academic discovery.
- Domain-specific sources when relevant, such as clinical-trial registries,
patent databases, standards bodies, or official technical docs.
### 3. Search and Log Evidence
Keep a search log that makes the review reproducible:
```markdown
| Database | Date searched | Query | Filters | Results | Export |
| --- | --- | --- | --- | ---: | --- |
| PubMed | 2026-05-11 | `("CRISPR"[tiab] OR "Cas9"[tiab]) AND "sickle cell"[tiab]` | 2020:2026, English | 86 | PMID list |
| arXiv | 2026-05-11 | `CRISPR sickle cell gene editing` | q-bio, 2020:2026 | 9 | BibTeX |
```
Save raw IDs, URLs, DOIs, abstracts, and notes separately from the final prose.
### 4. Deduplicate
Deduplicate in this order:
1. DOI
2. PMID or arXiv ID
3. exact title
4. normalized title plus first author and year
Record how many duplicates were removed.
### 5. Screen Sources
Screen in stages:
1. title
2. abstract
3. full text
For systematic work, record exclusion reasons:
- wrong population
- wrong intervention
- wrong outcome
- not primary research
- duplicate
- unavailable full text
- outside date range
### 6. Extract Data
Use a structured extraction table:
```markdown
| Study | Design | Population/Data | Method | Comparator | Outcome | Key finding | Limitations |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Author Year | RCT/cohort/review/etc. | sample or corpus | method | baseline | measured outcome | result | caveat |
```
For technical papers, include dataset, benchmark, metric, baseline, and
reproducibility notes.
### 7. Synthesize
Group evidence by theme rather than summarizing papers one by one.
Useful synthesis lenses:
- strongest evidence
- conflicting evidence
- methodological weaknesses
- population or dataset limits
- recency and replication
- practical implications
- unanswered questions
Separate claims by confidence:
- **High confidence**: replicated, high-quality evidence across sources.
- **Medium confidence**: plausible but limited by sample, method, or recency.
- **Low confidence**: early, speculative, single-source, or weakly measured.
### 8. Verify Citations
Before finalizing:
- verify DOI, PMID, arXiv ID, or official URL
- check author names and publication year
- do not cite a paper for a claim it does not make
- mark preprints as preprints
- distinguish reviews from primary evidence
## Output Template
```markdown
# Literature Review: <Topic>
Generated: <date>
Review type: <narrative | scoping | systematic | meta-analysis>
Search window: <dates>
Databases: <list>
## Research Question
## Search Strategy
## Inclusion and Exclusion Criteria
## Evidence Summary
## Thematic Synthesis
## Gaps and Limitations
## References
## Search Log
```
## Pitfalls
- Do not treat search snippets as evidence.
- Do not mix preprints, reviews, and primary studies without labeling them.
- Do not omit negative or conflicting findings.
- Do not claim systematic-review rigor without a reproducible protocol.
- Do not use a single database for a broad claim unless the scope is explicitly
limited to that database.

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---
name: scholar-evaluation
description: Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
origin: community
---
# Scholar Evaluation
Use this skill to evaluate academic or scientific work with a repeatable rubric.
## When to Use
- Reviewing a research paper, proposal, thesis chapter, or literature review.
- Checking whether claims are supported by cited evidence.
- Evaluating methodology, study design, analysis, or limitations.
- Comparing two or more papers for quality or relevance.
- Producing structured feedback for revision.
## Evaluation Scope
Start by identifying the artifact:
- empirical research paper
- theoretical paper
- technical report
- systematic or narrative literature review
- research proposal
- thesis or dissertation chapter
- conference abstract or short paper
Then choose scope:
- **comprehensive**: all rubric dimensions
- **targeted**: one or two dimensions, such as method or citations
- **comparative**: rank multiple works against the same rubric
## Rubric
Score each applicable dimension from 1 to 5:
- 5: excellent; clear, rigorous, and publication-ready
- 4: good; minor improvements needed
- 3: adequate; meaningful gaps but usable
- 2: weak; substantial revision needed
- 1: poor; major validity or clarity problems
Use `N/A` for dimensions that do not apply.
### 1. Problem and Research Question
- Is the problem clear and specific?
- Is the contribution meaningful?
- Are scope and assumptions explicit?
- Does the question match the claimed contribution?
### 2. Literature and Context
- Is relevant prior work covered?
- Does the work synthesize rather than merely list sources?
- Are gaps accurately identified?
- Are recent and foundational sources balanced?
### 3. Methodology
- Does the method answer the research question?
- Are design choices justified?
- Are variables, datasets, participants, or materials described clearly?
- Could another researcher reproduce the work?
- Are ethical and practical constraints acknowledged?
### 4. Data and Evidence
- Are data sources credible and appropriate?
- Is sample size or corpus coverage adequate?
- Are inclusion, exclusion, and preprocessing decisions documented?
- Are missing data and bias risks discussed?
### 5. Analysis
- Are statistical, qualitative, or computational methods appropriate?
- Are baselines and controls fair?
- Are uncertainty, sensitivity, or robustness checks included when needed?
- Are alternative explanations considered?
### 6. Results and Interpretation
- Are results clearly presented?
- Do claims stay within the evidence?
- Are figures, tables, and metrics understandable?
- Are negative or null results handled honestly?
### 7. Limitations and Threats to Validity
- Are limitations specific rather than generic?
- Are internal, external, construct, and conclusion-validity risks addressed?
- Does the paper distinguish speculation from demonstrated results?
### 8. Writing and Structure
- Is the argument easy to follow?
- Are sections organized around the research question?
- Are definitions and notation clear?
- Is the tone precise and scholarly?
### 9. Citations
- Do cited papers support the claims attached to them?
- Are primary sources used where possible?
- Are reviews labeled as reviews?
- Are preprints labeled as preprints?
- Are citation metadata and links correct?
## Review Process
1. Read the abstract, introduction, figures, and conclusion for claimed
contribution.
2. Read methods and results for evidence quality.
3. Check the strongest claims against cited sources.
4. Score each applicable dimension.
5. Separate critical blockers from revision suggestions.
6. End with concrete next edits.
## Output Template
```markdown
# Scholar Evaluation: <Artifact>
## Overall Assessment
- Overall score: <1-5 or N/A>
- Confidence: <high | medium | low>
- Summary: <3-5 sentences>
## Dimension Scores
| Dimension | Score | Evidence | Revision priority |
| --- | ---: | --- | --- |
| Problem and question | | | |
| Literature and context | | | |
| Methodology | | | |
| Data and evidence | | | |
| Analysis | | | |
| Results and interpretation | | | |
| Limitations | | | |
| Writing and structure | | | |
| Citations | | | |
## Critical Issues
## Recommended Revisions
## Evidence Checks Needed
```
## Pitfalls
- Do not use the score as a substitute for concrete feedback.
- Do not penalize a paper for omitting a dimension outside its scope.
- Do not treat citation count, venue, or author reputation as proof of quality.
- Do not accept unsupported claims just because they appear in the abstract.