agentleFS
sunnnybalasunnnybala/Rstack/lit-review/SKILL.md

lit-review

Skill69 starsChanged 7 months agoMITView on GitHub

Find relevant papers across major indices, structured summary, gap analysis. Use when asked to "find papers", "literature review", "what's been done", "related work", or "survey the field". Proactively invoke when the user describes a research idea and needs to understand existing work.

19 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use review-r, validate-bib and 7 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • luischanciSkill

    review-r

    Run the R code review protocol on R scripts. Checks code quality, reproducibility, domain correctness, and professional standards. Produces a report without editing files.

    Also in 4 projects that use this

  • luischanciSkill

    validate-bib

    Validate bibliography entries against citations in all manuscript files. Find missing entries and unused references.

    Also in 4 projects that use this

  • bidodevSkill

    devils-advocate

    Challenges decisions, plans, and code by systematically arguing the opposing side. Surfaces risks, pokes holes in assumptions, and stress-tests thinking before committing. Supports intensity levels (gentle, balanced, ruthless, linus) and works with conversation context or a specific file reference. Trigger on requests like "challenge this", "poke holes", "what could go wrong", "play devil's advocate", "linus mode", or "/devils-advocate".

    Also in 4 projects that use this

  • shanggqmSkill

    data-analysis

    数据分析技能,提供结构化的数据查询、统计分析和报告生成工作流。当用户需要查询数据库、进行数据统计分析、生成分析报告时使用。按规范输出原始数据、计算结果、Markdown报告和HTML可视化报告。

    Also in 4 projects that use this

  • seikaikyoSkill

    interview-me

    需求不明時的意圖萃取訪談:一次一題、每題附上自己的猜測、聽出「真正想要 vs 覺得應該要」,直到能預測使用者反應(約 95% 信心)才動工。適用:需求缺少對象 / 動機 / 成功標準 / 約束,或使用者點名「訪談我」「先確認一下」「我們確定嗎」。明確自足的指示、純資訊查詢、機械性操作不適用。

    Also in 4 projects that use this

  • SentrySkill

    commit

    Create commit messages following Sentry conventions. Use when committing code changes, writing commit messages, or formatting git history. Follows conventional commits with Sentry-specific issue references.

    Also in 4 projects that use this

  • +3 more from luischanci

The file

Show its rules and text

We split this skill into the rules it gives an agent

27 rules. 0 are common, 16 are distinctive, and 11 are very specific to this project.

  • 0

    Common

    Most of the 17 edited versions we read keep these.

  • 16

    Distinctive

    This author's own choices. Worth a look.

  • 11

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If output shows UPGRADE_AVAILABLE <old> <new>: read rstack-upgrade/SKILL.md and follow the "Inline Upgrade Flow".

    line 78
  2. Very specific:

    If output shows JUST_UPGRADED <from> <to>: tell user "Running RStack v{to} (just updated!)" and continue.

    line 79
  3. Very specific:

    If output shows NEEDS_SETUP: tell user "RStack not configured yet.

    line 81
  4. Very specific:

    If TEL_PROMPTED is no: Ask the user about telemetry.

    line 84
  5. Very specific:

    If A: run rstack-config set telemetry community

    line 95
  6. Distinctive:

    If B: ask a follow-up:

    line 97
  7. Very specific:

    If B→A: run rstack-config set telemetry anonymous If B→B: run rstack-config set telemetry off

    line 104
  8. Very specific:

    If B→B: run rstack-config set telemetry off

    line 105
  9. Very specific:

    Read idea.md at the project root if it exists.

    line 120
  10. Very specific:

    If idea.md does not exist, ask the user: "What research area should I survey?

    line 121
  11. Distinctive:

    Use two search methods:

    line 145
  12. Distinctive:

    Search for each query. Focus on finding academic papers, not blog posts or news articles.

    line 148
  13. Distinctive:

    Rate limit handling: Wait 3 seconds between S2 API calls.

    line 159
  14. Distinctive:

    Write each record as you go: Do not accumulate all papers in context — write to the JSONL file after assessing each paper.

    line 185
  15. Distinctive:

    If a paper lacks some fields (no abstract from S2, missing venue), fill what's available and mark missing fields as empty strings.

    line 187
  16. Very specific:

    After all papers are recorded, write a human-readable literature review to lit-review.md at the project root.

    line 193
  17. Distinctive:

    Important: When writing the consolidation, read from the JSONL file rather than relying on context memory.

    line 228
  18. Distinctive:

    If B: generate new search queries focused on the subtopic, repeat Steps 2-4, append new papers to the existing JSONL file, and update the …

    line 255
  19. Very specific:

    If C: update idea.md with the new direction, then re-run from Step 1.

    line 257
  20. Distinctive:

    Never hallucinate papers: Every paper must come from WebSearch or the S2 API.

    line 263
  21. Distinctive:

    Never fabricate citation counts, venues, or authors: If the data isn't available, leave the field empty.

    line 264
  22. Distinctive:

    Deduplicate: If the same paper appears in both WebSearch and S2 results, record it once (prefer the S2 version for structured data).

    line 266
  23. Distinctive:

    Be honest about coverage: If the search only found 5 papers, say so.

    line 267
  24. Distinctive:

    S2 API rate limit (429): Wait 60 seconds, retry. If persistent, switch to WebSearch only.

    line 274
  25. Distinctive:

    S2 API down: Use WebSearch exclusively.

    line 275
  26. Distinctive:

    Zero results from all sources: Ask user to refine or broaden the research idea.

    line 276
  27. Distinctive:

    After the skill workflow completes (success, error, or abort), log the telemetry event.

    line 283

“Common” is measured: kept by at least 6 in 10 of the 17 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Preamble (run first)

# --- GENERATED PREAMBLE START ---
_PROJECT_ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd)
if ! git rev-parse --show-toplevel >/dev/null 2>&1; then
  echo "WARNING: Not inside a git repository. Files will be written to $(pwd)."
fi
mkdir -p ~/.rstack/sessions ~/.rstack/analytics "$_PROJECT_ROOT/.rstack"
touch ~/.rstack/sessions/"$PPID"
find ~/.rstack/sessions -mmin +120 -type f -delete 2>/dev/null || true
_SESSIONS=$(find ~/.rstack/sessions -mmin -120 -type f 2>/dev/null | wc -l | tr -d ' ')
_RSTACK_DIR="$(cd "$(dirname "$0")/.." 2>/dev/null && pwd || echo "$HOME/.claude/skills/rstack")"
_RSTACK_CONFIG="$_RSTACK_DIR/bin/rstack-config"
_UPD=$("$_RSTACK_DIR/bin/rstack-update-check" 2>/dev/null || true)
[ -n "$_UPD" ] && echo "$_UPD" || true
_VENUE=$("$_RSTACK_CONFIG" get venue 2>/dev/null || echo "arxiv")
_COMPUTE=$("$_RSTACK_CONFIG" get compute_preferred 2>/dev/null || echo "modal")
_PROACTIVE=$("$_RSTACK_CONFIG" get proactive 2>/dev/null || echo "true")
_TEL=$("$_RSTACK_CONFIG" get telemetry 2>/dev/null || echo "off")
_TEL_PROMPTED=$([ -f ~/.rstack/.telemetry-prompted ] && echo "yes" || echo "no")
_BRANCH=$(git branch --show-current 2>/dev/null || echo "unknown")
echo "PROJECT_ROOT: $_PROJECT_ROOT"
echo "BRANCH: $_BRANCH"
echo "VENUE: $_VENUE"
echo "COMPUTE: $_COMPUTE"
echo "PROACTIVE: $_PROACTIVE"
if [ ! -f ~/.rstack/.setup-complete ]; then
  echo "NEEDS_SETUP"
fi
_TEL_START=$(date +%s)
_SESSION_ID="$$-$(date +%s)"
echo "TELEMETRY: ${_TEL:-off}"
echo "TEL_PROMPTED: $_TEL_PROMPTED"
if [ "$_TEL" != "off" ]; then
  echo '{"skill":"lit-review","ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","repo":"'$(basename "$(git rev-parse --show-toplevel 2>/dev/null)" 2>/dev/null || echo "unknown")'"}'  >> ~/.rstack/analytics/skill-usage.jsonl 2>/dev/null || true
fi
if [ "$_TEL" != "off" ]; then
  echo '{"skill":"lit-review","ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","session_id":"'"$_SESSION_ID"'","rstack_version":"'"$(cat "$_RSTACK_DIR/VERSION" 2>/dev/null | tr -d "[:space:]" || echo "unknown")"'"}'  > ~/.rstack/analytics/.pending-"$_SESSION_ID" 2>/dev/null || true
fi
for _PF in $(find ~/.rstack/analytics -maxdepth 1 -name '.pending-*' 2>/dev/null); do
  if [ -f "$_PF" ]; then
    _PF_BASE="$(basename "$_PF")"
    _PF_SID="${_PF_BASE#.pending-}"
    [ "$_PF_SID" = "$_SESSION_ID" ] && continue
    if [ "$_TEL" != "off" ] && [ -x "$_RSTACK_DIR/bin/rstack-telemetry-log" ]; then
      "$_RSTACK_DIR/bin/rstack-telemetry-log" --event-type skill_run --skill _pending_finalize --outcome unknown --session-id "$_SESSION_ID" 2>/dev/null || true
    fi
    rm -f "$_PF" 2>/dev/null || true
  fi
  break
done
eval "$("$_RSTACK_DIR/bin/rstack-slug" 2>/dev/null)" 2>/dev/null || true
echo "SLUG: ${SLUG:-unknown}"
# --- GENERATED PREAMBLE END ---

If output shows UPGRADE_AVAILABLE <old> <new>: read rstack-upgrade/SKILL.md and follow the "Inline Upgrade Flow". Then continue with this skill. If output shows JUST_UPGRADED <from> <to>: tell user "Running RStack v{to} (just updated!)" and continue.

If output shows NEEDS_SETUP: tell user "RStack not configured yet. Run /setup first." Then read setup-skill/SKILL.md and follow it inline before continuing.

If TEL_PROMPTED is no: Ask the user about telemetry. Use AskUserQuestion:

Help RStack get better! Community mode shares usage data (which skills you use, how long they take, crash info) with a stable device ID so we can track trends and fix bugs faster. No code, file paths, or repo names are ever sent. Change anytime with rstack-config set telemetry off.

Options:

  • A) Help RStack get better! (recommended)
  • B) No thanks

If A: run rstack-config set telemetry community

If B: ask a follow-up:

How about anonymous mode? Just a counter that helps us know if anyone's out there.

Options:

  • A) Sure, anonymous is fine
  • B) No thanks, fully off

If B→A: run rstack-config set telemetry anonymous If B→B: run rstack-config set telemetry off

Always run:

touch ~/.rstack/.telemetry-prompted

This only happens once. If TEL_PROMPTED is yes, skip this entirely.

Important: Note the PROJECT_ROOT value from the preamble output. All file paths below are relative to this project root directory. When instructions say "write to idea.md", write to {PROJECT_ROOT}/idea.md. When they say .rstack/lit-review.jsonl, write to {PROJECT_ROOT}/.rstack/lit-review.jsonl.


Step 0: Understand the Research Area

  1. Read idea.md at the project root if it exists. This is the research idea to survey.
  2. If idea.md does not exist, ask the user: "What research area should I survey? Describe your idea or topic."
  3. Save the idea to idea.md at the project root if not already saved.
  4. Extract 3-5 key concepts from the idea for search.

Step 1: Generate Search Queries

Convert the research idea into academic search terms. Generate 2-3 queries with different angles:

Query construction rules (from Ignis query-builder-agent pattern):

  • Each query should be 8-20 terms
  • Include: key technical terms, synonyms, core task/domain
  • Vary across queries: one broad, one specific to the method, one specific to the application

Examples:

  • Broad: "mixture of experts language model efficiency scaling"
  • Method-specific: "sparse MoE gating mechanism load balancing transformer"
  • Application-specific: "code generation small language model parameter efficient"

Step 2: Search for Papers

Use two search methods:

Method A — WebSearch (Claude's built-in tool): Search for each query. Focus on finding academic papers, not blog posts or news articles. Good search prefixes: "site:arxiv.org", "site:semanticscholar.org", "NeurIPS 2024", "ICML 2025"

Method B — Semantic Scholar API: For each query, call the S2 API to get structured metadata:

QUERY="mixture+of+experts+language+model+efficiency"
curl -s "https://api.semanticscholar.org/graph/v1/paper/search?query=$QUERY&limit=10&fields=title,authors,year,venue,abstract,externalIds,url,citationCount"

Rate limit handling: Wait 3 seconds between S2 API calls. The free tier allows 100 requests per 5 minutes. If you get a 429 response, wait 60 seconds and retry.

If S2 API fails entirely: Fall back to WebSearch only and note "Structured metadata unavailable — using web search results only."

Target: Find 15-25 relevant papers across both methods. Deduplicate by title.


Step 3: Assess and Record Each Paper

For each paper found, assess it and write a JSONL record to .rstack/lit-review.jsonl (in the project's .rstack/ plumbing directory).

Assessment criteria:

  • Relevance (0-10): How directly does this paper relate to the research idea?
    • 9-10: Directly addresses the same problem with the same or competing approach
    • 7-8: Closely related method or application
    • 5-6: Related but different angle (useful for context)
    • 1-4: Tangentially related (skip recording if below 5)
  • Key findings: 1-2 sentences on what the paper contributes
  • Relationship to idea: How does this paper relate to our specific idea?

JSONL record format (one per line, append to file):

{"v":1,"id":"s2-abc123","title":"Mixture of Experts for Efficient Language Models","authors":["Alice Smith","Bob Jones"],"year":2025,"venue":"NeurIPS","abstract":"We propose...","url":"https://arxiv.org/abs/2025.12345","source":"semantic_scholar","relevance":8,"key_findings":"Sparse MoE reduces compute by 4x with 2% accuracy loss","relationship_to_idea":"Direct competitor — same method, different domain","citation_count":42}

Write each record as you go. Do not accumulate all papers in context — write to the JSONL file after assessing each paper. This keeps context manageable.

If a paper lacks some fields (no abstract from S2, missing venue), fill what's available and mark missing fields as empty strings.


Step 4: Consolidate Literature Review

After all papers are recorded, write a human-readable literature review to lit-review.md at the project root.

Structure (from Ignis consolidate-lit-review-agent pattern):

# Literature Review: {topic}

Generated by /lit-review on {date}

## Background
{2-3 paragraphs: field context, why this area matters, recent trends}

## Key Themes
{Major research directions identified across the papers. Group related papers.}

## Methods Landscape
{What approaches are people using? Compare methodologies across papers.}

## Datasets and Benchmarks
{What evaluation benchmarks are standard? What datasets are commonly used?}

## Limitations and Open Questions
{What gaps exist in the current literature? What hasn't been tried?
This is the most important section — it directly feeds /novelty-check.}

## Implications for Our Work
{How does this literature inform our research idea? What should we build on?
What should we avoid? What baselines should we compare against?}

## References
{Numbered list of all papers with title, authors, year, venue, URL}

Length: 800-1500 words. Only cite papers that are actually in .rstack/lit-review.jsonl (plumbing).

Important: When writing the consolidation, read from the JSONL file rather than relying on context memory. This ensures accuracy.


Step 5: Human Checkpoint

Count papers: total found, highly relevant (relevance >= 7), and the main gap identified.

Use AskUserQuestion:

Project: {project name}, branch: {branch}

Literature review complete. Found {total} papers, {relevant} highly relevant.

Main finding: {one sentence about the key theme} Main gap: {one sentence about what's missing in the literature}

Saved to .rstack/lit-review.jsonl ({total} records) and lit-review.md.

RECOMMENDATION: Choose A to proceed to novelty assessment.

Options:

  • A) Proceed to /novelty-check
  • B) Deepen search — look for more papers about {specific subtopic}
  • C) Revise idea — the literature suggests a different direction
  • D) Done — I just needed the literature review

If B: generate new search queries focused on the subtopic, repeat Steps 2-4, append new papers to the existing JSONL file, and update the lit-review.md.

If C: update idea.md with the new direction, then re-run from Step 1.


Important Rules

  • Never hallucinate papers. Every paper must come from WebSearch or the S2 API. If you're not sure a paper exists, search for it first.
  • Never fabricate citation counts, venues, or authors. If the data isn't available, leave the field empty.
  • Write incrementally. Append each paper to the JSONL as you assess it, don't batch.
  • Deduplicate. If the same paper appears in both WebSearch and S2 results, record it once (prefer the S2 version for structured data).
  • Be honest about coverage. If the search only found 5 papers, say so. Don't pad with marginally relevant work.
  • Zero results? Widen search terms. Try different combinations. If still zero after 3 query variations, ask user to refine the research idea.

Failure Modes

  • S2 API rate limit (429): Wait 60 seconds, retry. If persistent, switch to WebSearch only.
  • S2 API down: Use WebSearch exclusively. Note "S2 unavailable" in lit-review.md.
  • Zero results from all sources: Ask user to refine or broaden the research idea.
  • Too many results (>50): Filter by relevance >= 7 and citation count. Prioritize recent papers (last 3 years) and highly cited papers.

Telemetry (run last)

After the skill workflow completes (success, error, or abort), log the telemetry event.

# --- GENERATED EPILOGUE START ---
_TEL_END=$(date +%s)
_TEL_DUR=$(( _TEL_END - _TEL_START ))
rm -f ~/.rstack/analytics/.pending-"$_SESSION_ID" 2>/dev/null || true
if [ "$_TEL" != "off" ]; then
  echo '{"skill":"lit-review","duration_s":"'"$_TEL_DUR"'","outcome":"OUTCOME","session":"'"$_SESSION_ID"'","ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'"}' >> ~/.rstack/analytics/skill-usage.jsonl 2>/dev/null || true
fi
if [ "$_TEL" != "off" ] && [ -x "$_RSTACK_DIR/bin/rstack-telemetry-log" ]; then
  "$_RSTACK_DIR/bin/rstack-telemetry-log" \
    --skill "lit-review" --duration "$_TEL_DUR" --outcome "OUTCOME" \
    --session-id "$_SESSION_ID" --pipeline-stage "lit-review" 2>/dev/null &
fi
# --- GENERATED EPILOGUE END ---

Replace OUTCOME with success/error/abort based on the workflow result.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.