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 christopherkarani/Wax --skill maintenancegit clone --depth 1 https://github.com/christopherkarani/WaxWrote 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/christopherkarani/wax/maintenance)<a href="https://agentmods.dev/skills/christopherkarani/wax/maintenance"><img src="https://agentmods.dev/badge/skills/christopherkarani/wax/maintenance/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/christopherkarani/wax/maintenance"><img src="https://agentmods.dev/badge/skills/christopherkarani/wax/maintenance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 20 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00018 | $0.00258 |
| Opus 5.5 | $0.00007 | $0.00103 |
| Sonnet 5.5 | $0.00004 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
wax-memory-maintenance 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 1mo 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.
What it actually says
Wax Memory Maintenance
Use when the active Hermes memory provider is wax-memory.
Checks
- Confirm the broker is reachable:
hermes wax-memory doctor - Prefer
wax_recallfor assembled context andwax_searchfor raw hits - Store durable facts with
wax_rememberandmemory_typefromnote,user_preference,decision,lesson,handoff,constraint,fact;task_stateis session-local working state and requires an active session - Write session continuity with
wax_handoff; do not dump raw transcripts - Use structured tools (
wax_entity_upsert,wax_fact_assert,wax_facts_query) only for stable knowledge-graph facts
Recovery
- If recall is empty, run
npx waxmcp --embedder minilm --transport http - If vector search is disabled, rebuild both
wax-cliandwax-mcpwith MiniLM/Arctic traits - After
/resetor/resume, rely on the provider session switch — do not invent a new session id
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.
- 1mo ago First seen · 23 lines · 18 tokens per session scan A 213df92671b4
wax-memory-maintenance is a skill published in the GitHub repository christopherkarani/Wax (805 stars, last pushed 25d ago), licensed Apache-2.0. It adds 18 tokens to every session and 258 once invoked, about $0.0001 per session on Opus 5.5. 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
capturing-learnings
Records a real failure-and-fix pair as a project-local lesson, with the specifics that make it recognisable next time. Use when a bug, leakage mistake, or validation error was found and corrected and should not recur. Use when someone asks to save what was learned from an experiment that went wrong.
developing-with-streamlit
Use for ALL Streamlit tasks: creating, editing, debugging, beautifying, styling, theming, optimizing, or deploying Streamlit apps. Also custom components, st.components.v2, HTML/JS/CSS work. Discovers and loads version-matched reference docs from the user's installed Streamlit (>=1.57). Triggers: streamlit, st.…
distill-feedback
A process for turning corrections you give an agent into lasting working rules. It reviews saved conversations and asks for approval before changing those rules.
fast-dash
Build a Fast Dash web app from a Python function. Use when the user wants to turn a function into an interactive app, add a UI to an existing function, build a dashboard / form / wizard, add a chat assistant to an app, or make an app that AI agents can drive over MCP. Fast Dash infers UI components from type hints, so…
evaluate-ml-pipeline
Evaluate one learner with skore.evaluate. The splitter and any non-default score are already on the DataOp from build-ml-pipeline. This skill writes the call without splitter=, persists the report, and records the locator. When the locked split is the training table and test table that shipped with the data, fit on…
model-ml-pipeline
Deterministic entry point for modeling. On a first landing, write the locked baseline design note and ask for approval. Later landings offer only the choices the workspace justifies (an EDA proposal, Backlog, discussion). A missing modeling lock loads frame-ml-problem and stops. After a design is approved, coordinate…