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 axiomantic/spellbook --skill tooling-discoverygit clone --depth 1 https://github.com/axiomantic/spellbookWrote 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/axiomantic/spellbook/tooling-discovery)<a href="https://agentmods.dev/skills/axiomantic/spellbook/tooling-discovery"><img src="https://agentmods.dev/badge/skills/axiomantic/spellbook/tooling-discovery/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/axiomantic/spellbook/tooling-discovery"><img src="https://agentmods.dev/badge/skills/axiomantic/spellbook/tooling-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Agent Snooping · line 16 Skill accesses MCP server configuration files (mcp.json). MCP configs contain server URLs, authentication tokens, and tool definitions — reading them allows the skill to discover and potentially abuse other tool integrations.Fix: Remove all code or instructions that read MCP configuration files (mcp.json). MCP server details should be managed by the agent runtime, not read by individual skills.
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 | $0.00072 | $0.00682 |
| Opus 5.5 | $0.00029 | $0.00273 |
| Sonnet 5.5 | $0.00014 | $0.00136 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
tooling-discovery 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.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tooling Discovery
Invariant Principles
- Registry is source of truth. Only recommend tools that exist in the curated YAML registry. Never fabricate tool entries.
- Trust tiers are non-negotiable. Always present trust classification. Tier 4+ tools require explicit risk warnings before recommendation.
- Detection before recommendation. Always run the tooling_discover MCP tool to check availability. Do not skip detection and guess.
Workflow
Step 1: Determine Domain Keywords
Extract domain keywords from one of:
- User's explicit request (e.g., "what tools exist for Jira integration?")
- Current project context (language, framework, key dependencies)
- Active feature being developed (technologies mentioned)
Combine into a comma-separated keyword string.
Step 2: Call tooling_discover
tooling_discover(
domain_keywords="<comma-separated keywords>",
project_path="<project root or empty for auto-detect>"
)
Step 3: Present Results
Group results by availability and trust:
Available Tools (detected on this system):
- List tools where
available: true, grouped by detection method - For each: name, type, trust tier label, description, how detected
Registry Tools (known but not detected):
- List tools where
available: false - For each: name, type, trust tier label, description, source URL
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 · 82 lines · 72 tokens per session scan A 78813e667089
tooling-discovery is a skill published in the GitHub repository axiomantic/spellbook (10 stars, last pushed 2d ago), licensed MIT. It adds 72 tokens to every session and 682 once invoked, about $0.0003 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 2026-09-03.
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