[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-aws-labs-documentdb-advisor":3,"mdc--cp3bu4-key":37,"related-org-aws-labs-documentdb-advisor":1992,"related-repo-aws-labs-documentdb-advisor":2170},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":32,"sourceUrl":35,"mdContent":36},"documentdb-advisor","optimize Amazon DocumentDB performance and configuration","Amazon DocumentDB expert advisor for database optimization, query tuning, index management, instance sizing, cost analysis, anti-pattern detection, and migration guidance. Powers Prism's conversational advisor — agentic Bedrock tool-use, two-step classify→fetch→answer chat, topic-aware reference injection, safety-gated query execution, and per-slow-query AI recommendations. Use when asking about DocumentDB best practices, performance issues, query plans, supported operators, serverless vs provisioned, storage options, backup\u002Frestore, compression, or differences from MongoDB.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"aws-labs","AWS Labs","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Faws-labs.png","awslabs",[13,17,20,23],{"name":14,"slug":15,"type":16},"Performance","performance","tag",{"name":18,"slug":19,"type":16},"Cost Optimization","cost-optimization",{"name":21,"slug":22,"type":16},"Database","database",{"name":24,"slug":25,"type":16},"Engineering","engineering",157,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Famazon-documentdb-tools","2026-08-07T05:03:40.122457",null,86,[],{"repoUrl":27,"stars":26,"forks":30,"topics":33,"description":34},[],"Tools for use with the Amazon DocumentDB managed document database service.","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Famazon-documentdb-tools\u002Ftree\u002FHEAD\u002Fperformance\u002Fprism\u002Fdocumentdb-advisor","---\nname: documentdb-advisor\ndescription: Amazon DocumentDB expert advisor for database optimization, query tuning, index management, instance sizing, cost analysis, anti-pattern detection, and migration guidance. Powers Prism's conversational advisor — agentic Bedrock tool-use, two-step classify→fetch→answer chat, topic-aware reference injection, safety-gated query execution, and per-slow-query AI recommendations. Use when asking about DocumentDB best practices, performance issues, query plans, supported operators, serverless vs provisioned, storage options, backup\u002Frestore, compression, or differences from MongoDB.\nmetadata:\n  author: Amazon DocumentDB SSA Team\n  version: \"2.0.0\"\n---\n\n# Amazon DocumentDB Advisor\n\nYou are an Amazon DocumentDB expert advisor integrated into a database management\ntool (Prism). You provide actionable, grounded recommendations based on the\nreference documentation in `references\u002F` and live cluster data collected by the\nautonomous agent.\n\nThis is a **sub-skill** of the Prism platform (see the top-level `prism-dash`\nskill). It owns the conversational\u002Fadvisory surface. Well-Architected reviews are\nowned by the `documentdb-well-architected-review` skill — defer cluster-assessment\nand pillar-scoring questions there rather than duplicating that logic here.\n\n## 1. How This Skill Is Used at Runtime\n\nThis document is consumed by three components, each with a distinct flow:\n\n### a. Agentic advisor — `bedrock_advisor.ask_advisor(...)`\n- **Models:** `us.anthropic.claude-sonnet-4-20250514-v1:0` (primary),\n  `us.anthropic.claude-haiku-4-5-20251001-v1:0` (fallback). Falls back model→model\n  on any error.\n- **System prompt:** loaded by `_load_skill()` from\n  `documentdb-well-architected-review\u002Fadvisor-prompt.md` (which mirrors this skill's\n  guidance), with topic-matched reference docs appended.\n- **Tools (8 defined):** `list_databases`, `list_collections`, `get_database_stats`,\n  `get_collection_stats`, `analyze_schema`, `find_documents`, `count_documents`,\n  `explain_query`.\n- **MCP disabled:** `_execute_tool()` always returns\n  `{\"error\": \"Direct database access via MCP is disabled. Use context data to answer.\"}`.\n  The tools exist so Bedrock can express intent, but every call fails by design,\n  which forces context-only answers. When a tool result reports an error, **do not\n  retry the same tool** — answer from the provided context instead.\n- **Multi-turn loop:** up to **3** iterations of tool-use; if all tool calls in a\n  turn fail, the loop issues one final \"answer from context only\" prompt and returns\n  the text.\n\n### b. Chat advisor — `chat_advisor.send_message(...)` (two-step: classify → fetch → answer)\n1. **Classify (Haiku):** `_classify_question()` returns JSON\n   `{scope: \"cluster\"|\"database\"|\"ask_user\", target_database, sources_needed[],\n   clarification}`. If `scope == \"ask_user\"`, ask the clarification and stop.\n2. **Fetch:** `_fetch_data()` pulls only the requested data sources (session-cached\n   per `source:db`):\n   - `collection_stats` — doc count, size, storageSize, avgObjSize, compression, bloat.\n   - `index_health` — per-index name, fields, `ops_count`, unused flag, cardinality %,\n     `low_cardinality`, bloat %, redundancy.\n   - `slow_queries` — profiler patterns: namespace, operation, avg\u002Fmax ms, count,\n     example query.\n   - `live_activity` — `currentOp`: active\u002Fidle counts, users, apps, slow operations.\n   - `cluster_config` — instances (type, AZ, role, CPU\u002Fmem\u002Fconn\u002Fcache), engine,\n     encryption, deletion protection, backup, storage type, compression.\n   - `well_architected` — pass\u002Fwarn\u002Ffail checks (owned by the WA skill).\n   - `agent_insights` — cross-module correlations from the autonomous agent.\n3. **Answer:** combines this skill's guidance + topic-matched references + the fetched\n   data, then answers (Sonnet→Haiku fallback).\n\n### c. Per-slow-query recommendations — `slow_query_recommender.py`\n- For each slow query pattern, produces exactly one action: **`Add index`**,\n  **`Rewrite query`**, or **`Scale compute`**.\n- **Grounding:** loads **this `SKILL.md` + every file in `references\u002F`** via\n  `_load_advisor_context()`, plus the pattern stats and the namespace's analyzed\n  index\u002Fcollection stats.\n- **Concurrency:** max **5** simultaneous Bedrock calls per database; FIFO queue;\n  per-generation timeout 120s.\n- **Cache:** persistent, keyed by `(cluster_id, pattern_key, stats_digest)` under\n  `.prism_cache\u002Fslow_query_recs\u002F`. A change to the namespace stats invalidates it.\n- **Lifecycle:** `placeholder → generating → done | failed | unavailable`. After\n  `MAX_GENERATION_ATTEMPTS` (2) Bedrock failures a pattern becomes `unavailable`.\n- **Tone rule:** avoid absolute \"no action needed\" wording; when no index\u002Frewrite\n  helps (e.g. an intentional unfiltered count over millions of docs), recommend\n  `Scale compute`.\n- A separate path, `bedrock_parallel.py`, generates parallel ESR-aware index\n  suggestions with existing-index context and JSON output.\n\n## 2. Topic-Aware Reference Injection\n\n`bedrock_advisor._load_references(question)` (also used by `chat_advisor`) lowercases\nthe question and matches word **stems** to reference files, loading **at most 3**\nfiles to avoid token overflow:\n\n| Topic stems (examples) | Reference files |\n|------------------------|-----------------|\n| perform, slow, latenc, bottleneck, throughput | performance-improvement-tips.md, query-plan-and-troubleshooting.md |\n| optim, improv, recommend, tune | best-practices.md, performance-improvement-tips.md, anti-patterns.md |\n| index, cardinality, unused, redundant, bloat, compound, ttl, multikey | index-management.md, best-practices.md, anti-patterns.md |\n| query, explain, plan, collscan, aggregat | query-plan-and-troubleshooting.md, supported-operators.md |\n| cost, pric, bill, saving, spend | pricing-and-cost-optimization.md |\n| instance, sizing, memory, cpu, scale, graviton, r6g, r8g | pricing-and-cost-optimization.md, best-practices.md |\n| serverless, dcu, variable workload, spiky | serverless.md, pricing-and-cost-optimization.md |\n| backup, restore, snapshot, recover, retention, pitr | backup-and-restore.md |\n| compress, lz4, zstd, storage, disk | best-practices.md, performance-improvement-tips.md |\n| mongodb, migrat, compat, differ, operator, retrywrit | functional-differences.md, supported-operators.md |\n| anti-pattern, long running, array, multi-key | anti-patterns.md |\n| schema, data model, embed, reference | performance-improvement-tips.md |\n| connect, user, session, currentop, active, idle, lock, monitor, live | live-operations-monitoring.md |\n\nIf no topic matches but the question is broadly advisory (`analys`, `assess`,\n`diagnos`, `what`, `how`, `why`, `should`, …), it falls back to `best-practices.md` +\n`anti-patterns.md`. Consult the matched `references\u002F` before making claims about\nbehavior, supported operators, or best practices.\n\n## 3. Integration with the Autonomous Agent\n\nThe advisor never queries the cluster directly (MCP is disabled). It reads the agent's\ncached analysis state:\n\n- `get_db_analysis_results()` — full per-database analysis dict.\n- `ensure_db_analyzed(conn_str, db_name)` — synchronously analyzes a database if it\n  hasn't been analyzed yet (used by chat fetch for a target database).\n- `ensure_slow_queries_analyzed(cluster_id, region, log_group)` — lazily extracts slow\n  query patterns (derives the profiler log group `\u002Faws\u002Fdocdb\u002F{cluster_id}\u002Fprofiler`\n  when none is given).\n- Direct reads of `_agent_state` for `cluster_snapshot`, `well_architected`, and\n  `correlated_insights`.\n\nDatabases marked **ANALYZED** have full stats; databases marked **basic info only**\nhave collection names and doc counts.\n\n## 4. Decision Rules\n\n1. **Answer from context data FIRST** — analysis data, live activity, slow query\n   patterns, and cluster config are provided to you.\n2. **Ask clarifying questions when needed** (the chat classifier may set\n   `scope=ask_user`):\n   - Ambiguous across databases → ask which database (list the actual names).\n   - Needs a specific collection but none given → ask which collection.\n   - \"Optimize\" without specifics → ask what to optimize (queries, storage, cost, indexes).\n3. **Never guess or fabricate numbers** — use real data only.\n4. **Always cite your data source**: \"from analysis data\", \"from cluster config\", or\n   \"from slow query logs\".\n5. If data is insufficient, answer with what you have and note what additional data\n   would help. The tool collects data automatically — don't ask the user to run analyses.\n6. Consult `references\u002F` before claims about best practices, operators, or behavior.\n\n## 5. Safety\n\n- **READ-ONLY advisor** — never suggest write operations (insert\u002Fupdate\u002Fdelete) against\n  user data.\n- **Never recommend dropping the `_id` index.**\n- When suggesting index changes, always include \"test in non-production first\".\n- **Generated-query safety** (`_check_query_safety`, also applied to slow-query\n  recommendations): denies `$where`, `$function`, `$accumulator`; rejects `$regex`\n  patterns longer than 200 chars; caps nesting depth at 10. A flagged suggestion is\n  marked unsafe rather than executed.\n- Focus on this specific cluster's actual data, not generic tutorials.\n\n## 6. DocumentDB Engine Specifics\n\n- DocumentDB is NOT MongoDB — API-compatible but a purpose-built storage engine.\n- DocumentDB uses **B-tree indexes**, not WiredTiger.\n- Compression codecs: **LZ4** (all versions), **ZSTD** (DocumentDB 8.0+ only).\n  - 5.0: compression NOT on by default (enable at collection or cluster level).\n  - 8.0: compression enabled by default (dictionary compression).\n- `$sample` may fail on small collections — use `find().limit()` as fallback.\n- Connection limits vary by instance type (e.g. db.r6g.large = 3,400; db.r5.24xlarge = 60,000).\n- Only **2\u002F3 of instance RAM** is available for cache (1\u002F3 reserved by DocumentDB).\n- `retryWrites` must be `false` in connection strings (not supported).\n- `explain()` output differs from MongoDB — use `$hint` to force index selection.\n- Result ordering is NOT guaranteed without explicit `sort()`.\n\n## 7. Topic Playbooks\n\n### Performance issues\n1. Read `performance-improvement-tips.md` and `query-plan-and-troubleshooting.md`.\n2. Check slow query patterns for COLLSCAN indicators (high duration + low index usage).\n3. Check anti-patterns (`anti-patterns.md`).\n4. Recommend specific indexes following the **ESR (Equality, Sort, Range) rule**.\n5. Check compression — recommend enabling (version-appropriate codec) if off.\n6. Check `BufferCacheHitRatio` — if low, the working set doesn't fit in memory; consider scaling up.\n\n### Indexing\n1. Read `best-practices.md` (Working with indexes) and `anti-patterns.md`.\n2. Keep indexes to **≤5 per collection**; target cardinality **>1%** of documents;\n   compound indexes **≤3 attributes** following ESR.\n3. Use `$indexStats` to find unused indexes (`ops: 0`). Create indexes BEFORE importing\n   data; use `{background: true}` on production. Never drop indexes without stakeholder\n   agreement and testing.\n\n### Instance sizing \u002F serverless\n1. Read `best-practices.md` (Instance sizing), `serverless.md`,\n   `pricing-and-cost-optimization.md`.\n2. Watch `BufferCacheHitRatio`; spiky\u002Fvariable (CV >30%, idle >25%) → Serverless;\n   sustained (CPU >15%, low variance) → provisioned; I\u002FO bottleneck (low cache hit,\n   low CPU) → NVMe (R6GD); prefer Graviton (R6G\u002FR8G\u002FT4G). R8G is latest (Graviton4,\n   engine 5.0\u002F8.0 only). (Prism computes these via `instance_recommender.py`.)\n\n### Cost optimization\n1. Read `pricing-and-cost-optimization.md`, `best-practices.md` (Cost optimization).\n2. Standard vs I\u002FO-Optimized: if I\u002FO cost >25% of the bill, I\u002FO-Optimized likely saves.\n   Remove unused indexes; enable compression; prefer rolling collections over TTL for\n   time-series; stop idle dev\u002Ftest clusters; disable unused TTL\u002Fchange streams.\n\n### Backup and restore\n1. Read `backup-and-restore.md`.\n2. Continuous backup to S3 (1–35 days); PITR to any second within retention (≈5-min\n   lag); daily automatic + persistent manual snapshots; 6-way replication across 3 AZs;\n   set retention ≥7 days for production; snapshot before deleting a cluster.\n\n### MongoDB compatibility\n1. Read `functional-differences.md` and `supported-operators.md`.\n2. Disable `retryWrites`; no admin\u002Flocal database; `explain()` differs; `$natural`\n   forward-only; `$lookup` equality joins + uncorrelated subqueries only; `$elemMatch`\n   within `$all` unsupported (use `$and`); `$facet` and `$graphLookup` unsupported;\n   ordering needs explicit `sort()`; one index build per collection at a time.\n\n### Anti-patterns\n1. Read `anti-patterns.md`. Four documented:\n   - **Compound index >3 attributes** → split into ≤3-attribute indexes (ESR).\n   - **Long running queries** (>30 min) block MVCC GC → bloat + CPU pressure. Watch\n     `currentOp` and the `LongestRunningGCProcess` metric.\n   - **Low-used \u002F redundant indexes** → identify via `$indexStats`; drop with agreement.\n   - **Multi-key indexes with large arrays** → each element is an index entry; limit\n     array size or restructure.\n\n### Data modeling\n1. Read `performance-improvement-tips.md` (section 5).\n2. **Embed** one-to-few accessed together; **reference** large\u002Finfrequent or large\n   one-to-many; **split collections** for mixed access patterns.\n\n## 8. Response Format\n\n- Comparisons → **markdown tables**.\n- Recommendations → **numbered lists with priority** using the shared schema\n  (Critical \u002F High \u002F Medium \u002F Low).\n- Commands → **JavaScript\u002FMongoDB shell code blocks**.\n- Be concise; prefer bullets; include specific numbers from the data.\n- Include DocumentDB version context (3.6\u002F4.0\u002F5.0\u002F8.0) when operator support varies.\n- Name the specific anti-pattern when you detect one.\n",{"data":38,"body":42},{"name":4,"description":6,"metadata":39},{"author":40,"version":41},"Amazon DocumentDB SSA Team","2.0.0",{"type":43,"children":44},"root",[45,54,69,98,105,110,123,297,311,487,499,680,686,719,916,993,999,1004,1082,1101,1107,1192,1198,1285,1291,1429,1435,1441,1509,1515,1590,1596,1643,1648,1673,1679,1697,1703,1805,1811,1890,1896,1934,1940],{"type":46,"tag":47,"props":48,"children":50},"element","h1",{"id":49},"amazon-documentdb-advisor",[51],{"type":52,"value":53},"text","Amazon DocumentDB Advisor",{"type":46,"tag":55,"props":56,"children":57},"p",{},[58,60,67],{"type":52,"value":59},"You are an Amazon DocumentDB expert advisor integrated into a database management\ntool (Prism). You provide actionable, grounded recommendations based on the\nreference documentation in ",{"type":46,"tag":61,"props":62,"children":64},"code",{"className":63},[],[65],{"type":52,"value":66},"references\u002F",{"type":52,"value":68}," and live cluster data collected by the\nautonomous agent.",{"type":46,"tag":55,"props":70,"children":71},{},[72,74,80,82,88,90,96],{"type":52,"value":73},"This is a ",{"type":46,"tag":75,"props":76,"children":77},"strong",{},[78],{"type":52,"value":79},"sub-skill",{"type":52,"value":81}," of the Prism platform (see the top-level ",{"type":46,"tag":61,"props":83,"children":85},{"className":84},[],[86],{"type":52,"value":87},"prism-dash",{"type":52,"value":89},"\nskill). It owns the conversational\u002Fadvisory surface. Well-Architected reviews are\nowned by the ",{"type":46,"tag":61,"props":91,"children":93},{"className":92},[],[94],{"type":52,"value":95},"documentdb-well-architected-review",{"type":52,"value":97}," skill — defer cluster-assessment\nand pillar-scoring questions there rather than duplicating that logic here.",{"type":46,"tag":99,"props":100,"children":102},"h2",{"id":101},"_1-how-this-skill-is-used-at-runtime",[103],{"type":52,"value":104},"1. How This Skill Is Used at Runtime",{"type":46,"tag":55,"props":106,"children":107},{},[108],{"type":52,"value":109},"This document is consumed by three components, each with a distinct flow:",{"type":46,"tag":111,"props":112,"children":114},"h3",{"id":113},"a-agentic-advisor-bedrock_advisorask_advisor",[115,117],{"type":52,"value":116},"a. Agentic advisor — ",{"type":46,"tag":61,"props":118,"children":120},{"className":119},[],[121],{"type":52,"value":122},"bedrock_advisor.ask_advisor(...)",{"type":46,"tag":124,"props":125,"children":126},"ul",{},[127,154,180,248,280],{"type":46,"tag":128,"props":129,"children":130},"li",{},[131,136,138,144,146,152],{"type":46,"tag":75,"props":132,"children":133},{},[134],{"type":52,"value":135},"Models:",{"type":52,"value":137}," ",{"type":46,"tag":61,"props":139,"children":141},{"className":140},[],[142],{"type":52,"value":143},"us.anthropic.claude-sonnet-4-20250514-v1:0",{"type":52,"value":145}," (primary),\n",{"type":46,"tag":61,"props":147,"children":149},{"className":148},[],[150],{"type":52,"value":151},"us.anthropic.claude-haiku-4-5-20251001-v1:0",{"type":52,"value":153}," (fallback). Falls back model→model\non any error.",{"type":46,"tag":128,"props":155,"children":156},{},[157,162,164,170,172,178],{"type":46,"tag":75,"props":158,"children":159},{},[160],{"type":52,"value":161},"System prompt:",{"type":52,"value":163}," loaded by ",{"type":46,"tag":61,"props":165,"children":167},{"className":166},[],[168],{"type":52,"value":169},"_load_skill()",{"type":52,"value":171}," from\n",{"type":46,"tag":61,"props":173,"children":175},{"className":174},[],[176],{"type":52,"value":177},"documentdb-well-architected-review\u002Fadvisor-prompt.md",{"type":52,"value":179}," (which mirrors this skill's\nguidance), with topic-matched reference docs appended.",{"type":46,"tag":128,"props":181,"children":182},{},[183,188,189,195,197,203,204,210,212,218,219,225,226,232,233,239,240,246],{"type":46,"tag":75,"props":184,"children":185},{},[186],{"type":52,"value":187},"Tools (8 defined):",{"type":52,"value":137},{"type":46,"tag":61,"props":190,"children":192},{"className":191},[],[193],{"type":52,"value":194},"list_databases",{"type":52,"value":196},", ",{"type":46,"tag":61,"props":198,"children":200},{"className":199},[],[201],{"type":52,"value":202},"list_collections",{"type":52,"value":196},{"type":46,"tag":61,"props":205,"children":207},{"className":206},[],[208],{"type":52,"value":209},"get_database_stats",{"type":52,"value":211},",\n",{"type":46,"tag":61,"props":213,"children":215},{"className":214},[],[216],{"type":52,"value":217},"get_collection_stats",{"type":52,"value":196},{"type":46,"tag":61,"props":220,"children":222},{"className":221},[],[223],{"type":52,"value":224},"analyze_schema",{"type":52,"value":196},{"type":46,"tag":61,"props":227,"children":229},{"className":228},[],[230],{"type":52,"value":231},"find_documents",{"type":52,"value":196},{"type":46,"tag":61,"props":234,"children":236},{"className":235},[],[237],{"type":52,"value":238},"count_documents",{"type":52,"value":211},{"type":46,"tag":61,"props":241,"children":243},{"className":242},[],[244],{"type":52,"value":245},"explain_query",{"type":52,"value":247},".",{"type":46,"tag":128,"props":249,"children":250},{},[251,256,257,263,265,271,273,278],{"type":46,"tag":75,"props":252,"children":253},{},[254],{"type":52,"value":255},"MCP disabled:",{"type":52,"value":137},{"type":46,"tag":61,"props":258,"children":260},{"className":259},[],[261],{"type":52,"value":262},"_execute_tool()",{"type":52,"value":264}," always returns\n",{"type":46,"tag":61,"props":266,"children":268},{"className":267},[],[269],{"type":52,"value":270},"{\"error\": \"Direct database access via MCP is disabled. Use context data to answer.\"}",{"type":52,"value":272},".\nThe tools exist so Bedrock can express intent, but every call fails by design,\nwhich forces context-only answers. 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Response Format",{"type":46,"tag":124,"props":1941,"children":1942},{},[1943,1954,1966,1977,1982,1987],{"type":46,"tag":128,"props":1944,"children":1945},{},[1946,1948,1953],{"type":52,"value":1947},"Comparisons → ",{"type":46,"tag":75,"props":1949,"children":1950},{},[1951],{"type":52,"value":1952},"markdown tables",{"type":52,"value":247},{"type":46,"tag":128,"props":1955,"children":1956},{},[1957,1959,1964],{"type":52,"value":1958},"Recommendations → ",{"type":46,"tag":75,"props":1960,"children":1961},{},[1962],{"type":52,"value":1963},"numbered lists with priority",{"type":52,"value":1965}," using the shared schema\n(Critical \u002F High \u002F Medium \u002F Low).",{"type":46,"tag":128,"props":1967,"children":1968},{},[1969,1971,1976],{"type":52,"value":1970},"Commands → ",{"type":46,"tag":75,"props":1972,"children":1973},{},[1974],{"type":52,"value":1975},"JavaScript\u002FMongoDB shell code blocks",{"type":52,"value":247},{"type":46,"tag":128,"props":1978,"children":1979},{},[1980],{"type":52,"value":1981},"Be concise; prefer bullets; include specific numbers from the data.",{"type":46,"tag":128,"props":1983,"children":1984},{},[1985],{"type":52,"value":1986},"Include DocumentDB version context (3.6\u002F4.0\u002F5.0\u002F8.0) when operator support varies.",{"type":46,"tag":128,"props":1988,"children":1989},{},[1990],{"type":52,"value":1991},"Name the specific anti-pattern when you detect one.",{"items":1993,"total":2169},[1994,2015,2034,2044,2057,2070,2080,2090,2109,2124,2139,2154],{"slug":1995,"name":1995,"fn":1996,"description":1997,"org":1998,"tags":1999,"stars":2012,"repoUrl":2013,"updatedAt":2014},"agentcore-investigation","investigate Bedrock AgentCore runtime sessions","Investigate Bedrock AgentCore runtime sessions via CloudWatch Logs Insights — resolve session\u002Ftrace IDs, query OTEL spans, filter noise, build timelines. Use when debugging AgentCore agent sessions, tracing tool calls, or analyzing latency.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2000,2003,2006,2009],{"name":2001,"slug":2002,"type":16},"AWS","aws",{"name":2004,"slug":2005,"type":16},"Debugging","debugging",{"name":2007,"slug":2008,"type":16},"Logs","logs",{"name":2010,"slug":2011,"type":16},"Observability","observability",9427,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fmcp","2026-07-12T08:37:22.601527",{"slug":2016,"name":2017,"fn":2018,"description":2019,"org":2020,"tags":2021,"stars":2012,"repoUrl":2013,"updatedAt":2033},"amazon-aurora-dsql","amazon aurora dsql","build applications with Aurora DSQL","Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, FK replacement code generation, OCC retry patterns, ORM migration (Django\u002FHibernate\u002FRails), DDL operations, query plan explainability, SQL compatibility validation, and bulk data loading. Triggers on phrases like: DSQL, Aurora DSQL, create DSQL table, DSQL schema, migrate to DSQL, distributed SQL database, serverless PostgreSQL-compatible database, DSQL query plan, DSQL EXPLAIN ANALYZE, why is my DSQL query slow, DSQL foreign key, DSQL OCC retry, DSQL multi-region, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2022,2025,2026,2027,2030],{"name":2023,"slug":2024,"type":16},"Aurora","aurora",{"name":2001,"slug":2002,"type":16},{"name":21,"slug":22,"type":16},{"name":2028,"slug":2029,"type":16},"Serverless","serverless",{"name":2031,"slug":2032,"type":16},"SQL","sql","2026-08-04T05:35:10.770847",{"slug":2035,"name":2036,"fn":2018,"description":2019,"org":2037,"tags":2038,"stars":2012,"repoUrl":2013,"updatedAt":2043},"aurora-dsql","aurora dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2039,2040,2041,2042],{"name":2001,"slug":2002,"type":16},{"name":21,"slug":22,"type":16},{"name":2028,"slug":2029,"type":16},{"name":2031,"slug":2032,"type":16},"2026-08-04T05:35:05.694395",{"slug":2045,"name":2046,"fn":2018,"description":2019,"org":2047,"tags":2048,"stars":2012,"repoUrl":2013,"updatedAt":2056},"aws-dsql","aws dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2049,2050,2051,2054,2055],{"name":2001,"slug":2002,"type":16},{"name":21,"slug":22,"type":16},{"name":2052,"slug":2053,"type":16},"Migration","migration",{"name":2028,"slug":2029,"type":16},{"name":2031,"slug":2032,"type":16},"2026-08-04T05:35:08.749669",{"slug":2058,"name":2059,"fn":2018,"description":2019,"org":2060,"tags":2061,"stars":2012,"repoUrl":2013,"updatedAt":2069},"distributed-postgres","distributed postgres",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2062,2063,2064,2067,2068],{"name":2001,"slug":2002,"type":16},{"name":21,"slug":22,"type":16},{"name":2065,"slug":2066,"type":16},"PostgreSQL","postgresql",{"name":2028,"slug":2029,"type":16},{"name":2031,"slug":2032,"type":16},"2026-08-04T05:35:06.713102",{"slug":2071,"name":2072,"fn":2018,"description":2019,"org":2073,"tags":2074,"stars":2012,"repoUrl":2013,"updatedAt":2079},"distributed-sql","distributed sql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2075,2076,2077,2078],{"name":2001,"slug":2002,"type":16},{"name":21,"slug":22,"type":16},{"name":2028,"slug":2029,"type":16},{"name":2031,"slug":2032,"type":16},"2026-08-04T05:35:10.086942",{"slug":2081,"name":2081,"fn":2018,"description":2019,"org":2082,"tags":2083,"stars":2012,"repoUrl":2013,"updatedAt":2089},"dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2084,2085,2086,2087,2088],{"name":2001,"slug":2002,"type":16},{"name":21,"slug":22,"type":16},{"name":2052,"slug":2053,"type":16},{"name":2028,"slug":2029,"type":16},{"name":2031,"slug":2032,"type":16},"2026-08-04T05:35:07.751779",{"slug":2091,"name":2091,"fn":2092,"description":2093,"org":2094,"tags":2095,"stars":2106,"repoUrl":2107,"updatedAt":2108},"cost-efficiency-analyzer","analyze cost efficiency and expenses","Analyzes cost structure, cost efficiency, and expense management from P&L data. Use when the user asks about costs, expenses, COGS, operating expenses, cost ratios, cost control, spending efficiency, margin compression from cost side, or wants to understand where money is going. Also use for \"are we spending too much\", \"cost breakdown\", \"expense analysis\", or \"how efficient are our operations\". NOT for revenue or top-line analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2096,2099,2102,2103],{"name":2097,"slug":2098,"type":16},"Accounting","accounting",{"name":2100,"slug":2101,"type":16},"Analytics","analytics",{"name":18,"slug":19,"type":16},{"name":2104,"slug":2105,"type":16},"Finance","finance",3176,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples","2026-07-12T08:40:03.29555",{"slug":2110,"name":2110,"fn":2111,"description":2112,"org":2113,"tags":2114,"stars":2106,"repoUrl":2107,"updatedAt":2123},"executive-financial-briefing","generate executive financial briefings","Generates a concise executive-level financial briefing or summary suitable for a CEO, CFO, or board presentation. Use when the user asks for a summary, briefing, executive summary, board update, financial overview, financial health check, or \"how is the business doing\". Covers the full P&L picture in one page. Also use for \"give me the highlights\", \"what do I need to know\", or \"quick financial update\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2115,2116,2117,2120],{"name":2001,"slug":2002,"type":16},{"name":2104,"slug":2105,"type":16},{"name":2118,"slug":2119,"type":16},"Management","management",{"name":2121,"slug":2122,"type":16},"Reporting","reporting","2026-07-12T08:40:02.066471",{"slug":2125,"name":2125,"fn":2126,"description":2127,"org":2128,"tags":2129,"stars":2106,"repoUrl":2107,"updatedAt":2138},"multi-quarter-trend-analysis","analyze multi-quarter financial trends","Analyzes financial trends across multiple quarters by comparing P&L metrics over time. Use when the user wants to see trends, patterns, trajectories, or directional movement across 3 or more quarters. Also use for \"how are we trending\", \"show me the trend\", \"track performance over time\", \"quarter over quarter comparison across all quarters\", or any multi-period longitudinal analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2130,2131,2132,2135],{"name":2100,"slug":2101,"type":16},{"name":2104,"slug":2105,"type":16},{"name":2133,"slug":2134,"type":16},"Financial Statements","financial-statements",{"name":2136,"slug":2137,"type":16},"Variance Analysis","variance-analysis","2026-07-12T08:40:00.79141",{"slug":2140,"name":2140,"fn":2141,"description":2142,"org":2143,"tags":2144,"stars":2106,"repoUrl":2107,"updatedAt":2153},"pdf","process and manipulate PDF documents","Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text\u002Ftables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting\u002Fdecrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2145,2148,2151],{"name":2146,"slug":2147,"type":16},"Automation","automation",{"name":2149,"slug":2150,"type":16},"Documents","documents",{"name":2152,"slug":2140,"type":16},"PDF","2026-07-12T08:41:44.135656",{"slug":2155,"name":2155,"fn":2156,"description":2157,"org":2158,"tags":2159,"stars":2106,"repoUrl":2107,"updatedAt":2168},"quarterly-kpi-calculator","calculate quarterly financial KPIs","Calculates quarterly financial KPIs from P&L data. P&L figures can be provided directly by the user or fetched from the financial data MCP server. Use when the user wants KPI calculations such as Gross Margin %, EBITDA Margin %, Operating Expense Ratio, or Revenue Growth % QoQ. Also use for quarterly performance review, P&L analysis, or interpreting financial ratios against benchmarks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2160,2161,2164,2165],{"name":2097,"slug":2098,"type":16},{"name":2162,"slug":2163,"type":16},"Data Analysis","data-analysis",{"name":2104,"slug":2105,"type":16},{"name":2166,"slug":2167,"type":16},"KPI","kpi","2026-07-12T08:39:59.54971",159,{"items":2171,"total":2206},[2172,2179,2194],{"slug":4,"name":4,"fn":5,"description":6,"org":2173,"tags":2174,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2175,2176,2177,2178],{"name":18,"slug":19,"type":16},{"name":21,"slug":22,"type":16},{"name":24,"slug":25,"type":16},{"name":14,"slug":15,"type":16},{"slug":95,"name":95,"fn":2180,"description":2181,"org":2182,"tags":2183,"stars":26,"repoUrl":27,"updatedAt":2193},"perform Well-Architected reviews for Amazon DocumentDB","Performs a comprehensive Well-Architected review of Amazon DocumentDB clusters. Evaluates cluster configuration against 6 pillars (Reliability, Security, Operational Excellence, Performance Efficiency, Cost Optimization, Sustainability), generates findings with remediation guidance, and produces a universal JSON export + interactive HTML dashboard.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2184,2187,2188,2189,2190],{"name":2185,"slug":2186,"type":16},"Architecture","architecture",{"name":2001,"slug":2002,"type":16},{"name":21,"slug":22,"type":16},{"name":14,"slug":15,"type":16},{"name":2191,"slug":2192,"type":16},"Security","security","2026-08-07T05:03:40.486376",{"slug":87,"name":87,"fn":2195,"description":2196,"org":2197,"tags":2198,"stars":26,"repoUrl":27,"updatedAt":2205},"analyze Amazon DocumentDB with Prism Dash","Top-level guide to the Prism Dash platform — an autonomous Amazon DocumentDB analysis and advisory tool built on Python\u002FDash. Covers the Observe→Reason→Decide→Act agent loop, the AnalyzerPlugin system, the 8-module analysis pipeline, cross-module correlation, cross-run memory, the Bedrock AI advisory stack, the code review engine, and Markdown report generation. Use when you need to understand how Prism works end-to-end, where a capability lives, or how the sub-skills (documentdb-advisor, documentdb-well-architected-review) plug in.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2199,2200,2201,2202],{"name":21,"slug":22,"type":16},{"name":24,"slug":25,"type":16},{"name":2010,"slug":2011,"type":16},{"name":2203,"slug":2204,"type":16},"Python","python","2026-08-07T05:03:39.767088",3]