Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add alrcatraz/astra-aigate --skill omni-context-rtkgit clone --depth 1 https://github.com/alrcatraz/astra-aigateWrote 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/alrcatraz/astra-aigate/omni-context-rtk)<a href="https://agentmods.dev/skills/alrcatraz/astra-aigate/omni-context-rtk"><img src="https://agentmods.dev/badge/skills/alrcatraz/astra-aigate/omni-context-rtk/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alrcatraz/astra-aigate/omni-context-rtk"><img src="https://agentmods.dev/badge/skills/alrcatraz/astra-aigate/omni-context-rtk.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00033 | $0.00542 |
| Opus 5 | $0.00016 | $0.00271 |
| Sonnet 5 | $0.00007 | $0.00108 |
| Haiku 4.5 | $0.00003 | $0.00054 |
Grade A, and why
omni-context-rtk scanned grade A 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl https://localhost:20128/api/context/rtk/config \ This is a copy
100% identical to omni-context-rtk — 24 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Overview
Configure RTK filters, context engineering rules, and context relay settings. Test compression with real prompt samples and manage context transformation pipelines.
Authentication
All requests require a valid Bearer token or session cookie. Obtain a token via POST /api/auth/login or configure REQUIRE_API_KEY=false for local development.
Endpoints
GET /api/context/rtk/config
Get RTK compression settings
curl https://localhost:20128/api/context/rtk/config \
-H "Authorization: Bearer $OMNIROUTE_TOKEN"
PUT /api/context/rtk/config
Update RTK compression settings
curl -X PUT https://localhost:20128/api/context/rtk/config \
-H "Authorization: Bearer $OMNIROUTE_TOKEN"
-H "Content-Type: application/json" \
-d '{}'
GET /api/context/rtk/filters
List RTK filters and load diagnostics
curl https://localhost:20128/api/context/rtk/filters \
-H "Authorization: Bearer $OMNIROUTE_TOKEN"
POST /api/context/rtk/import
Validate or install an RTK TOML schema v1 filter file
curl -X POST https://localhost:20128/api/context/rtk/import \
-H "Authorization: Bearer $OMNIROUTE_TOKEN"
-H "Content-Type: application/json" \
-d '{}'
POST /api/context/rtk/test
Run RTK compression preview for text
curl -X POST https://localhost:20128/api/context/rtk/test \
-H "Authorization: Bearer $OMNIROUTE_TOKEN"
-H "Content-Type: application/json" \
-d '{}'
GET /api/context/rtk/raw-output/{id}
Read retained redacted RTK raw output
curl https://localhost:20128/api/context/rtk/raw-output/{id} \
-H "Authorization: Bearer $OMNIROUTE_TOKEN"
Payloads
See the full OpenAPI specification at GET /api/openapi/spec or docs/openapi.yaml for detailed request/response schemas.
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.
- 10d ago First seen · 80 lines · 33 tokens per session scan A 1882e1910047
omni-context-rtk is a skill published in the GitHub repository alrcatraz/astra-aigate (1 stars, last pushed 3d ago), licensed MIT. It adds 33 tokens to every session and 542 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to omni-context-rtk, differing in 24 lines, and is treated as a copy.
Other skills, from other repositories
context-injection
Place trusted contextual information into prompts or agent state using explicit boundaries, provenance, and templates. Use when relevant context has already been selected and must be inserted safely; use context-retrieval to find it or context-optimization to choose and order it.
prompt-engineering
Use when advanced prompt engineering — chain-of-thought, few-shot, tree-of-thought, self-consistency, meta-prompting, system design, debugging, and optimization for production AI systems. Use when working with prompt engineering.
dify-workflow
Use when dify AI workflow platform — LLM apps, knowledge bases, agents, workflow orchestration, API deployment. Use when working with dify workflow.
bootstrap-llm-synthesis
Construct the LLM synthesis prompt from project surface scan + optional tree-sitter context + optional Q&A answers. Call the LLM. Parse and validate the response into 6-8 structured memory entries with clarity tags and source traceability. Used as Stage 3 of the /gaai:bootstrap pipeline.
context-repo-mcp
Search, retrieve, version, and persist prompts, documents, and collections through the hosted Context Repo MCP. Use this skill whenever the user mentions Context Repo, saved prompts, prompt templates, stored documents, collections, semantic search, or hierarchical document navigation.
ai-native-context-engineering
A workflow for designing how an AI system receives and manages information for a task. It covers choosing context, controlling the amount of information, and checking the result.