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Domain

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notque
domain

Domain-specific: SAP Commerce, OpenSearch detection, WordPress validation, enterprise search.

Overview

Publishernotque
Repositoryvexjoy-agent
Skill namedomain
Stars
443
Forks
50
Bundled files
14
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 14 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by notque on GitHub. Read the source before you install it.

Installation

Install the Domain AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/notque/vexjoy-agent/tree/main/skills/domain/domain
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/notque/vexjoy-agent.git /tmp/vexjoy-agent
mkdir -p .claude/skills
cp -r /tmp/vexjoy-agent/skills/domain/domain .claude/skills/domain
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Domain in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Domain on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Domain is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Domain-Specific Skills

Five domains: SAP Commerce Go review, SAP Commerce compliance audit, OpenSearch SIEM detection engineering, WordPress live validation, and enterprise search. Classify the request into one domain, then follow its section.

Mode Detection

DomainSignalAgent
SAPCC Reviewsapcc review, 10-specialist review, lead reviewgolang-general-engineer
SAPCC Auditsapcc audit, sapcc compliance, full repo auditgolang-general-engineer
OpenSearch DetectionSIEM, SIGMA, MITRE, detection engineering, SOC(this session)
WordPress Validationvalidate wordpress post, check live post, post rendering(this session)
Enterprise Searchsearch relevance, ranking, BM25, query understandingopensearch-elasticsearch-engineer

SAPCC Review

10-agent domain-specialist review. Each agent masters one rule domain and scans every package. Differs from SAPCC Audit: audit segments by package (generalist), review segments by rule domain (specialist, cross-package).

Phase 1: DISCOVER

Verify sapcc project and map the repo.

bash
head -5 go.mod && grep -c "sapcc" go.mod
find . -name "*.go" -not -path "*/vendor/*" | wc -l
find . -name "*.go" -not -path "*/vendor/*" | sed 's|/[^/]*$||' | sort | uniq -c | sort -rn

Check key imports: go-bits, go-api-declarations, gophercloud, gorilla/mux, database/sql.

Gate: Repo mapped. If no sapcc imports, warn but continue.

Phase 2: DISPATCH

Load references/sapcc-review-agent-dispatch-prompts.md for the 10 agent specs.

Dispatch all 10 in ONE message via Agent tool. Each agent gets: path to sapcc-code-patterns.md, assigned sections, domain-specific reference, all .go files to scan, finding output format.

Gate: All 10 dispatched in single message.

Phase 3: AGGREGATE

Run git status --short to capture modified and untracked files. Collect all findings. Deduplicate by file:line (keep higher severity). Apply severity boosts:

Pattern StrengthBoost
NON-NEGOTIABLE (4+ repos)+1 level
Strong Signal (2-3 repos)No change
Context-Specific (1 repo)-1 level

Mark quick wins (single-line, no behavioral change, low test risk). Write sapcc-review-report.md with: verdict, scorecard (10 domains x severity), quick wins, findings by severity, positives, systemic recommendations.

Phase 4: FIX (only with --fix)

Create worktree sapcc-review-fixes. Apply quick wins first. After each group: go build ./... && go vet ./... && make check 2>/dev/null || go test ./.... If fix breaks tests, revert and note. Commit as fix: apply sapcc-review findings (N fixes across M files).


SAPCC Audit

Full-repo compliance scan. Segments by package (generalist per package).

Phase 1: DISCOVER

Verify sapcc project (grep "sapcc" go.mod). Map packages: find . -name "*.go" -not -path "./vendor/*" | sed 's|/[^/]*$||' | sort -u. Count files per package. Plan 5-8 agents, 5-15 files each.

Gate: Packages mapped, agents planned.

Phase 2: DISPATCH

Load references/sapcc-audit-phase-2-dispatch-agents.md for the dispatch prompt (11 review areas: over-engineering, dead code, error messages, constructors, interface contracts, copy-paste, HTTP handlers, database patterns, type patterns, logging, mixed approaches). Dispatch all in one message via Task tool with subagent_type=golang-general-engineer.

Phase 3: COMPILE REPORT

Deduplicate by file:line. Write sapcc-audit-report.md with: verdict, must-fix/should-fix/nit counts, per-package summary table. Display verdict, must-fix count, and top 5 findings inline.

Finding format: [MUST-FIX/SHOULD-FIX/NIT]: summary with file:line, current code, correct code, and rationale.

Audit only: reads and reports. Does not modify code unless --fix.


OpenSearch Detection Engineering

SIEM detection authoring and validation on OpenSearch Security Analytics: SIGMA rules, query DSL translation, MITRE ATT&CK mapping, anomaly detection, correlation, and SOC incident escalation.

Hardcoded Behaviors

  • MITRE ATT&CK on every detection. Include technique ID (e.g., T1110.003)
    • tactic name + kill chain phase. Tactic alone is insufficient.
  • Field-existence check before rule creation. Run GET {index}/_mapping; confirm every rule field exists. Absent fields cause silent failure.
  • Concrete API commands. Provide PUT _mapping, POST _aliases, not abstract advice.
  • Escalation validation. Verify all 9 fields before escalation: ticket ID, alert link, MITRE mapping, timeline, investigation actions, impact analysis, evidence artifacts, containment recommendation, 5 Ws.
  • Severity tier = binding SLA. Not advisory targets.
  • Detection-owned index. When bootstrapping field aliases, recommend a dedicated index separate from the ingestion datastream.

Hard Gates

PatternFix
Rule field absent from index mappingGET {index}/_mapping; confirm or add field
MITRE mapping missing technique ID or tacticSpecify both T####.### and tactic
Escalation missing any of 9 fieldsComplete all fields per checklist
Chained findings monitor on high-frequency scheduleUse static query indices
Field alias bootstrap on shared datastreamCreate detection-owned index

Workflow

  1. Scope: Identify attack scenario, data source, severity tier. Map to MITRE ATT&CK. Confirm log source is ingested.
  2. Validate fields: GET {index}/_mapping for each rule field. Check cardinality for terms aggregations.
  3. Author: Write SIGMA rule (vendor-neutral), translate to OpenSearch DSL. Load references/opensearch-detection-engineering.md for translation patterns. Apply FP suppression (CIDR, service-account prefixes, time windows).
  4. Safety check: Check for index flood, alias bootstrap risk. Load references/opensearch-detection-safety-patterns.md for the full checklist.
  5. Document: 6-section use case (General Info, Context, Outcomes, Detection Logic, Continuous Improvement, Analyst Support). Load references/opensearch-incident-escalation.md for template and KPIs.
  6. Calibrate: Dry-run 5 business days, label TPs/FPs, adjust until FP rate <= 10%.
  7. Escalation (when alert fires): Build 9-field package, apply SLA, hand off per RACI.

Verification STOP Blocks

After authoring: "Have I verified every field via GET {index}/_mapping?" After escalation: "Does the package include all 9 fields?" After chained monitor: "Does this create a new query index per run?" After MITRE mapping: "Did I include both technique ID and tactic?"


WordPress Live Validation

Loads a published WordPress post in a headless browser and verifies rendering matches what was uploaded. The browser is the source of truth.

Browser backend: Playwright MCP (default). Chrome DevTools MCP when the user says "check in my browser" or wants Lighthouse/performance profiling.

Constraints

  • Read-only. Never click, type, or modify the WordPress site.
  • Evidence-based. Every result references a DOM value, network response, or screenshot. No "looks fine."
  • Non-blocking. Failed validation produces a report; does not revert uploads.
  • Severity: BLOCKER (broken content), WARNING (degraded but functional), INFO (informational).
  • Requires Playwright MCP or Chrome DevTools MCP. If neither available, skip.

Phase 1: NAVIGATE

Load references/wordpress-phase-checks.md for the 4-step procedure. Navigate to URL, wait for content area (try selectors: article -> .entry-content -> .post-content -> main), remove cookie banners.

Gate: HTTP 200, content selector found. If 4xx/5xx or no selector: screenshot, FAIL, STOP.

Phase 2: VALIDATE

Load references/wordpress-validation-checks.md for severity rationale and edge cases. Load references/wordpress-playwright-tools.md for tool signatures.

Run all 7 checks:

CheckSeverity
Title matchBLOCKER
H2 structureWARNING
Image loadingBLOCKER
JS console errorsWARNING
OG tagsWARNING
Meta descriptionWARNING
Placeholder/draft textBLOCKER

Execute each check via browser tools. Do not reason about outcomes -- run the command and report observed results.

Gate: All 7 checks executed with severity and evidence.

Phase 3: RESPONSIVE CHECK

Test three viewports: mobile (375x812), tablet (768x1024), desktop (1440x900). Per viewport: resize, screenshot, check overflow, check container visibility. See references/wordpress-phase-checks.md for JS snippets.

Phase 4: REPORT

Output structured report:

LIVE VALIDATION: {url}
CONTENT INTEGRITY: [PASS/FAIL/WARN] per check with evidence
SEO / SOCIAL: OG tags, meta description with values
RESPONSIVE: per viewport with overflow status and screenshot path
RESULT: {PASS | FAIL - N blockers, M warnings}

Error Handling

ErrorResponse
Playwright MCP unavailableSkip report, do not retry
4xx/5xxScreenshot, report HTTP status, STOP at Phase 1
Content selector not foundScreenshot + DOM snapshot; attempt OG checks without selector
Image network timeoutReport; if all fail, note possible CDN issue
Cookie banner blocks contentPhase 1 attempts DOM removal; DOM checks still work

Enterprise Search

Search infrastructure: relevance tuning, query understanding, index management, quality measurement, performance optimization. Always specify target platform and version.

Sub-mode Detection

ModeSignalLoad
RELEVANCEBM25, boost, LTR, rankingreferences/search-relevance-tuning.md
QUERYintent, entity extraction, expansion, synonymsreferences/search-query-understanding.md
INDEXschema, mapping, analyzer, reindex, ILMreferences/search-index-management.md
QUALITYnDCG, MRR, judgments, A/B testreferences/search-search-quality.md
PERFORMANCEslow query, shard, cache, circuit breakerreferences/search-performance-optimization.md
ARCHITECTUREhybrid search, vector search, platform selectionLoad per sub-topic

Always load references/search-llm-search-failure-modes.md as a guardrail.

Shared Workflow Pattern

  1. Diagnose the problem class before acting.
  2. Baseline current metrics. No tuning without measurement.
  3. Change one variable at a time.
  4. Validate against baseline. Accept only statistically significant improvements.

Platform Conventions

PlatformQuery LanguageConfig
Elasticsearch 8.xQuery DSL (JSON)elasticsearch.yml
OpenSearch 2.xQuery DSL (JSON)opensearch.yml
Solr 9.xSolrQL / JSON Request APIsolrconfig.xml
VespaYQLservices.xml
TypesenseREST paramsCLI / JSON

Cross-platform traps: OpenSearch diverges from ES 7.10 on security/ML/alerting. ES _field_caps changed between 7.x and 8.x. Solr edismax != ES multi_match.

Output Rules

  • All query DSL in fenced blocks with platform + version annotation.
  • Every recommendation: what to change, why, expected effect, how to measure.
  • Configuration snippets must be copy-pasteable with comments.

Deep References

Load on demand when a phase needs detailed lookup data.

ContextReference
SAPCC Review Phase 2: 10 agent specsreferences/sapcc-review-agent-dispatch-prompts.md
SAPCC Audit Phase 2: dispatch promptreferences/sapcc-audit-phase-2-dispatch-agents.md
SIGMA authoring, DSL translation, MITRE catalogreferences/opensearch-detection-engineering.md
Detector failures, alias conflicts, index floodreferences/opensearch-detection-safety-patterns.md
Escalation checklist, severity SLAs, KPIsreferences/opensearch-incident-escalation.md
WordPress check specs, severities, edge casesreferences/wordpress-validation-checks.md
WordPress Playwright tool signaturesreferences/wordpress-playwright-tools.md
WordPress phase procedures with JS snippetsreferences/wordpress-phase-checks.md
Search relevance tuning, BM25, LTR, boostsreferences/search-relevance-tuning.md
Query understanding, intent, expansionreferences/search-query-understanding.md
Index management, schema, analyzers, ILMreferences/search-index-management.md
Search quality metrics, evaluation methodologyreferences/search-search-quality.md
Search performance, caching, shardingreferences/search-performance-optimization.md
LLM failure modes in search engineeringreferences/search-llm-search-failure-modes.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Domain AI skill do?

Domain-specific: SAP Commerce, OpenSearch detection, WordPress validation, enterprise search.

Why use Domain on TypingMind?

Because you install it once and use it with any model. Domain is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Domain in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/notque/vexjoy-agent/tree/main/skills/domain/domain. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Domain?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Domain?

As many as you like. As long as a model supports skills, you can use Domain with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Domain AI skill free?

Yes. It is published on GitHub by notque under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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