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 agentmods add skills/xbluesky/cortexes/cortex-querynpx skills add XBlueSky/cortexes --skill cortex-querygit clone --depth 1 https://github.com/XBlueSky/cortexesWhat 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 | $0.00157 | $0.00756 |
| Opus 5 | $0.00078 | $0.00378 |
| Sonnet 5 | $0.00031 | $0.00151 |
| Haiku 4.5 | $0.00016 | $0.00076 |
Grade A, and why
cortex-query scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cortex Query — Search the Vault
Search the cortex Obsidian vault using semantic search.
Resolve Vault Path
Read ~/.cortex/config.json to get vault_path.
If the file doesn't exist, tell the user to run /cortex:genesis first.
Search Strategy (Layered)
Layer 1: Vector Search (primary)
Use cortex-vec for semantic search:
cortex-vec search "<query>" --n 5
cortex-vec is installed as a CLI tool (from PyPI via uv tool install cortex-vec or pip — see the README's Quick Start; /cortex:genesis offers
the install when it is missing).
Context-aware filtering: If the current session is inside a git repo,
detect the repo name and add --repo filter as default scope:
cortex-vec search "<query>" --repo <detected-repo> --n 5
The user can override this by saying "search all" or "search across everything".
Additional filters: Apply when the user specifies:
--type note|project|weekly— filter by content type--category Nginx|Linux|...— filter by category
Interpreting scores:
- Score > 0.80: High confidence match — present prominently
- Score 0.60-0.80: Possible match — present as suggestions
- Score < 0.60: Weak match — mention only if nothing better found
Layer 2: Exact Match (supplement)
If Layer 1 returns no strong results (all scores < 0.60), or if the user is searching for an exact string (command, config path, error message):
grep -ri "<query>" <vault_path>/Notes/ <vault_path>/Projects/
Show matching files with brief excerpts.
Layer 3: Raw Search (archive, on request)
Only when the user specifically asks about recent sessions or raw data:
grep -ri "<query>" <vault_path>/Raw/
Show matches with date and repo context.
Response Format
Present results to the user:
Found N results for "<query>":
1. [score] Title (Type, Category/Repo)
→ one-line summary
2. [score] Title (Type, Category/Repo)
→ one-line summary
- Use wikilink format when referencing notes:
[[note-name]] - If multiple matches, list them and ask which one to read
- If user wants details → read the full file
- For Weekly entries, show the date and summary line
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 First seen · 97 lines · 157 tokens per session scan A c7e216924da6
cortex-query is a skill published in the GitHub repository XBlueSky/cortexes (20 stars, last pushed 26d ago), licensed Apache-2.0. It adds 157 tokens to every session and 756 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
vera
Code search over the current repository. Before reading files to answer "where is X", "how does Y work", "find Z", or "what calls W", run vera search " " or vera references first. Use vera grep for exact strings and regex, vera structural for definitions, routes, and env reads. Do not read multiple files hoping to…
semantic-search
Code search for this repo — pick the right Beacon tool for the question: exact symbol lookup, reference tracing, file outline, or semantic search.
release-gatekeeper
End-to-end release validation for Connapse — the 'final boss' before any version ships. Downloads the latest alpha from GitHub Releases, deploys an isolated Docker instance (separate from production), then systematically tests every feature: UI via Playwright, API via curl/REST, MCP tools, search quality, security…
create-tickets
Batch-create GitHub issues from a brainstorming discussion. Decomposes ideas into properly-sized tickets with labels, milestones, and project board placement. Trigger when user says: create tickets, make tickets, turn ideas into issues, create issues from brainstorm, create issues from discussion, batch create issues.
discover-work
Deep research across codebase, GitHub issues, discussions, project board, and architecture docs to discover new tasks, gaps, technical debt, and improvement ideas. Trigger when user says: discover work, find tasks, what needs doing, audit codebase, find gaps, technical debt audit, backlog discovery, brainstorm tasks…
next-task
Recommend the best next task to work on based on open GitHub issues. Analyzes priority, dependencies, milestone urgency, and codebase readiness. Trigger when user asks: what should I work on, what is next, next task, pick a task, what to do next, suggest work, prioritize tasks.