Install in Claude Code
Copygit clone --depth 1 https://github.com/Orkas-AI/Orkas /tmp/seo-tech-audit && cp -r /tmp/seo-tech-audit/resources/builtin/marketplace/agents/e064dca9e1bd/skills/seo-tech-audit ~/.claude/skills/seo-tech-auditThen start a new Claude Code session; the skill loads automatically.
Definition
SKILL.md
# seo-tech-audit
Judge the technical SEO facts produced by `seo-crawl` and return falsifiable findings + a health score. This is pure analysis — it does no network I/O and only reasons over the crawl JSON.
## When to use
- The diagnose flow has a `seo-crawl` result and needs technical findings + a health score before writing the report.
- Re-running after an `apply` edit to confirm a finding cleared (the leading_indicator/failure_criterion drive the recheck).
## When NOT to use
- Acquiring page data — that is `seo-crawl` (this skill consumes its output).
- Content quality / E-E-A-T / GEO citability scoring — separate skills.
- Rendering the dashboard or writing the action plan — that is the report skill.
## Preconditions
- A `seo-crawl` JSON object (its `{ "data": { site, pages } }` shape, or the bare `data`).
- Python 3.9+ (stdlib only).
## How to call
```
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" seo-tech-audit audit -- --input <crawl.json> [--out <audit.json>]
```
- `--input` path to the `seo-crawl` JSON (omit or `-` to read stdin).
- `--out` optional path to also write the audit JSON.
## Expected output
JSON on stdout:
```json
{ "ok": true, "data": {
"health_score": 0,
"assessed_dimensions": [ "content_meta", "structure" ],
"not_assessed": [ { "dimension": "security", "check": "https",
"reason": "no request was made (local file crawl)" } ],
"dimension_scores": { "security": null, "indexability": 100, "content_meta": 100,
"structure": 100, "schema": 100, "i18n": 100, "media": 100,
"mobile": 100, "crawlability": 100 },
"summary": { "critical": 0, "high": 0, "medium": 0, "low": 0, "total": 0 },
"findings": [ {
"id": "title_missing", "dimension": "content_meta", "severity": "critical",
"title": "...", "evidence": "<fact from crawl>", "recommendation": "...",
"leading_indicator": "<metric that should move if fixed>",
"failure_criterion": "<how we know it did NOT work>", "data_tier": "Measured"
} ]
} }
```
Findings are sorted critical→low. Failure: `{"ok": false, "error": "..."}` (e.g. crawl JSON had no pages).
## Scoring
`health_score = clamp(100 − Σ severity weights, 0, 100)` with weights critical=25, high=12, medium=6, low=2; the same weights drive per-dimension subscores. Scoring is deterministic so two runs over the same crawl are identical (drift-comparable).
A check whose input the crawl never measured does not run: its dimension scores `null` and is listed in `not_assessed`, and `health_score` covers only the checks that did run. This is why a local-file crawl cannot report a clean security or indexability result — scoring deducts for findings, so a check that silently does not fire would otherwise read as a pass.