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Get Started Free →Converts documents and URLs to markdown via tiered fallback (MCP markitdown, native tools, user notice). Use when a skill must ingest PDF, DOCX, or URL content.
.claude/skills/athola-document-conversion/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -19% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 577% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 428% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 49% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 103% | 0% |
Convert documents and URLs to markdown using a three-tier fallback strategy. This skill is infrastructure: consumer skills reference it via dependency rather than reimplementing conversion logic.
markdown for downstream processing
Identify the document type from the URI before converting.
| Extension | Format | Tier 1 | Tier 2 | |-----------|--------|--------|--------| | .pdf | PDF | Yes | Read tool (pages) | | .docx, .doc | Word | Yes | None | | .pptx, .ppt | PowerPoint | Yes | None | | .xlsx, .xls | Excel | Yes | None | | .html, .htm | HTML | Yes | WebFetch | | .csv | CSV | Yes | Read tool | | .json | JSON | Yes | Read tool | | .xml | XML | Yes | Read tool | | .png, .jpg, .jpeg, .gif, .webp | Image | Yes | Read tool (visual) | | .mp3, .wav, .m4a | Audio | Yes | None | | .zip | Archive | Yes | None | | .epub | E-book | Yes | None |
See modules/format-matrix.md for quality comparison across tiers.
To convert a document to markdown:
1. DETECT -- Identify format from URI extension or context
2. TRY -- Tier 1: MCP markitdown (best quality)
3. DEGRADE -- Tier 2: native Claude Code tools (if Tier 1 fails)
4. INFORM -- Tier 3: tell user what's needed (if no coverage)
5. SANITIZE -- Apply content-sanitization (external content)Call the convert_to_markdown MCP tool with the document URI. See modules/uri-construction.md for URI formatting rules.
If the tool is available and succeeds, you have the best possible conversion. Proceed to the SANITIZE step.
If the tool is not available (not found, connection error) or fails, proceed to Tier 2.
Use built-in tools as format-specific fallbacks. See modules/fallback-tiers.md for per-format instructions.
Supported in Tier 2: PDF, HTML, images, CSV, JSON, XML. Not supported in Tier 2: DOCX, PPTX, XLSX, audio, archives, e-books. Proceed to Tier 3 for these.
When neither Tier 1 nor Tier 2 can handle the format:
> I cannot convert this {format} file without the markitdown > MCP server. To enable conversion, add this to .mcp.json: > > json > { > "mcpServers": { > "markitdown": { > "type": "stdio", > "command": "uvx", > "args": ["markitdown-mcp"] > } > } > } > > > Alternatively, convert the file to PDF or HTML first, > which I can read with built-in tools.
All converted content is external. Apply the leyline:content-sanitization checklist:
Consumer skills depend on this skill and reference the protocol by name:
yamldependencies: - leyline:document-conversion
Then in their workflow: "Convert the document using the leyline:document-conversion protocol."
modules/format-matrix.mdmodules/fallback-tiers.mdmodules/uri-construction.mdfirst; Tier 2 (native tools) only if Tier 1 is unavailable or fails; Tier 3 (user notification with install instructions) only if Tier 2 has no coverage for the format
--- EXTERNAL CONTENT [source: <tool>] --- boundary markers and truncated to 2000 words per section before being passed downstream
Tier 2 coverage, the exact Tier 3 user notification template is shown including the .mcp.json snippet
conversion; unknown extension surfaced to the user rather than passed to converters blindly
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→pass | 16,124 | 6,233 | -61% | 1 | 1 | 0% | 3,040 | 2,458 | -19% | 0 | 0 | — |
case-01 | fail→fail | 7,709 | 8,308 | +8% | 1 | 1 | 0% | 422 | 1,760 | +317% | 0 | 0 | — |
case-03 | fail→pass | 3,153 | 16,769 | +432% | 1 | 1 | 0% | 507 | 3,434 | +577% | 0 | 0 | — |
case-04 | fail→fail | 2,144 | 4,817 | +125% | 1 | 1 | 0% | 249 | 1,382 | +455% | 0 | 0 | — |
case-05 | fail→fail | 2,334 | 5,992 | +157% | 1 | 1 | 0% | 328 | 1,645 | +402% | 0 | 0 | — |
case-06 | pass→pass | 3,918 | 27,483 | +601% | 1 | 1 | 0% | 633 | 1,705 | +169% | 0 | 0 | — |
case-07 | fail→pass | 3,795 | 5,996 | +58% | 1 | 1 | 0% | 433 | 2,285 | +428% | 0 | 0 | — |
case-08 | fail→pass | 9,932 | 7,584 | -24% | 1 | 1 | 0% | 1,654 | 2,470 | +49% | 0 | 0 | — |
case-09 | pass→fail | 7,084 | 7,682 | +8% | 1 | 1 | 0% | 1,252 | 1,815 | +45% | 0 | 0 | — |
case-10 | fail→pass | 6,496 | 6,090 | -6% | 1 | 1 | 0% | 1,177 | 2,385 | +103% | 0 | 0 | — |
case-11 | fail→pass | 10,483 | 8,431 | -20% | 1 | 1 | 0% | 1,806 | 1,682 | -7% | 0 | 0 | — |
case-12 | fail→pass | 8,758 | 4,164 | -52% | 1 | 1 | 0% | 1,439 | 1,870 | +30% | 0 | 0 | — |
case-13 | pass→pass | 10,177 | 3,633 | -64% | 1 | 1 | 0% | 1,684 | 1,705 | +1% | 0 | 0 | — |
case-14 | pass→pass | 10,488 | 5,758 | -45% | 1 | 1 | 0% | 1,801 | 2,171 | +21% | 0 | 0 | — |
case-15 | fail→pass | 13,458 | 8,670 | -36% | 1 | 1 | 0% | 2,169 | 2,695 | +24% | 0 | 0 | — |
case-16 | fail→fail | 21,016 | 6,376 | -70% | 1 | 1 | 0% | 1,436 | 2,291 | +60% | 0 | 0 | — |
case-17 | pass→pass | 12,611 | 6,388 | -49% | 1 | 1 | 0% | 2,313 | 2,349 | +2% | 0 | 0 | — |
case-18 | fail→pass | 11,720 | 4,864 | -58% | 1 | 1 | 0% | 1,987 | 1,938 | -2% | 0 | 0 | — |
case-19 | fail→fail | 8,022 | 4,665 | -42% | 1 | 1 | 0% | 1,524 | 2,040 | +34% | 0 | 0 | — |
case-20 | fail→pass | 11,816 | 5,308 | -55% | 1 | 1 | 0% | 1,826 | 2,241 | +23% | 0 | 0 | — |
case-21 | fail→pass | 13,089 | 5,013 | -62% | 1 | 1 | 0% | 2,024 | 1,679 | -17% | 0 | 0 | — |
case-22 | pass→pass | 9,441 | 2,579 | -73% | 1 | 1 | 0% | 1,545 | 1,621 | +5% | 0 | 0 | — |
case-23 | fail→pass | 10,353 | 1,971 | -81% | 1 | 1 | 0% | 1,875 | 1,475 | -21% | 0 | 0 | — |
case-24 | fail→pass | 6,945 | 4,879 | -30% | 1 | 1 | 0% | 407 | 2,020 | +396% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted, and 20 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +50 percentage points is the difference between those two pass rates over the 20 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.