# Firecrawl Research Patterns > Programmatic Firecrawl usage via the public API, academic paper routing, recursive deep research, and raw corpus persistence. - Skill: `terrylica/firecrawl-research-patterns` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds add terrylica/firecrawl-research-patterns` - Raw SKILL.md: https://api.skillmd.com/api/skills/terrylica/firecrawl-research-patterns/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: terrylica (https://skillmd.com/u/terrylica) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/terrylica/firecrawl-research-patterns --- # Firecrawl Research Patterns Programmatic patterns for using Firecrawl in research workflows — search, scrape, route academic papers, run recursive deep research, and persist raw results for future re-analysis. **Use the public Firecrawl API. There is no self-hosted instance.** `POST https://api.firecrawl.dev/v2/scrape` answers without an API key, which covers the low-volume, occasional conversions this repo actually does. Rate limits and queueing are acceptable — do not stand up a private deployment to avoid them. For archiving AI chat conversations (ChatGPT/Gemini shares), see `Skill(gh-tools:research-archival)`. --- > **Self-Evolving Skill**: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues. ## FIRST — TodoWrite Task Templates **MANDATORY**: Select and load the appropriate template before any research work. ### Intent routing — AI chat share URLs (chatgpt / gemini / claude) AI chat share URLs (`chatgpt.com/share/*`, `chat.openai.com/share/*`, `gemini.google.com/share/*`, `g.co/gemini/share/*`, `claude.ai/share/*`, `claude.ai/chat/*`) can be processed by **either** this skill or `Skill(gh-tools:research-archival)`. Pick by **intent**, not URL pattern: | Your intent | Skill | Output | | ---------------------------------------------------------------------------------- | ------------------------------------ | ------------------------------------------------------------------------------ | | One-off read / extract conversation text for analysis | **This skill** — public API (Sec. 1) | Markdown file on Caddy; no frontmatter, no Issue, no provenance. | | Long-term archive with identity verification, frontmatter, GitHub Issue cross-link | `Skill(gh-tools:research-archival)` | `docs/research/YYYY-MM-DD-{slug}-{type}.md` + issue with Discovery Provenance. | | Already have the file, just need to scrape extra content into the same corpus file | **This skill** | Append-mode workflow under your control. | > **Both paths share the same Firecrawl backend.** `research-archival` calls Firecrawl too — it adds an archival layer on top. There is no scraping capability gap between the two; the difference is what happens to the bytes after they come back. **WebFetch limitation, regardless of intent**: Claude Code hard-blocks `WebFetch` against `chatgpt.com`. Use Firecrawl. **Prefer Firecrawl over Jina Reader for chat shares — Jina silently truncates.** Measured 2026-08-13 on two `chatgpt.com/share/*` URLs, same moment, both returning HTTP 200 with no error: | Link | Firecrawl `v2/scrape` | Jina `r.jina.ai` | Jina coverage | | ---- | ------------------------------------------- | ----------------------------------------- | ------------- | | 1 | 57,616 chars · 76 headings · 128 table rows | 9,397 chars · 12 headings · 22 table rows | **17%** | | 2 | 136,590 chars · 85 headings · 69 table rows | 15,960 chars · 13 headings · 9 table rows | **12%** | Firecrawl reached the true end of both pages (the `Sources` / `ChatGPT is AI and can make mistakes.` footer). Jina stopped mid-sentence — link 1 ended at `"I would spend money on **Synol"`. Neither hit the login wall and Firecrawl's extra bulk was not nav boilerplate (its most-repeated line is blank). **A truncated Jina result looks like a successful short page**, which is the dangerous failure: there is no error to catch. Jina also needs `-H "x-timeout: 30"` at all — the default returns ~321 bytes of login chrome (`"Log in to get answers"`) with only a soft `Warning:` line. Keep Jina as a fallback for simple static pages; do not use it for chat shares or anything JS-rendered. ### Template A — Single Firecrawl Search + Persist ``` 1. No health check needed — the public API has no self-hosted liveness concern. Handle per-request failures instead (Section 1). 2. Execute search — POST /v2/search with query, limit, scrapeOptions 3. Persist raw results — save each result page to docs/research/corpus/ with frontmatter 4. Update corpus index — append entries to docs/research/corpus-index.jsonl 5. Extract findings — summarize key learnings from raw corpus files ``` ### Template B — Academic Paper Retrieval + Persist ``` 1. Identify source — classify URL/DOI per academic-paper-routing.md decision tree 2. Route to scraper — arxiv direct HTML, Semantic Scholar API, Firecrawl, or Jina Reader 3. Scrape content — execute fetch with appropriate method and timeout 4. Persist raw result — save to docs/research/corpus/ with academic-specific frontmatter 5. Update corpus index — append entry to corpus-index.jsonl 6. Summarize paper — extract key claims, methods, results from raw corpus file ``` ### Template C — Full Recursive Deep Research with Corpus ``` 1. No health check needed — the public API has no self-hosted liveness concern. Handle per-request failures instead (Section 1). 2. Initialize parameters — set breadth (default 4), depth (default 2), concurrency (default 2) 3. Generate search queries — LLM generates N queries from topic + prior learnings 4. Execute searches — Firecrawl /v2/search for each query via p-limit(concurrency) 5. Persist raw results — save ALL scraped pages to docs/research/corpus/ with provenance 6. Extract learnings — LLM extracts key findings + follow-up questions per result set 7. Recurse — for each follow-up, recurse with breadth=ceil(breadth/2), depth=depth-1 8. Base case — depth=0, return accumulated learnings 9. Synthesize report — LLM generates final markdown from all learnings 10. Write session report — save to docs/research/sessions/ with corpus file references 11. Update corpus index — append all new entries to corpus-index.jsonl ``` ### Template D — Corpus Review / Re-Analysis ``` 1. Inventory corpus — read docs/research/corpus-index.jsonl, filter by session/topic/date 2. Read raw files — load matching corpus files from docs/research/corpus/ 3. Re-analyze — extract new insights with current context/questions 4. Update session report — amend or create new session report in docs/research/sessions/ ``` ### Template E — Image-Rich Paper with Inline Figures Use when paper contains architecture diagrams, result plots, attention maps, or any critical visual content. ``` 1. Scrape text — use the public API (`/v2/scrape`, preserves absolute image URLs) or Jina fallback 2. Detect figures — scan scraped markdown for ![alt](URL) patterns with .png/.jpg/.svg 3. Extract figure URLs — for arXiv: probe https://arxiv.org/html/{id}v{n}/x{N}.png until 404 4. Keep URLs inline — DO NOT rewrite to local relative paths (breaks GitHub rendering) 5. Ensure inline embedding — markdown body must have ![Figure N](absolute-url) for each figure 6. Catalog in frontmatter — add figure_count and figure_urls list (all absolute URLs) 7. Save corpus file — GFM markdown with inline absolute URLs renders on GitHub without hosting 8. Update corpus-index.jsonl — include has_figures: true, figure_count, figure_urls ``` --- ## Section 1 — Programmatic Firecrawl Usage **Endpoint**: the public API at `https://api.firecrawl.dev`. No API key, no host, no tunnel — it answers unauthenticated. ```bash curl -sS -X POST https://api.firecrawl.dev/v2/scrape \ -H 'Content-Type: application/json' \ -d '{"url":"","formats":["markdown"],"waitFor":8000,"timeout":60000}' # -> {"success":true,"data":{"markdown":"..."}} ``` **Always send `waitFor`** for JS-rendered pages (SPAs, chat shares, dashboards). Without it the scrape can return the pre-hydration shell — which for a login-walled SPA is the login chrome, not the content, and it returns HTTP 200 while doing so. A 200 is not proof of extraction; check for content you expect. **Do not resurrect a self-hosted deployment.** One ran on gpu-host-2:3002 (5 containers: api, playwright-service, nuq-postgres, rabbitmq, redis) and was retired 2026-08-13, reclaiming ~18 GB. The public API covers this repo's volume. Standing one up again trades ~18 GB and five long-running containers for rate limits nobody was hitting. ### Why `fetch()` Instead of `@mendable/firecrawl-js` SDK The official SDK uses `jiti` for dynamic imports, which is incompatible with Bun's module resolution. Direct `fetch()` calls are simpler, more reliable, and have zero dependencies. ### Two Endpoints | Endpoint | Purpose | When to Use | | ----------------- | --------------------- | ------------------------------------------------- | | `POST /v2/search` | Search + scrape combo | Research queries — returns multiple scraped pages | | `POST /v2/scrape` | Single URL scrape | Known URL — extract markdown from one page | See [api-endpoint-reference.md](./references/api-endpoint-reference.md) for full request/response contracts. ### Quick Examples Use the public API base. Pull from `$FIRECRAWL_BASE` env var if your project sets one, otherwise hard-code the FQDN: ```typescript const FIRECRAWL_BASE = process.env.FIRECRAWL_BASE ?? "https://api.firecrawl.dev"; ``` **Search** (returns multiple results with markdown): ```typescript const res = await fetch(`${FIRECRAWL_BASE}/v2/search`, { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ query: "mixture of experts scaling laws", limit: 5, scrapeOptions: { formats: ["markdown"] }, }), }); const { data } = await res.json(); // data: [{ url, markdown, metadata }] ``` **Scrape** (single URL): ```typescript const res = await fetch(`${FIRECRAWL_BASE}/v2/scrape`, { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ url: "https://arxiv.org/abs/2401.12345", formats: ["markdown"], waitFor: 3000, // ms — for JS-heavy pages }), }); const { data } = await res.json(); // data: { markdown, metadata } ``` ### Error Handling ```typescript // Always set a timeout const controller = new AbortController(); const timeoutId = setTimeout(() => controller.abort(), 15_000); try { const res = await fetch(url, { ...opts, signal: controller.signal }); if (!res.ok) throw new Error(`Firecrawl: ${res.status} ${res.statusText}`); const json = await res.json(); if (!json.data || (Array.isArray(json.data) && json.data.length === 0)) { // Empty results — not an error, but no content to process } } finally { clearTimeout(timeoutId); } ``` --- ## Section 2 — Academic Paper Routing Route paper retrieval to the most effective method based on source. Full decision tree in [academic-paper-routing.md](./references/academic-paper-routing.md). ### Quick Reference | Source | Best Method | Fallback | | ----------------- | ------------------------------------- | ------------------------- | | arxiv.org | Direct HTML (`/html/ID`) | Firecrawl `/v2/scrape` | | Semantic Scholar | API (`api.semanticscholar.org`) | Firecrawl search by title | | ACL Anthology | Firecrawl `/v2/scrape` | Direct PDF download | | NeurIPS/ICML/ICLR | Firecrawl `/v2/scrape` with `waitFor` | Search by title | | IEEE Xplore | Firecrawl with `waitFor: 3000` | Author's website | | ACM DL | Firecrawl with `waitFor: 3000` | Author's website | | Author blogs | Jina Reader (`r.jina.ai`) | Firecrawl `/v2/scrape` | | Google Scholar | Firecrawl `/v2/search` | Direct search query | ### DOI Resolution ```typescript // DOI → publisher URL → route to appropriate scraper const res = await fetch(`https://doi.org/${doi}`, { redirect: "follow" }); const publisherUrl = res.url; // e.g., https://dl.acm.org/doi/10.1145/... // Then route publisherUrl through the decision tree above ``` --- ## Section 3 — Recursive Research Protocol The iterative search → extract → recurse → synthesize pattern. Full step-by-step protocol in [recursive-research-protocol.md](./references/recursive-research-protocol.md). ### Algorithm Overview ``` deepResearch(topic, breadth=4, depth=2, concurrency=2): 1. Generate N search queries (N = breadth) from topic + prior learnings 2. For each query (via p-limit concurrency): a. Firecrawl /v2/search → get results b. PERSIST each raw result to docs/research/corpus/ c. Extract learnings + follow-up questions 3. For each follow-up question: → Recurse with breadth=ceil(breadth/2), depth=depth-1 4. Base case: depth=0 → return accumulated learnings 5. Synthesize final report from all learnings 6. Write session report to docs/research/sessions/ ``` ### Default Parameters (from working implementation) | Parameter | Default | Max | Rationale | | ------------- | ------- | --- | ------------------------------------------------------- | | `breadth` | 4 | — | Number of parallel search queries per level | | `depth` | 2 | 5 | Recursion levels (depth > 5 yields diminishing returns) | | `concurrency` | 2 | — | Parallel Firecrawl requests (public API — be gentle) | | `limit` | 5 | — | Results per search query | | `timeout` | 15000ms | — | Per-search timeout | ### Token Budget Each search returns up to 5 pages. Trim each page to ~25,000 tokens before LLM processing: ```typescript function trimToTokenLimit(text: string, maxTokens: number): string { if (!text) return ""; const estimatedTokens = Math.ceil(text.length / 3.5); if (estimatedTokens <= maxTokens) return text; const maxChars = Math.floor(maxTokens * 3.5 * 0.8); return text.slice(0, maxChars); } ``` ### Partial Failure Principle **Partial results are better than total failure.** If a query fails, log it and continue with remaining queries. Never abort the entire research session because one query timed out. --- ## Section 4 — Raw Corpus Persistence **Critical principle**: Every Firecrawl-scraped page must be persisted in its **original raw markdown** with provenance metadata. Synthesized reports reference these originals but never replace them. Full format specification in [corpus-persistence-format.md](./references/corpus-persistence-format.md). ### Directory Layout ``` {project-root}/ ├── docs/research/ │ ├── corpus/ # Raw scraped pages (committed) │ │ └── YYYY-MM-DD-{slug}.md # One file per scraped URL │ ├── sessions/ # Research session reports (committed) │ │ └── YYYY-MM-DD-{topic-slug}.md # Synthesized report with corpus refs │ └── corpus-index.jsonl # Append-only registry (committed) ``` ### Corpus File Frontmatter ```yaml --- source_url: https://arxiv.org/html/2401.12345 scraped_at: "2026-02-25T14:30:00Z" scraper: firecrawl firecrawl_endpoint: /v2/search search_query: "mixture of experts scaling" result_index: 2 research_session: "2026-02-25-moe-scaling" depth_level: 1 claude_code_uuid: SESSION_UUID content_tokens_approx: 4200 --- [RAW MARKDOWN FROM FIRECRAWL — NEVER MODIFIED] ``` ### Key Rules 1. Content below `---` is the **exact markdown Firecrawl returned** — no summarization, trimming, or reformatting 2. One file per URL per scrape — if the same URL is scraped in multiple sessions, each gets its own timestamped file 3. File naming: `YYYY-MM-DD-{slug}.md` where slug is kebab-case from page title or URL path (max 60 chars) 4. Session reports in `docs/research/sessions/` reference corpus files by relative path ### Corpus Index (JSONL) ```json { "url": "https://arxiv.org/html/2401.12345", "file": "corpus/2026-02-25-moe-scaling-arxiv-2401-12345.md", "scraped_at": "2026-02-25T14:30:00Z", "session": "2026-02-25-moe-scaling", "tokens": 4200, "scraper": "firecrawl" } ``` ### Why This Matters - **LLM re-analysis**: Future sessions can re-read raw corpus files and extract different insights with better prompts or newer models - **No information loss**: Synthesis drops details; raw files preserve everything Firecrawl captured - **Deduplication awareness**: The JSONL index lets agents skip URLs already in the corpus - **Git-friendly**: Markdown files diff cleanly, JSONL is append-only --- ## Section 5 — Retired: self-hosted operations The self-hosted deployment (gpu-host-2:3002, plus ports 3003/3004 behind Caddy) was **retired 2026-08-13**. Its containers, images and build cache are gone and ~18 GB was reclaimed. Use the public API in Section 1. Do not reintroduce a private deployment for this repo's volume. ## Section 6 — Image and Figure Capture Text-only scrapers (Jina, direct Firecrawl) capture prose but lose architecture diagrams, result plots, and attention maps. For image-rich papers, always capture figures. ### When to Capture Images Capture figures when the paper contains any of: - Architecture diagrams (model structure, attention patterns) - Benchmark/result comparison plots - Qualitative examples (generated outputs, visualizations) - Algorithm flowcharts or pseudocode figures ### arXiv HTML Figure URL Discovery arXiv HTML papers store figures at sequential absolute URLs (`x1.png`, `x2.png`, ...). Probe to discover all figure URLs — do NOT download them locally: ```bash ARXIV_ID="2312.00752" ARXIV_VER="v2" BASE_URL="https://arxiv.org/html/${ARXIV_ID}${ARXIV_VER}" FIGURE_URLS=() # Probe sequential URLs until 404 — collect absolute URLs only for i in $(seq 1 50); do url="${BASE_URL}/x${i}.png" status=$(curl -s -o /dev/null -w "%{http_code}" "$url") if [ "$status" != "200" ]; then echo "Stopped at x${i}.png (${status}) — found ${#FIGURE_URLS[@]} figures" break fi FIGURE_URLS+=("$url") echo "Found: $url" done ``` The collected absolute URLs go directly into the markdown body and frontmatter — no local copies needed. ### Inline Figure Embedding (GFM) Each figure must appear inline in the corpus markdown as an absolute URL so GitHub renders it in-place: ```markdown ## Key Figures ![Figure 1 — Mamba SSM architecture](https://arxiv.org/html/2312.00752v2/x1.png) ![Figure 2 — Selective scan mechanism](https://arxiv.org/html/2312.00752v2/x2.png) ![Figure 3 — Performance vs sequence length](https://arxiv.org/html/2312.00752v2/x3.png) ``` > **Never rewrite to relative paths** like `./figures/x1.png` — relative paths break on GitHub unless images are committed to the same repo. ### Extracting Existing Inline URLs from Scraped Markdown Firecrawl embeds absolute image URLs in the scraped markdown. Extract them for the frontmatter catalog: ```bash CORPUS_FILE="docs/research/corpus/2026-03-13-mamba-ssm.md" # Extract all absolute image URLs already in the markdown grep -oE 'https://[^)]+\.(png|jpg|svg|gif|webp)' "$CORPUS_FILE" | sort -u ``` These URLs are already inline — just copy them into the frontmatter `figure_urls` list. ### Frontmatter for Image-Rich Papers The YAML frontmatter catalogs all figure source URLs for provenance. The markdown body embeds them inline: ```yaml --- source_url: https://arxiv.org/html/2312.00752v2 scraped_at: "2026-03-13T00:00:00Z" scraper: firecrawl tags: [ssm, state-space-model, mamba, sequence-modeling] content_tokens_approx: 4200 has_figures: true figure_count: 12 figure_urls: - https://arxiv.org/html/2312.00752v2/x1.png - https://arxiv.org/html/2312.00752v2/x2.png - https://arxiv.org/html/2312.00752v2/x3.png - https://arxiv.org/html/2312.00752v2/x4.png - https://arxiv.org/html/2312.00752v2/x5.png --- ``` ### Corpus Index Entry with Figures ```json { "url": "https://arxiv.org/html/2312.00752v2", "file": "corpus/2026-03-13-mamba-ssm.md", "scraped_at": "2026-03-13T00:00:00Z", "session": "2026-03-13-mamba-ssm", "scraper": "firecrawl", "has_figures": true, "figure_count": 12, "figure_urls": [ "https://arxiv.org/html/2312.00752v2/x1.png", "https://arxiv.org/html/2312.00752v2/x2.png" ] } ``` ### Firecrawl vs Jina Reader: Empirical Comparison (arXiv) **Validated on arXiv:2312.00752v2 (Mamba paper) — both scrapers running, same URL:** | Scraper | Bytes | Lines | Words | Figures (absolute inline) | Math on GitHub | | ------------------------ | ------ | ----- | ------ | ------------------------- | -------------------------------------- | | Firecrawl `/v2/scrape` | 99,104 | 1,267 | 13,182 | 13 ✅ | ❌ doubled Unicode+LaTeX, no `$...$` | | Jina Reader | 84,832 | 596 | 10,761 | 12 ✅ | ❌ doubled Unicode+LaTeX, no `$...$` | | Pandoc from LaTeX source | — | — | — | via `\includegraphics` | ✅ `$inline$` + ` ```math ``` ` blocks | **Verdict**: Firecrawl gets **17% more bytes, 2.1× more lines, 22% more words, 1 extra figure** than Jina on this paper — consistent with the far larger gap measured on chat shares. **Both emit absolute inline figure URLs**, so no URL reconstruction is needed from either scraper. **Recommended arXiv workflow**: 1. Firecrawl `POST /v2/scrape` (preferred) — more complete content, figures inline 2. Jina Reader (fallback, static pages only) — 17% less content but still gets absolute figure URLs 3. Probe loop to build `figure_urls` frontmatter catalog regardless of scraper used 4. For human-readable math on GitHub: Pandoc from arXiv LaTeX source (see below) ### Math Rendering: Empirically Validated Approaches **Validated on arXiv:2312.00752v2 (Mamba paper), March 2026.** #### Firecrawl/Jina Math Output: Unreadable on GitHub Both Firecrawl and Jina Reader extract math by doubling content — each equation appears as a Unicode render followed immediately by raw LaTeX source, packed into markdown table cells with `\displaystyle` prefixes and `\\bm{}` escaping. Example from the empirical test: ``` | | h′​(t)\\displaystyle h^{\\prime}(t) | \=𝑨​h​(t)+𝑩​x​(t)\\displaystyle=\\bm{A}h(t)+\\bm{B}x(t) | | (1a) | ``` No `$...$` delimiters — **GitHub cannot render this as math**. The raw LaTeX portion is parseable by an LLM (equations are present), but the output is completely unreadable to humans on GitHub. **For LLM consumption**: Firecrawl's doubled content is sufficient — the LaTeX source is embedded and an LLM can extract it. **For human-readable GitHub rendering**: Use Pandoc from the arXiv LaTeX source tarball (see below). #### Pandoc from arXiv LaTeX Source (Human-Readable Math) Produces proper `$inline$` and ` ```math ``` ` display blocks that GitHub's MathJax/KaTeX renders natively: ```bash ARXIV_ID="2312.00752" # Download arXiv LaTeX source tarball curl -L "https://arxiv.org/src/${ARXIV_ID}" -o "${ARXIV_ID}-src.tar.gz" mkdir -p "${ARXIV_ID}-src" tar xzf "${ARXIV_ID}-src.tar.gz" -C "${ARXIV_ID}-src/" # Find main .tex entry point and section files ls "${ARXIV_ID}-src/"*.tex ls "${ARXIV_ID}-src/src/"*.tex 2>/dev/null # some papers put sections in src/ # Option A: Convert individual section files (safer — avoids macro parse errors) pandoc "${ARXIV_ID}-src/src/background.tex" \ --to gfm+tex_math_dollars \ --wrap=none \ -o "${ARXIV_ID}-background.md" # Option B: Convert full main.tex (may fail on custom macros like \iftoggle) pandoc "${ARXIV_ID}-src/main.tex" \ --to gfm+tex_math_dollars \ --wrap=none \ -o "${ARXIV_ID}-pandoc.md" ``` Install: `brew install pandoc`. Works on any arXiv paper that publishes LaTeX source (most do). **Pandoc output quality** (empirically validated): - Inline math: `$x(t) \in \R \mapsto y(t) \in \R$` ✅ GitHub renders - Display math: ` ```math\n\begin{align}\nh'(t) &= \A h(t) + \B x(t)\n\end{align}\n``` ` ✅ GitHub renders - Custom macros (`\A`, `\B`, `\R`, `\dt`, `\dA`, `\dB`): ⚠️ **undefined in KaTeX** — macros pass through as-is and may partially fail on GitHub without the preamble's `\newcommand` definitions **Handling custom macros**: Prepend the `\newcommand` block from `main.tex` preamble to the output: ````bash # Extract custom macro definitions from preamble grep '\\newcommand\|\\renewcommand\|\\def ' "${ARXIV_ID}-src/main.tex" > macros.tex # Pandoc does not read preamble macros — include them explicitly in a math block at the top: echo '```math' > preamble-block.md cat macros.tex >> preamble-block.md echo '```' >> preamble-block.md cat preamble-block.md "${ARXIV_ID}-pandoc.md" > "${ARXIV_ID}-with-macros.md" ```` **Known Pandoc parse errors on arXiv LaTeX**: | Error trigger | Cause | Workaround | | -------------------- | ---------------------------------------------- | ----------------------------------------- | | `\iftoggle{arxiv}` | Undefined toggle macro (etoolbox package) | Convert section files instead of main.tex | | `\begin{figure*}` | Two-column figure environment breaks structure | Use `head -N` to avoid broken `\end` tags | | `\bm{}`, `\mathbf{}` | Passes through — may not render in KaTeX | Check paper's macro file for mappings | --- ## Anti-Patterns | # | Anti-Pattern | Why It Fails | Correct Approach | | --- | --------------------------------------------- | ---------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- | | 1 | Using `@mendable/firecrawl-js` SDK | `jiti` dynamic imports break in Bun | Direct `fetch()` calls | | 2 | Searching paywalled sites without `waitFor` | JS SPAs return empty shell | Use `waitFor: 3000` for IEEE, ACM DL | | 3 | Setting depth > 5 | Exponential query explosion, diminishing returns | Cap at depth 5 (`clampDepth()`) | | 4 | No timeout on `fetch()` | Hangs indefinitely on unreachable pages | Always use `AbortController` with 15s timeout | | 5 | Not trimming long page content | Exceeds LLM context window | `trimToTokenLimit(text, 25_000)` per page | | 6 | Aborting on partial failure | Loses all completed work | Log failures, continue with remaining queries | | 7 | Gating a run on a liveness check | The public API has no health endpoint and no host to be down | Handle per-request failures: retry once, then fall back. See Section 1. | | 8 | Saving only synthesis without raw originals | Loses source material, prevents re-analysis | Always persist raw Firecrawl markdown to corpus | | 9 | Rewriting figure URLs to local relative paths | Relative paths like `./figures/x1.png` break on GitHub — images don't render | Keep absolute URLs inline in markdown body (`![Fig](https://arxiv.org/html/{id}/x1.png)`); catalog in frontmatter `figure_urls` list — see Section 6 | --- ## References - [API Endpoint Reference](./references/api-endpoint-reference.md) — `/v2/search` and `/v2/scrape` contracts - [Academic Paper Routing](./references/academic-paper-routing.md) — Decision tree for paper sources - [Recursive Research Protocol](./references/recursive-research-protocol.md) — Step-by-step recursive pattern - [Corpus Persistence Format](./references/corpus-persistence-format.md) — Raw content archival format + directory layout - [Evolution Log](./references/evolution-log.md) — Dated change history for this skill ## Post-Execution Reflection After this skill completes, check before closing: 1. **Did the command succeed?** — If not, fix the instruction or error table that caused the failure. 2. **Did parameters or output change?** — If the underlying tool's interface drifted, update Usage examples and Parameters table to match. 3. **Was a workaround needed?** — If you had to improvise (different flags, extra steps), update this SKILL.md so the next invocation doesn't need the same workaround. Only update if the issue is real and reproducible — not speculative.