* feat: expand Kiro adapter to full language coverage - Add 17 new agents (typescript, rust, kotlin, java, cpp, django, swift, fsharp, pytorch, mle, performance-optimizer) in both .md and .json formats - Add 25 new skills (rust, kotlin, java/spring, django, fastapi, nestjs, react, nextjs, cpp, swift, mle/pytorch, deep-research, strategic-compact, autonomous-loops, content-hash-cache-pattern) - Add 6 new language-specific steering files (rust, kotlin, java, cpp, php, ruby) - Add 3 new hooks (rust-check-on-edit, python-lint-on-edit, security-check-on-create) - Update README with expanded component inventory and documentation - Fix install.sh line endings for macOS compatibility Total Kiro components: 33 agents, 43 skills, 22 steering files, 13 hooks * fix: resolve P1/P2 violations in Kiro agents, skills, and steering - java-patterns.md: remove reference to non-existent quarkus-patterns skill - kotlin-patterns.md: fix insecure BuildConfig recommendation for secrets - swift-actor-persistence: fix Swift version claim (5.9+) and Dictionary crash - java-reviewer.md: add recursive framework detection + robust diff chain - kotlin-reviewer.md: replace unreliable diff detection with fallback chain - rust-reviewer.md: add diff fallback + make CI gating mandatory - jpa-patterns: add DISTINCT to fetch-join query to prevent duplicates - django-reviewer.md: add migration safety check, narrow save() rule, fix pytest-django behavior description * fix: resolve remaining violations in Kiro agents, skills, and docs Agents: - java-build-resolver.md: remove quarkus-patterns ref, fix 'Initialise' spelling - java-reviewer.json: remove quarkus-patterns ref from prompt - mle-reviewer.md, cpp-build-resolver.md, java-build-resolver.md, performance-optimizer.md: fix allowedTools 'read' -> 'fs_read' Hooks: - rust-check-on-edit: fix description to match askAgent behavior Skills: - content-hash-cache-pattern: hyphenate 'Content-Hash-Based' - cpp-testing: hyphenate 'real-time' - django-security: use placeholder secrets, fix CSRF_COOKIE_HTTPONLY=False - nestjs-patterns: add Logger to HttpExceptionFilter for non-Http errors - react-patterns: add React 19 compatibility note for useActionState - rust-patterns: remove edition-specific 'Rust 2024+' reference - springboot-patterns: cap exponential backoff, recommend Resilience4j - springboot-security: fix invalid @Query SQL injection example - swift-protocol-di-testing: add thread-safety doc comment to mock Docs: - README.md: fix Project Structure counts (33/43/22/13) * fix: sync README tree with counts, restore local diff in kotlin-reviewer, correct django FK index guidance - README.md: Project Structure tree now lists all 33 agents, 43 skills, 22 steering files, and 13 hooks (was showing old subset) - kotlin-reviewer.md: restore git diff --staged / git diff for local pre-commit review before falling back to HEAD~1 - django-reviewer.md: clarify that ForeignKey fields are indexed by default; only flag missing db_index on non-FK filter columns
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name, description, origin
| name | description | origin |
|---|---|---|
| deep-research | Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations. | ECC |
Deep Research
Drift-prone skill. Firecrawl/Exa MCP tool names, quotas, and result shapes change. Verify the configured MCP tools and current API docs before promising coverage or quoting live source counts.
Produce thorough, cited research reports from multiple web sources using firecrawl and exa MCP tools.
When to Activate
- User asks to research any topic in depth
- Competitive analysis, technology evaluation, or market sizing
- Due diligence on companies, investors, or technologies
- Any question requiring synthesis from multiple sources
- User says "research", "deep dive", "investigate", or "what's the current state of"
MCP Requirements
At least one of:
- firecrawl —
firecrawl_search,firecrawl_scrape,firecrawl_crawl - exa —
web_search_exa,web_search_advanced_exa,crawling_exa
Both together give the best coverage. Configure in ~/.claude.json or ~/.codex/config.toml.
Workflow
Step 1: Understand the Goal
Ask 1-2 quick clarifying questions:
- "What's your goal — learning, making a decision, or writing something?"
- "Any specific angle or depth you want?"
If the user says "just research it" — skip ahead with reasonable defaults.
Step 2: Plan the Research
Break the topic into 3-5 research sub-questions. Example:
- Topic: "Impact of AI on healthcare"
- What are the main AI applications in healthcare today?
- What clinical outcomes have been measured?
- What are the regulatory challenges?
- What companies are leading this space?
- What's the market size and growth trajectory?
Step 3: Execute Multi-Source Search
For EACH sub-question, search using available MCP tools:
With firecrawl:
firecrawl_search(query: "<sub-question keywords>", limit: 8)
With exa:
web_search_exa(query: "<sub-question keywords>", numResults: 8)
web_search_advanced_exa(query: "<keywords>", numResults: 5, startPublishedDate: "2025-01-01")
Search strategy:
- Use 2-3 different keyword variations per sub-question
- Mix general and news-focused queries
- Aim for 15-30 unique sources total
- Prioritize: academic, official, reputable news > blogs > forums
Step 4: Deep-Read Key Sources
For the most promising URLs, fetch full content:
With firecrawl:
firecrawl_scrape(url: "<url>")
With exa:
crawling_exa(url: "<url>", tokensNum: 5000)
Read 3-5 key sources in full for depth. Do not rely only on search snippets.
Step 5: Synthesize and Write Report
Structure the report:
# [Topic]: Research Report
*Generated: [date] | Sources: [N] | Confidence: [High/Medium/Low]*
## Executive Summary
[3-5 sentence overview of key findings]
## 1. [First Major Theme]
[Findings with inline citations]
- Key point ([Source Name](url))
- Supporting data ([Source Name](url))
## 2. [Second Major Theme]
...
## 3. [Third Major Theme]
...
## Key Takeaways
- [Actionable insight 1]
- [Actionable insight 2]
- [Actionable insight 3]
## Sources
1. [Title](url) — [one-line summary]
2. ...
## Methodology
Searched [N] queries across web and news. Analyzed [M] sources.
Sub-questions investigated: [list]
Step 6: Deliver
- Short topics: Post the full report in chat
- Long reports: Post the executive summary + key takeaways, save full report to a file
Parallel Research with Subagents
For broad topics, use Claude Code's Task tool to parallelize:
Launch 3 research agents in parallel:
1. Agent 1: Research sub-questions 1-2
2. Agent 2: Research sub-questions 3-4
3. Agent 3: Research sub-question 5 + cross-cutting themes
Each agent searches, reads sources, and returns findings. The main session synthesizes into the final report.
Quality Rules
- Every claim needs a source. No unsourced assertions.
- Cross-reference. If only one source says it, flag it as unverified.
- Recency matters. Prefer sources from the last 12 months.
- Acknowledge gaps. If you couldn't find good info on a sub-question, say so.
- No hallucination. If you don't know, say "insufficient data found."
- Separate fact from inference. Label estimates, projections, and opinions clearly.
Examples
"Research the current state of nuclear fusion energy"
"Deep dive into Rust vs Go for backend services in 2026"
"Research the best strategies for bootstrapping a SaaS business"
"What's happening with the US housing market right now?"
"Investigate the competitive landscape for AI code editors"