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* feat(skills): add systematic-literature-review skill for multi-paper SLR workflows Adds a new skill that produces a structured systematic literature review (SLR) across multiple academic papers on a topic. Addresses #1862 with a pure skill approach: no new tools, no architectural changes, no new dependencies. Skill layout: - SKILL.md — 4+1 phase workflow (plan, search, extract, synthesize, present) - scripts/arxiv_search.py — arXiv API client, stdlib only, with a requests->urllib fallback shim modeled after github-deep-research's github_api.py - templates/{apa,ieee,bibtex}.md — citation format templates selected dynamically in Phase 4, mirroring podcast-generation's templates/ pattern Design notes: - Multi-paper synthesis uses the existing `task` tool to dispatch extraction subagents in parallel. SKILL.md's Phase 3 includes a fixed decision table for batch splitting to respect the runtime's MAX_CONCURRENT_SUBAGENTS = 3 cap, and explicitly tells the agent to strip the "Task Succeeded. Result: " prefix before parsing subagent JSON output. - arXiv only, by design. Semantic Scholar and PubMed adapters would push the scope toward a standalone MCP server (see #933) and are intentionally out of scope for this skill. - Coexists with the existing `academic-paper-review` skill: this skill does breadth-first synthesis across many papers, academic-paper-review does single-paper peer review. The two are routed via distinct triggers and can compose (SLR on many + deep review on 1-2 important ones). - Hard upper bound of 50 papers, tied to the Phase 3 concurrency strategy. Larger surveys degrade in synthesis quality and are better split by sub-topic. BibTeX template explicitly uses @misc for arXiv preprints (not @article), which is the most common mistake when generating BibTeX for arXiv papers. arxiv_search.py was smoke-tested end-to-end against the live arXiv API with two query shapes (relevance sort, submittedDate sort with category filter); all returned JSON fields parse correctly (id normalization, Atom namespace handling, URL encoding for multi-word queries). * fix(skills): prevent LLM from saving intermediate search results to file Adds an explicit "do not save" instruction at the end of Phase 2. Observed during Test 1 with DeepSeek: the model saved search results to a markdown file before proceeding to Phase 3, wasting 2-3 tool call rounds and increasing the risk of hitting the graph recursion limit. The search JSON should stay in context for Phase 3, not be persisted. * fix(skills): use relevance+start-date instead of submittedDate sorting Test 2 revealed that arXiv's submittedDate sorting returns the most recently submitted papers in the category regardless of query relevance. Searching "diffusion models" with sortBy=submittedDate in cs.CV returned papers on spatial memory, Navier-Stokes, and photon-counting CT — none about diffusion models. The LLM then retried with 4 different queries, wasting tool calls and approaching the recursion limit. Fix: always sort by relevance; when the user wants "recent" papers, combine relevance sorting with --start-date to constrain the time window. Also add an explicit "run the search exactly once" instruction to prevent the retry loop. * fix(skills): wrap multi-word arXiv queries in double quotes for phrase matching Without quotes, `all:diffusion model` is parsed by arXiv's Lucene as `all:diffusion OR model`, pulling in unrelated papers from physics (thermal diffusion) and other fields. Wrapping in double quotes forces phrase matching: `all:"diffusion model"`. Also fixes date filtering: the previous bug caused 2011 papers to appear in results despite --start-date 2024-04-09, because the unquoted query words were OR'd with the date constraint. Verified: "diffusion models" --category cs.CV --start-date 2024-04-09 now returns only relevant diffusion model papers published after April 2024. * fix(skills): add query phrasing guide and enforce subagent delegation Two fixes from Test 2 observations with DeepSeek: 1. Query phrasing: add a table showing good vs bad query examples. The script wraps multi-word queries in double quotes for phrase matching, so long queries like "diffusion models in computer vision" return 0 results. Guide the LLM to use 2-3 core keywords + --category instead. 2. Subagent enforcement: DeepSeek was extracting metadata inline via python -c scripts instead of using the task tool. Strengthen Phase 3 to explicitly name the task tool, say "do not extract metadata yourself", and explain why (token budget, isolation). This is more direct than the previous natural-language-only approach while still providing the reasoning behind the constraint. * fix(skills): strengthen search keyword guidance and subagent enforcement Address two issues found during end-to-end testing with DeepSeek: 1. Search retry: LLM passed full topic descriptions as queries (e.g. "diffusion models in computer vision"), which returned 0 results due to exact phrase matching and triggered retries. Added explicit instruction to extract 2-3 core keywords before searching. 2. Subagent bypass: LLM used python -c to extract metadata instead of dispatching via task tool. Added explicit prohibition list (python -c, bash scripts, inline extraction) with ❌ markers for clarity. * fix(skills): address Copilot review feedback on SLR skill - Fix legacy arXiv ID parsing: preserve archive prefix for pre-2007 papers (e.g. hep-th/9901001 instead of just 9901001) - Fix phase count: "four phases" -> "five phases" - Add subagent_enabled prerequisite note to SKILL.md Notes section - Remove PR-specific references ("PR 1") from ieee.md and bibtex.md templates, replace with workflow-scoped wording - Fix script header: "stdlib only" -> "no additional dependencies required", fix relative path to github_api.py reference - Remove reference to non-existent docs/enhancement/ path in header * Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>