refactor(agents): relocate example blocks to body (researchers + gemini-bridge)

This commit is contained in:
Kjell Tore Guttormsen 2026-06-29 10:14:35 +02:00
commit 816bf2a5fc
5 changed files with 109 additions and 99 deletions

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@ -4,26 +4,6 @@ description: |
Use this agent when the research task requires practical, real-world experience rather
than official documentation — community sentiment, production war stories, known gotchas,
and what developers actually encounter when using a technology.
<example>
Context: trekresearch needs real-world experience data on a database migration
user: "/trekresearch What's the real-world experience with migrating from MongoDB to PostgreSQL?"
assistant: "Launching community-researcher to find migration stories, GitHub discussions, and community experience reports."
<commentary>
Official docs won't cover migration regrets or production war stories. community-researcher
targets GitHub issues, blog posts, and discussions where real experience lives.
</commentary>
</example>
<example>
Context: trekresearch is building a technology comparison
user: "/trekresearch Research community sentiment around adopting SvelteKit vs Next.js"
assistant: "I'll use community-researcher to find discussions, blog posts, and community reports on both frameworks."
<commentary>
Framework comparisons live in community discourse, not official docs. community-researcher
finds the practical signal that helps teams make adoption decisions.
</commentary>
</example>
model: opus
color: green
tools: ["WebSearch", "WebFetch", "mcp__tavily__tavily_search", "mcp__tavily__tavily_research"]
@ -133,3 +113,25 @@ End with a summary table:
Do not pick a side — report the split.
- **Flag if a "problem" has since been fixed.** Check if the issue/complaint references a
version that has since been patched or superseded.
## When to use — examples
<example>
Context: trekresearch needs real-world experience data on a database migration
user: "/trekresearch What's the real-world experience with migrating from MongoDB to PostgreSQL?"
assistant: "Launching community-researcher to find migration stories, GitHub discussions, and community experience reports."
<commentary>
Official docs won't cover migration regrets or production war stories. community-researcher
targets GitHub issues, blog posts, and discussions where real experience lives.
</commentary>
</example>
<example>
Context: trekresearch is building a technology comparison
user: "/trekresearch Research community sentiment around adopting SvelteKit vs Next.js"
assistant: "I'll use community-researcher to find discussions, blog posts, and community reports on both frameworks."
<commentary>
Framework comparisons live in community discourse, not official docs. community-researcher
finds the practical signal that helps teams make adoption decisions.
</commentary>
</example>

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@ -4,26 +4,6 @@ description: |
Use this agent when the research task has an emerging conclusion that needs adversarial
stress-testing — find counter-evidence, overlooked alternatives, and reasons the leading
answer might be wrong.
<example>
Context: trekresearch has found evidence favoring a technology and needs the other side
user: "/trekresearch We're leaning toward adopting Kafka for our event streaming needs"
assistant: "Launching contrarian-researcher to find the strongest arguments against Kafka and what alternatives might serve better."
<commentary>
The research equivalent of plan-critic. When one option is emerging as the answer,
contrarian-researcher actively seeks disconfirming evidence to pressure-test the conclusion.
</commentary>
</example>
<example>
Context: trekresearch is comparing options and needs the downsides of the leading candidate
user: "/trekresearch Compare Redis vs Memcached — initial research favors Redis"
assistant: "I'll use contrarian-researcher to find the strongest case against Redis and scenarios where Memcached wins."
<commentary>
Contrarian-researcher finds the downsides of the leading option — not to be negative,
but to ensure the final recommendation is genuinely considered.
</commentary>
</example>
model: opus
effort: high
color: red
@ -152,3 +132,25 @@ Followed by a **Verdict** section:
apply to a read-heavy workload. Assess relevance before reporting.
- **Check recency.** A problem from 2019 that the project fixed in 2021 is not current
counter-evidence. Flag whether issues are current or historical.
## When to use — examples
<example>
Context: trekresearch has found evidence favoring a technology and needs the other side
user: "/trekresearch We're leaning toward adopting Kafka for our event streaming needs"
assistant: "Launching contrarian-researcher to find the strongest arguments against Kafka and what alternatives might serve better."
<commentary>
The research equivalent of plan-critic. When one option is emerging as the answer,
contrarian-researcher actively seeks disconfirming evidence to pressure-test the conclusion.
</commentary>
</example>
<example>
Context: trekresearch is comparing options and needs the downsides of the leading candidate
user: "/trekresearch Compare Redis vs Memcached — initial research favors Redis"
assistant: "I'll use contrarian-researcher to find the strongest case against Redis and scenarios where Memcached wins."
<commentary>
Contrarian-researcher finds the downsides of the leading option — not to be negative,
but to ensure the final recommendation is genuinely considered.
</commentary>
</example>

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@ -3,26 +3,6 @@ name: docs-researcher
description: |
Use this agent when the research task requires authoritative information from official
documentation, RFCs, vendor specifications, or Microsoft/Azure documentation.
<example>
Context: trekresearch needs to ground an OAuth2 implementation in official specs
user: "/trekresearch Research OAuth2 PKCE flow for our SPA"
assistant: "Launching docs-researcher to find the official RFC and vendor documentation for OAuth2 PKCE."
<commentary>
docs-researcher targets authoritative sources — RFCs, specs, official vendor docs —
not community opinions. This is the right agent for protocol and standards questions.
</commentary>
</example>
<example>
Context: trekresearch encounters an Azure-specific technology
user: "/trekresearch How should we configure Azure Service Bus for our event pipeline?"
assistant: "I'll use docs-researcher with Microsoft Learn to get authoritative Azure Service Bus documentation."
<commentary>
Microsoft/Azure technologies have dedicated MCP tools (microsoft_docs_search,
microsoft_docs_fetch) that docs-researcher uses for higher-quality results.
</commentary>
</example>
model: opus
color: blue
tools: ["WebSearch", "WebFetch", "Read", "mcp__tavily__tavily_search", "mcp__tavily__tavily_research", "mcp__microsoft-learn__microsoft_docs_search", "mcp__microsoft-learn__microsoft_docs_fetch"]
@ -119,3 +99,25 @@ End with a summary table:
- **Flag conflicts between official sources.** When vendor docs and the spec disagree, report both.
- **Stay focused.** Research only what the research question asks. Do not explore tangentially.
- **Official sources only.** If you cannot find an official source, say so — do not substitute a blog post.
## When to use — examples
<example>
Context: trekresearch needs to ground an OAuth2 implementation in official specs
user: "/trekresearch Research OAuth2 PKCE flow for our SPA"
assistant: "Launching docs-researcher to find the official RFC and vendor documentation for OAuth2 PKCE."
<commentary>
docs-researcher targets authoritative sources — RFCs, specs, official vendor docs —
not community opinions. This is the right agent for protocol and standards questions.
</commentary>
</example>
<example>
Context: trekresearch encounters an Azure-specific technology
user: "/trekresearch How should we configure Azure Service Bus for our event pipeline?"
assistant: "I'll use docs-researcher with Microsoft Learn to get authoritative Azure Service Bus documentation."
<commentary>
Microsoft/Azure technologies have dedicated MCP tools (microsoft_docs_search,
microsoft_docs_fetch) that docs-researcher uses for higher-quality results.
</commentary>
</example>

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@ -5,25 +5,6 @@ description: |
needed on a technology choice, architectural question, or complex research topic.
Provides triangulation value by running a completely independent research path
that can confirm or challenge findings from other agents.
<example>
Context: trekresearch launches gemini-bridge for an independent second opinion on a technology choice
user: "/trekplan Should we use Kafka or NATS for our event streaming layer?"
assistant: "Launching gemini-bridge for an independent second opinion on Kafka vs NATS."
<commentary>
Technology choice with significant architectural implications triggers gemini-bridge
to provide an independent research path alongside local exploration agents.
</commentary>
</example>
<example>
Context: user wants deep research via Gemini on a complex architectural question
user: "Get me a Gemini deep research on event sourcing patterns for distributed systems"
assistant: "I'll use the gemini-bridge agent to run a deep research on event sourcing patterns."
<commentary>
Direct request for Gemini research on a complex architectural question triggers the agent.
</commentary>
</example>
model: opus
color: magenta
tools: ["mcp__gemini-mcp__gemini_deep_research", "mcp__gemini-mcp__gemini_get_research_status", "mcp__gemini-mcp__gemini_get_research_result", "mcp__gemini-mcp__gemini_research_followup"]
@ -147,3 +128,24 @@ and other external agents:*
- **Graceful degradation at every step.** Unavailable tool, failed research, timeout —
all are handled with a clear status message and immediate return. Never leave the
pipeline hanging.
## When to use — examples
<example>
Context: trekresearch launches gemini-bridge for an independent second opinion on a technology choice
user: "/trekplan Should we use Kafka or NATS for our event streaming layer?"
assistant: "Launching gemini-bridge for an independent second opinion on Kafka vs NATS."
<commentary>
Technology choice with significant architectural implications triggers gemini-bridge
to provide an independent research path alongside local exploration agents.
</commentary>
</example>
<example>
Context: user wants deep research via Gemini on a complex architectural question
user: "Get me a Gemini deep research on event sourcing patterns for distributed systems"
assistant: "I'll use the gemini-bridge agent to run a deep research on event sourcing patterns."
<commentary>
Direct request for Gemini research on a complex architectural question triggers the agent.
</commentary>
</example>

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@ -3,26 +3,6 @@ name: security-researcher
description: |
Use this agent when the research task requires security investigation of a technology,
dependency, or library — CVEs, audit history, supply chain risks, and OWASP relevance.
<example>
Context: trekresearch is evaluating whether a dependency is safe to adopt
user: "/trekresearch Research whether we should trust the `node-fetch` library"
assistant: "Launching security-researcher to check CVE history, supply chain risk, and audit reports for node-fetch."
<commentary>
Before adopting a dependency, security-researcher checks the attack surface: known
vulnerabilities, maintainer health, and whether past issues were handled responsibly.
</commentary>
</example>
<example>
Context: trekresearch is assessing the security posture of a technology choice
user: "/trekresearch Evaluate the security implications of using JWT for session management"
assistant: "I'll use security-researcher to check known JWT vulnerabilities, OWASP guidance, and community security reports."
<commentary>
Technology choices have security tradeoffs. security-researcher maps the threat surface
using CVE databases, OWASP categories, and verified audit reports.
</commentary>
</example>
model: opus
color: red
tools: ["WebSearch", "WebFetch", "mcp__tavily__tavily_search", "mcp__tavily__tavily_research"]
@ -140,3 +120,25 @@ End with an overall security summary table:
risks from incomplete information.
- **Severity matters.** A CVSS 9.8 is not equivalent to a CVSS 3.2 — report scores
and distinguish between critical and low-severity findings.
## When to use — examples
<example>
Context: trekresearch is evaluating whether a dependency is safe to adopt
user: "/trekresearch Research whether we should trust the `node-fetch` library"
assistant: "Launching security-researcher to check CVE history, supply chain risk, and audit reports for node-fetch."
<commentary>
Before adopting a dependency, security-researcher checks the attack surface: known
vulnerabilities, maintainer health, and whether past issues were handled responsibly.
</commentary>
</example>
<example>
Context: trekresearch is assessing the security posture of a technology choice
user: "/trekresearch Evaluate the security implications of using JWT for session management"
assistant: "I'll use security-researcher to check known JWT vulnerabilities, OWASP guidance, and community security reports."
<commentary>
Technology choices have security tradeoffs. security-researcher maps the threat surface
using CVE databases, OWASP categories, and verified audit reports.
</commentary>
</example>