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kanchana-mongodb
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* feat(mongodb-search-and-ai): add Automated Embedding, refine skill after review Add Automated Embedding (autoEmbed) support and polish the skill following a full /review-skill pass (structural validation + LLM scoring + live cluster validation against sample_mflix.movies). Changes: - SKILL.md: add "Automated Embedding" search type, cluster-tier check step, Core Principle #6 (accessible language), condense $regex/$text anti-pattern, trim redundant "Remember" section; restore license/metadata frontmatter to match sibling skills. - references/automated-embedding.md: new reference for autoEmbed index types and text-query (query vs queryVector) syntax. - references/vector-search.md: consolidate pre/post-filter docs, fix dead TOC anchor (#filter-fields), remove duplicated performance notes and score caveats. - references/hybrid-search.md: $rankFusion (8.0+) / $scoreFusion (8.2+) updates. Review outcome: structural validation passed; SKILL.md overall 3.83 -> 4.0, reference aggregate 4.4 -> 4.6, no dimension below 3. Live-validated the highest-novelty autoEmbed query/queryVector distinction against a live Atlas cluster. * test(mongodb-search-and-ai): add Automated Embedding evals + iteration-1 results Add three Automated Embedding eval cases (ids 16-18) covering the autoEmbed index type, plain-text query field (vs queryVector), and model selection (voyage-4-lite for cost, voyage-code-3 for code). Add SUMMARY.md recording the iteration-1 skill-creator run: with_skill 95.2% vs without_skill 43.4% (+51.8%), Claude Opus 4.6, MongoDB MCP against sample_mflix. All 18 evals differentiate; the new autoEmbed cases show +75%/+100%/+75% deltas. * Potential fix for pull request finding Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> * Potential fix for pull request finding Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> * chore(mongodb-search-and-ai): sync plugin skill copies Regenerate the mongodb and mongodb-atlas plugin copies of the mongodb-search-and-ai skill from the canonical skills/ source so the plugin mirrors reflect the Automated Embedding + review refinements. Ran tools/sync-plugin-skills.ts; output is idempotent (no further drift). Fixes the failing sync-check CI job on PR #57. * fix(mongodb-search-and-ai): use valid mongosh syntax in autoEmbed index example Address Copilot review on PR #57: the createSearchIndex example used db.<collection>, which isn't valid mongosh syntax. Use db.collection to match the convention in the sibling reference files (placeholders stay inside strings). Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent). * AGSK-18 fixes version * fix(mongodb-search-and-ai): correct version gates to 8.3+ SME correction: $scoreFusion and self-managed Automated Embedding require MongoDB 8.3+ (previously documented as 8.2+). Normalize the $scoreFusion note in SKILL.md to use the "+" suffix for consistency. Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent). * fix(mongodb-search-and-ai): address Copilot review feedback - Add "Automated Embedding" to the workflow proceed-gate in SKILL.md so Automated Embedding requests don't dead-end after the Cluster Check. - Make self-managed prerequisites edition-agnostic: drop "Community Edition"/"Enterprise Edition" qualifiers in automated-embedding.md and SKILL.md; key the requirement on MongoDB 8.3+ with mongot + Voyage AI. Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent). * fix(mongodb-search-and-ai): correct rate-limit unit in autoEmbed upgrade note Address Copilot review on PR #57: the free->paid upgrade note said "667x higher TPM", but 667x is the RPM increase (3 -> 2,000 RPM). Reword to state the RPM change explicitly and note TPM rises correspondingly, so it matches the rate-limit tables above. Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent). * docs(mongodb-search-and-ai): note Atlas prerequisites can change Address Copilot review on PR #57: the Automated Embedding cluster tier and autoscaling prerequisites were stated as absolutes. Add a brief note in SKILL.md and automated-embedding.md to confirm current requirements in the Atlas UI or official docs, since these can drift across releases. Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent). * docs(mongodb-search-and-ai): cover item-to-item similarity for autoEmbed Address jeff-allen-mongo's review on PR #57: the decision framework and Automated Embedding query guidance didn't handle "given item A, find similar items" requests. Since autoEmbed $vectorSearch.query only accepts a plain text string (no raw-vector read path to reuse a document's stored embedding), add: - A clarifying Discovery question in SKILL.md (free text vs. similarity to an existing item). - An "Item-to-Item Similarity" callout in automated-embedding.md showing how to resolve the source item to its indexed text field and pass that as the query (with a self-exclusion filter), plus a TOC entry. Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent). * docs(mongodb-search-and-ai): simplify Automated Embedding autoscaling note Condense the M10+ dedicated cluster prerequisite to "enable storage autoscaling" and align the Item-to-Item Similarity TOC entry as a top-level bullet. Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent). * docs(mongodb-search-and-ai): simplify autoEmbed auto-scaling prerequisite Condense the Atlas Clusters prerequisite to "M10+ dedicated clusters require storage auto-scaling to be enabled", dropping the detailed max-tier table and explanatory paragraph. Fix subject/verb agreement in the staleness note. Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent). * docs(mongodb-search-and-ai): address Copilot review nits - SKILL.md: standardize on "auto-scaling" (hyphenated) to match references/automated-embedding.md and the Atlas UI spelling. - references/vector-search.md: remove trailing whitespace on the pre-filtering Definition line. - references/hybrid-search.md: format all referenced filenames in the Scope paragraph as inline code for consistency. Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent). * test(mongodb-search-and-ai): expect cluster check in autoEmbed evals Align Automated Embedding eval cases 16-18 with the updated skill workflow: each expected_output now requires a cluster check before index creation - confirm Atlas (all tiers incl. M0) vs self-managed (8.3+ with mongot + Voyage AI key), and call out the M10+ dedicated storage auto-scaling prerequisite. * chore(mongodb-search-and-ai): sync plugin mirrors for auto-scaling wording Propagate the canonical SKILL.md edit ("M10+ without storage auto-scaling") to the four plugin copies via tools/sync-plugin-skills.ts. Fixes the failing Plugin Skills Sync check caused by mirror drift. * fix(mongodb-search-and-ai): apply review suggestions and sync plugin mirrors Co-authored-by: kanchana-mongodb <54281287+kanchana-mongodb@users.noreply.github.com> * docs(mongodb-search-and-ai): address review feedback Route search-type selection from step 2 and defer prerequisites to reference files, per PR review. - SKILL.md: merge search-type routing into "Determine Search Type" (step 2); remove standalone Cluster Check / Version Check / Consult Reference Files steps. Each search type links directly to its reference file and defers prerequisite verification to that file. - automated-embedding.md: move cluster/self-managed prerequisites and the M10+ auto-scaling + Voyage AI fallback behavior into Prerequisites; drop "Atlas UI" wording; fix inaccurate "no vector pipelines required" intro; combine intro+scope; remove decorative "---" rules; drop the duplicated "When to Use" routing block; document optional filter type/path fields. - vector-search.md / hybrid-search.md: combine intro + scope to reduce redundancy; move hybrid version-gate fallback behavior into hybrid-search.md. - evals.json: fix missing comma (invalid JSON). - Regenerate plugin skill mirrors via tools/sync-plugin-skills.ts. * docs(mongodb-search-and-ai): fold in remaining Copilot suggestions Address open Copilot review comments on the Automated Embedding reference, then regenerate the plugin mirrors. - automated-embedding.md: use a concrete `db.movies` collection name in the createSearchIndex and item-to-item examples (instead of `db.collection` / `db.<collection>`) for consistency with sibling reference files; use the modern `{ projection: { plot: 1 } }` findOne options form; add a note that `_id` is always available as a filter field on an autoEmbed index (no need to declare it) for the self-exclusion filter. - Regenerate plugin skill mirrors via tools/sync-plugin-skills.ts. * docs(mongodb-search-and-ai): clarify filter-field guidance Address Copilot re-review comments on automated-embedding.md. - Reword the self-referential filter-field inline comment to explain the purpose (index fields as filters to enable pre-filtering / scoped search) instead of "recommended for filter fields". - Clarify the `_id` note so it no longer contradicts the Query Parameters table: filter fields must generally be indexed as type "filter", with `_id` called out as the one implicit exception; add the same caveat to the table's `filter` row. - Regenerate plugin skill mirrors via tools/sync-plugin-skills.ts. * Update MCP config description in SUMMARY.md Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>