diff --git a/.github/workflows/multi-model-eval.yml b/.github/workflows/multi-model-eval.yml
index a528fe5d..28c09b84 100644
--- a/.github/workflows/multi-model-eval.yml
+++ b/.github/workflows/multi-model-eval.yml
@@ -63,3 +63,38 @@ jobs:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
         run: deno run eval-skill-triggers --model ${{ matrix.model }} --concurrency ${{ matrix.concurrency }}
+
+      - name: Upload eval results
+        if: steps.check.outputs.run == 'true' && always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: eval-results-${{ matrix.model }}
+          path: evals/promptfoo/results.json
+          retention-days: 30
+
+  analysis:
+    name: Cross-Model Analysis
+    runs-on: ubuntu-latest
+    needs: eval
+    if: always()
+    permissions:
+      contents: read
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v6
+
+      - name: Setup Deno
+        uses: denoland/setup-deno@v2
+        with:
+          deno-version: v2.x
+
+      - name: Download all eval results
+        uses: actions/download-artifact@v4
+        with:
+          pattern: eval-results-*
+          path: eval-artifacts
+
+      - name: Run cross-model analysis
+        run: deno run --allow-read --allow-env=GITHUB_STEP_SUMMARY --allow-write scripts/analyze_eval_results.ts eval-artifacts
+        env:
+          GITHUB_STEP_SUMMARY: ${{ github.step_summary }}
diff --git a/scripts/analyze_eval_results.ts b/scripts/analyze_eval_results.ts
new file mode 100644
index 00000000..808fe560
--- /dev/null
+++ b/scripts/analyze_eval_results.ts
@@ -0,0 +1,301 @@
+// Swamp, an Automation Framework
+// Copyright (C) 2026 System Initiative, Inc.
+//
+// This file is part of Swamp.
+//
+// Swamp is free software: you can redistribute it and/or modify
+// it under the terms of the GNU Affero General Public License version 3
+// as published by the Free Software Foundation, with the Swamp
+// Extension and Definition Exception (found in the "COPYING-EXCEPTION"
+// file).
+//
+// Swamp is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU Affero General Public License for more details.
+//
+// You should have received a copy of the GNU Affero General Public License
+// along with Swamp.  If not, see <https://www.gnu.org/licenses/>.
+
+/**
+ * Analyzes eval results from all models and produces a cross-model comparison.
+ * Reads results.json files uploaded as artifacts by each eval job.
+ *
+ * Usage: deno run --allow-read --allow-env=GITHUB_STEP_SUMMARY --allow-write scripts/analyze_eval_results.ts <artifacts-dir>
+ */
+
+import { join } from "@std/path";
+
+interface EvalResult {
+  success: boolean;
+  testCase?: {
+    description?: string;
+    vars?: Record<string, string>;
+  };
+  response?: {
+    output?: unknown;
+  };
+}
+
+interface EvalStats {
+  successes: number;
+  failures: number;
+  errors: number;
+  tokenUsage: {
+    total: number;
+    prompt: number;
+    completion: number;
+  };
+  durationMs: number;
+}
+
+interface PromptfooOutput {
+  results: {
+    stats: EvalStats;
+    results: EvalResult[];
+  };
+}
+
+interface ModelSummary {
+  model: string;
+  total: number;
+  passed: number;
+  failed: number;
+  errors: number;
+  passRate: number;
+  tokens: number;
+  durationMs: number;
+  failures: string[];
+}
+
+function extractToolCallName(output: unknown): string | undefined {
+  if (!output) return undefined;
+
+  if (Array.isArray(output)) {
+    for (const item of output) {
+      // OpenAI format
+      if (item?.function?.name) return item.function.name;
+      // Google format
+      if (item?.functionCall?.name) return item.functionCall.name;
+      // Generic format
+      if (item?.name) return item.name;
+    }
+  }
+
+  if (typeof output === "object" && output !== null) {
+    const obj = output as Record<string, unknown>;
+    if (obj.function && typeof obj.function === "object") {
+      return (obj.function as Record<string, unknown>).name as string;
+    }
+    if (obj.functionCall && typeof obj.functionCall === "object") {
+      return (obj.functionCall as Record<string, unknown>).name as string;
+    }
+  }
+
+  return undefined;
+}
+
+async function loadResults(artifactsDir: string): Promise<ModelSummary[]> {
+  const summaries: ModelSummary[] = [];
+
+  for await (const entry of Deno.readDir(artifactsDir)) {
+    if (!entry.isDirectory || !entry.name.startsWith("eval-results-")) {
+      continue;
+    }
+
+    const model = entry.name.replace("eval-results-", "");
+    const resultsPath = join(artifactsDir, entry.name, "results.json");
+
+    let data: PromptfooOutput;
+    try {
+      data = JSON.parse(await Deno.readTextFile(resultsPath));
+    } catch {
+      console.warn(`Skipping ${model}: no results.json found`);
+      continue;
+    }
+
+    const { stats, results } = data.results;
+    const total = stats.successes + stats.failures;
+    const failures = results
+      .filter((r) => !r.success)
+      .map((r) => {
+        const desc = r.testCase?.description ?? "unknown";
+        const calledTool = extractToolCallName(r.response?.output);
+        const outputStr = typeof r.response?.output === "string"
+          ? r.response.output.slice(0, 80)
+          : calledTool
+          ? `routed to ${calledTool}`
+          : "text response (no tool call)";
+        return `${desc} → ${outputStr}`;
+      });
+
+    summaries.push({
+      model,
+      total,
+      passed: stats.successes,
+      failed: stats.failures,
+      errors: stats.errors,
+      passRate: total > 0 ? stats.successes / total : 0,
+      tokens: stats.tokenUsage.total,
+      durationMs: stats.durationMs,
+      failures,
+    });
+  }
+
+  // Sort by pass rate descending
+  summaries.sort((a, b) => b.passRate - a.passRate);
+  return summaries;
+}
+
+function findCrossModelFailures(
+  summaries: ModelSummary[],
+): Map<string, string[]> {
+  // Map test description -> list of models that failed it
+  const failureMap = new Map<string, string[]>();
+
+  for (const summary of summaries) {
+    for (const failure of summary.failures) {
+      // Extract just the test description (before →)
+      const desc = failure.split(" → ")[0].trim();
+      if (!failureMap.has(desc)) {
+        failureMap.set(desc, []);
+      }
+      failureMap.get(desc)!.push(summary.model);
+    }
+  }
+
+  return failureMap;
+}
+
+function formatDuration(ms: number): string {
+  const seconds = Math.floor(ms / 1000);
+  const minutes = Math.floor(seconds / 60);
+  const remainingSeconds = seconds % 60;
+  return minutes > 0 ? `${minutes}m ${remainingSeconds}s` : `${seconds}s`;
+}
+
+async function main(): Promise<void> {
+  const artifactsDir = Deno.args[0];
+  if (!artifactsDir) {
+    console.error("Usage: analyze_eval_results.ts <artifacts-dir>");
+    Deno.exit(1);
+  }
+
+  const summaries = await loadResults(artifactsDir);
+
+  if (summaries.length === 0) {
+    console.log("No eval results found.");
+    return;
+  }
+
+  // Console output
+  console.log("\n=== Cross-Model Skill Trigger Eval Analysis ===\n");
+
+  console.log("Model Results:");
+  for (const s of summaries) {
+    const status = s.passRate >= 0.9 ? "PASS" : "FAIL";
+    console.log(
+      `  ${status} ${s.model}: ${s.passed}/${s.total} (${(s.passRate * 100).toFixed(1)}%) — ${s.tokens.toLocaleString()} tokens, ${formatDuration(s.durationMs)}`,
+    );
+  }
+
+  // Cross-model failures
+  const crossModelFailures = findCrossModelFailures(summaries);
+  const multiModelFailures = [...crossModelFailures.entries()]
+    .filter(([_, models]) => models.length > 1)
+    .sort((a, b) => b[1].length - a[1].length);
+
+  if (multiModelFailures.length > 0) {
+    console.log("\nCross-Model Failures (same test fails on multiple models):");
+    for (const [desc, models] of multiModelFailures) {
+      console.log(`  [${models.length}/${summaries.length}] ${desc}`);
+      console.log(`         Models: ${models.join(", ")}`);
+    }
+  }
+
+  // Per-model unique failures
+  const singleModelFailures = [...crossModelFailures.entries()]
+    .filter(([_, models]) => models.length === 1);
+
+  if (singleModelFailures.length > 0) {
+    console.log("\nModel-Specific Failures (only one model fails):");
+    for (const [desc, models] of singleModelFailures) {
+      console.log(`  [${models[0]}] ${desc}`);
+    }
+  }
+
+  // Detailed failure list per model
+  for (const s of summaries) {
+    if (s.failures.length > 0) {
+      console.log(`\n${s.model} failures (${s.failures.length}):`);
+      for (const f of s.failures) {
+        console.log(`  ${f}`);
+      }
+    }
+  }
+
+  // Overall verdict
+  const allPassed = summaries.every((s) => s.passRate >= 0.9);
+  console.log(
+    `\nVerdict: ${allPassed ? "ALL MODELS PASS" : "ACTION REQUIRED — some models below 90% threshold"}`,
+  );
+
+  // GitHub Actions summary
+  const summaryFile = Deno.env.get("GITHUB_STEP_SUMMARY");
+  if (summaryFile) {
+    let md = "## Cross-Model Skill Trigger Eval Analysis\n\n";
+
+    // Summary table
+    md += "### Results\n\n";
+    md += "| Model | Pass Rate | Passed | Failed | Tokens | Duration | Status |\n";
+    md += "|-------|-----------|--------|--------|--------|----------|--------|\n";
+    for (const s of summaries) {
+      const status = s.passRate >= 0.9 ? "✅ Pass" : "❌ Fail";
+      md +=
+        `| ${s.model} | ${(s.passRate * 100).toFixed(1)}% | ${s.passed} | ${s.failed} | ${s.tokens.toLocaleString()} | ${formatDuration(s.durationMs)} | ${status} |\n`;
+    }
+
+    // Cross-model failures
+    if (multiModelFailures.length > 0) {
+      md += "\n### Cross-Model Failures\n\n";
+      md +=
+        "These tests fail on multiple models, suggesting skill description issues:\n\n";
+      md += "| Test | Models Failing | Count |\n";
+      md += "|------|---------------|-------|\n";
+      for (const [desc, models] of multiModelFailures) {
+        const escapedDesc = desc.replace(/\|/g, "\\|");
+        md +=
+          `| ${escapedDesc} | ${models.join(", ")} | ${models.length}/${summaries.length} |\n`;
+      }
+    }
+
+    // Model-specific failures
+    if (singleModelFailures.length > 0) {
+      md += "\n### Model-Specific Failures\n\n";
+      md +=
+        "These tests fail on only one model, suggesting model-specific quirks:\n\n";
+      md += "| Test | Model |\n";
+      md += "|------|-------|\n";
+      for (const [desc, models] of singleModelFailures) {
+        const escapedDesc = desc.replace(/\|/g, "\\|");
+        md += `| ${escapedDesc} | ${models[0]} |\n`;
+      }
+    }
+
+    // Verdict
+    md += "\n### Verdict\n\n";
+    if (allPassed) {
+      md += "✅ **All models pass** the 90% threshold.\n";
+    } else {
+      const failing = summaries.filter((s) => s.passRate < 0.9);
+      md += "❌ **Action required** — the following models are below the 90% threshold:\n\n";
+      for (const s of failing) {
+        md += `- **${s.model}**: ${(s.passRate * 100).toFixed(1)}%\n`;
+      }
+    }
+
+    await Deno.writeTextFile(summaryFile, md, { append: true });
+  }
+}
+
+await main();