Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add joestump/claude-plugin-sdd/plugin install sddWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/joestump/claude-plugin-sdd/index)<a href="https://agentmods.dev/skills/joestump/claude-plugin-sdd/index"><img src="https://agentmods.dev/badge/skills/joestump/claude-plugin-sdd/index.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00082 | $0.11416 |
| Opus 5 | $0.00041 | $0.05708 |
| Sonnet 5 | $0.00016 | $0.02283 |
| Haiku 4.5 | $0.00008 | $0.01142 |
Grade B, and why
index scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
6. **qmd install writability** (non-blocking; surfaces as report warning). When qmd was installed via `sudo npm install -g`, its `node-llama-cpp` GPU build artifacts cannot be written under `/usr/lib/node_modules/...` by How it starts
The opening of the file, as written. The whole thing — 565 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Index Repository into QMD
Harness portability. This skill runs on any agent harness that loads Agent Skills — Claude Code, Codex CLI, OpenCode, Crush. Tool names used below (
AskUserQuestion,Task,TeamCreate,SendMessage,TaskCreate,ToolSearch,mcp__*,${CLAUDE_PLUGIN_ROOT}) denote capabilities, not hard requirements: map each to your harness's equivalent or use the documented fallback per${CLAUDE_PLUGIN_ROOT}/references/harness-compat.md. References toCLAUDE.mdmean the project memory file (CLAUDE.md,AGENTS.md, orCRUSH.md) per harness-compat § "Project Memory File". A citation of the formshared-patterns.md § "Section"names one##heading in that file — load only that section (see its "How to Read This File" note), never the whole file.
Create per-repository qmd collections so agents and humans can run hybrid search across a repo's ADRs, OpenSpec specs, source code, and tracker issues from a single query plane. Each repository owns four collections ({repo}-adrs, {repo}-specs, {repo}-code, {repo}-issues) so searches can be filtered cleanly with qmd query "..." -c {repo}-adrs. The issues collection is populated by syncing the configured tracker into .sdd/issues/{id}.md files (per ADR-0025 and ${CLAUDE_PLUGIN_ROOT}/references/tracker-sync.md). Workspace projects (ADR-0016) get one set of collections per module: {repo}-{module}-{kind}.
Process
- Resolve artifact paths: Follow the Artifact Path Resolution pattern from
${CLAUDE_PLUGIN_ROOT}/references/shared-patterns.md§ "Artifact Path Resolution". If$ARGUMENTScontains--module <name>, scope to that module; otherwise, in a workspace, iterate all modules. The resolved ADR directory is{adr-dir}and spec directory is{spec-dir}, both per-module in workspace mode.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago Changed 93867777035a
- 6d ago First seen · 565 lines · 82 tokens per session scan B ab53280add1e
index is a skill published in the GitHub repository joestump/claude-plugin-sdd (32 stars, last pushed yesterday), licensed MIT. It adds 82 tokens to every session and 11,416 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.