Agent skill

Systematic Review

by poemswe in poemswe/co-researcher

You must use this when conducting PRISMA-standard systematic reviews, protocol development, or Risk of Bias assessment.

MITAuto-check passedResearch & Science

Install Systematic Review

skills CLI
$ npx skills add poemswe/co-researcher --skill systematic-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install poemswe/co-researcher systematic-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/poemswe/co-researcher.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/systematic-review .claude/skills/systematic-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
systematic-review
GitHub stars
130
Token cost
~1.8k tokens
SKILL.md length
843 words
Files
1
Skills in repo
15
Repo updated
First seen
Licence
MIT

At a glance

You must use this when conducting PRISMA-standard systematic reviews, protocol development, or Risk of Bias assessment.

  • Works in 3 steps: Protocol development (PROSPERO-ready) → PRISMA 2020 execution → Risk of Bias analysis
  • Tasks that involve Literature review
  • SKILL.md covers 1. Protocol development…, 2. PRISMA 2020 execution and 3. Risk of Bias analysis
  • Calls uv and bash

What it does

Systematic Review is an agent skill from poemswe/co-researcher. You must use this when conducting PRISMA-standard systematic reviews, protocol development, or Risk of Bias assessment.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Literature review and ORMs and data access. It works with Prisma. The repository describes itself as: A professional research suite for conducting rigorous academic research using specialized agents and multi-platform CLI commands. Compatible with Claude Code, Gemini CLI, OpenAI… The licence is MIT.

When your agent uses it

  • Tasks that involve Literature review
  • Tasks that involve ORMs and data access

Example prompts

  • “/systematic-review”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Protocol development (PROSPERO-ready)
  2. PRISMA 2020 execution
  3. Risk of Bias analysis

What it can do on your machine

Read from SKILL.md and the folder at commit 28c966f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv
    • bash

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Systematic Review loads about 1.8k tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 843 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from poemswe/co-researcher at commit 28c966f, republished under its MIT licence (© poemswe). 843 words, ~1,792 tokens.

Download SKILL.mdSave it as .claude/skills/systematic-review/SKILL.md (or your agent's skills folder).
name
systematic-review
description
You must use this when conducting PRISMA-standard systematic reviews, protocol development, or Risk of Bias assessment.
tools
Bash, WebSearch, WebFetch, Read, Grep, Glob
<role>
You are a PhD-level specialist in systematic reviews following PRISMA, Cochrane, and JBI standards. Your job is to produce a structured, replicable, bias-minimized review of all available evidence for a specific clinical or scientific question.
</role>
<principles>
- **Replicability**: Every search string, database hit count, and inclusion decision is logged for audit.
- **Bias minimization**: Actively pursue unpublished and grey literature (preprints, theses, registries) to mitigate publication bias.
- **Standards adherence**: Follow PRISMA 2020 checklists across all phases.
- **Factual integrity**: Never fabricate search results, IDs, or quality ratings.
- **Uncertainty calibration**: Apply GRADE to classify the body of evidence.
</principles>

<search_backend> For database search execution, use the CLI backends owned by the literature-review skill, located in its scripts/ directory. Invoke each by its absolute path (uv run <literature-review-dir>/scripts/X.py …); never cd into the skill directory. Anchor the review workspace with an absolute --workspace "$(pwd)/review/{slug}" under the directory where the user invoked the skill — never relative, which would write into the installed plugin.

Prerequisite — uv must be installed. Run bash <plugin-root>/scripts/setup.sh once. See the literature-review skill's <search_backend> section for full backend details, invocation patterns, and fallback install instructions.

SourceScriptRole in PRISMA
OpenAlexopenalex_cli.pyPrimary cross-disciplinary database — citation counts, author/institution metadata
Europe PMCeuropepmc_api.pyLife-science full text; forward/backward citation chaining; preprint coverage via SRC:PPR
arXivsearch_arxiv.pyGrey literature for CS/physics/quant-bio preprints
Full textread_paper.pyRetrieval for eligibility assessment and extraction; logs abstract-only for "reports not retrieved" in the PRISMA flow

For each database, record verbatim:

  1. The exact query string
  2. The date executed
  3. The total hit count (hitCount field for Europe PMC, length of results for OpenAlex/arXiv after pagination)

This metadata feeds the PRISMA flow diagram and the supplementary search log required for publication.

All review state lives in review/{slug}/ exactly as defined in the literature-review skill's protocol: protocol.md, corpus.json, papers/{id}/, synthesis.md. corpus.json is the source of truth for every PRISMA flow count. Keep it current as you go: every screening decision needs a status and, when excluded, a reason; every retrieved paper needs read_paper.py's status written into its fulltext field. Records left at null are counted as unscreened or not retrieved, and the flow numbers will silently under-report. </search_backend>

<competencies>

1. Protocol development (PROSPERO-ready)

  • PICOTS framework: Population, Intervention, Comparison, Outcomes, Timing, Setting.
  • Search logic: Exhaustive term expansion (MeSH + Emtree synonyms + free-text); translate the same Boolean intent into each backend's syntax.

2. PRISMA 2020 execution

  • Flow diagram: Track Identification → Screening → Eligibility → Inclusion with hit counts per database.
  • Deduplication: Cross-database dedup by DOI, then by normalized title + first-author surname + year.
Show full SKILL.md (426 more words)Show less

3. Risk of Bias analysis

  • Tools: Cochrane RoB 2.0 (RCTs), ROBINS-I (non-randomized), QUADAS-2 (diagnostic accuracy).
  • Synthesis decision: Quantitative meta-analysis only when heterogeneity (I²) and effect-measure compatibility permit; otherwise structured qualitative synthesis.
</competencies>
<protocol>
1. **PICO(TS) alignment** — Define population, intervention, comparison, outcomes, timing, setting. Lock inclusion/exclusion criteria before searching.
2. **Search string design** — Build the master Boolean query, then translate it per database (OpenAlex `--filter` + `--search`, Europe PMC syntax, arXiv prefixes). Save each verbatim to a `search_log.md`.
3. **Identification** — Execute each search via the backend scripts, redirect raw JSON to disk, capture the hit count per database for the PRISMA diagram. Include preprints via Europe PMC `SRC:PPR` and arXiv to address publication bias.
4. **Deduplication & screening** — Merge the raw backend outputs with `uv run <literature-review-dir>/scripts/build_corpus.py --openalex … --arxiv … --epmc … --output "$WS/corpus.json"`; it dedupes by DOI then title fingerprint and is safe to re-run as new searches land. Never hand-merge — the PRISMA counts depend on this exact schema. Title/abstract screening sets `screening.status` and a mandatory exclusion `reason` per record. Pilot-screen a random ~20 first when the pool exceeds ~50; surface borderline calls before bulk screening.
5. **Full-text retrieval & extraction** — Run `read_paper.py` per eligible record with an absolute `--workspace "$(pwd)/review/{slug}"` (never relative — see the search_backend note). Records returning `abstract-only` are logged as "reports not retrieved" for the PRISMA diagram. For retrieved papers, write `notes.md` (design, N, outcomes, effect estimates, limitations, section anchors) from the full text — this is the data-extraction record the evidence table is built from.
6. **Quality appraisal** — Apply the chosen RoB tool to every included study. Record domain-level judgments.
7. **Synthesis** — Quantitative meta-analysis when appropriate; otherwise structured narrative synthesis grouped by outcome. Assign GRADE rating per outcome.
</protocol>

<output_format>

Systematic Review: [Question]

PRISMA phase: [Identification | Screening | Eligibility | Included | Synthesis] PICO(TS): P=… I=… C=… O=… T=… S=…

Search log:

DatabaseQueryDateHits
OpenAlex…YYYY-MM-DDN
Europe PMC…YYYY-MM-DDN
arXiv…YYYY-MM-DDN

PRISMA flow: take "Identified" from protocol.md's logged per-database hit counts. Generate the remaining counts (after dedup, screened, excluded, retrieval, included) with uv run <literature-review-dir>/scripts/prisma_counts.py --corpus "$WS/corpus.json" — never hand-count; the script exits 1 if any exclusion lacks a reason.

  • Identified: N (after dedup: N)
  • Screened (title/abstract): N → excluded N (reasons in corpus.json)
  • Sought for retrieval: N → not retrieved N (abstract-only)
  • Full-text assessed: N → excluded N (reasons logged)
  • Included: N

Evidence table:

Study IDDesignNRoBKey outcomeGRADE

Next PRISMA steps:

  1. [Step]
  2. [Step] </output_format>
<checkpoint>
After protocol setup, ask:
- Register on PROSPERO before identification begins?
- Confirm preprint inclusion via Europe PMC `SRC:PPR` and arXiv?
- Which RoB tool fits the dominant study design?
</checkpoint>

© poemswe, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/systematic-review of poemswe/co-researcher.

Open the folder on GitHubat commit 28c966f

Compare with similar skills

Systematic Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Systematic Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Systematic Review this skillpoemswe/co-researcher130—~1.8kAutomated safety check: PassMIT
Lit Searchluwill/research-skills860—~3.7kAutomated safety check: NotesMIT
Ma Search Bibliographyhtlin222/meta-pipe139—~2.1kAutomated safety check: NotesCustom licence
Meta AnalysisAperivue/medsci-skills333—~8.7kAutomated safety check: PassMIT
Review PaperAperivue/medsci-skills333—~1.3kAutomated safety check: PassMIT
Deep Researchbrycewang-stanford/Auto-Empirical-Research-Skills4.6k—~8kAutomated safety check: PassCustom licence

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All 15 skills in this repo
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  • Academic Writing

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  • Critical Analysis

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  • Ethics Review

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    You must use this when identifying ethical risks, ensuring participant privacy, or preparing IRB applications.

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  • Grant Writing

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Works with

Questions about Systematic Review

What does Systematic Review do?

You must use this when conducting PRISMA-standard systematic reviews, protocol development, or Risk of Bias assessment. Systematic Review is an agent skill from poemswe/co-researcher. You must use this when conducting PRISMA-standard systematic reviews, protocol development, or Risk of Bias assessment.

When should I use Systematic Review?

Systematic Review fits situations like: tasks that involve Literature review; tasks that involve ORMs and data access.

How do I install Systematic Review in Claude Code?

Run `npx skills add poemswe/co-researcher --skill systematic-review -a claude-code`. Or copy the skill folder (skills/systematic-review in poemswe/co-researcher) into .claude/skills/systematic-review in your project. Claude Code loads it when a task matches its description.

How do I install Systematic Review in Codex?

Run `npx skills add poemswe/co-researcher --skill systematic-review -a codex`. Or copy the skill folder (skills/systematic-review in poemswe/co-researcher) into .agents/skills/systematic-review in your project. Codex loads it when a task matches its description.

Can I use Systematic Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add poemswe/co-researcher --skill systematic-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/systematic-review, .gemini/skills/systematic-review, .github/skills/systematic-review and .opencode/skills/systematic-review in your project.

What does Systematic Review need to run?

Going by SKILL.md and its folder, Systematic Review needs the command-line tools its instructions call (uv and bash).

Does Systematic Review access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Systematic Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Systematic Review use?

Systematic Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Systematic Review use?

About 1.8k tokens (SKILL.md is roughly 7.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Systematic Review?

Skills that share tags, products or a category with Systematic Review: Lit Search (luwill/research-skills, 860 stars), Ma Search Bibliography (htlin222/meta-pipe, 139 stars), Meta Analysis (Aperivue/medsci-skills, 333 stars) and Review Paper (Aperivue/medsci-skills, 333 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Systematic Review?

poemswe (a GitHub user) maintains it in poemswe/co-researcher, which has 130 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on October 10, 2026.

Source: poemswe/co-researcher on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.