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 danielrosehill/Claude-Taxonomy-Creation-Plugin --skill generate-from-specgit clone --depth 1 https://github.com/danielrosehill/Claude-Taxonomy-Creation-PluginWrote 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/danielrosehill/claude-taxonomy-creation-plugin/generate-from-spec)<a href="https://agentmods.dev/skills/danielrosehill/claude-taxonomy-creation-plugin/generate-from-spec"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-taxonomy-creation-plugin/generate-from-spec/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/danielrosehill/claude-taxonomy-creation-plugin/generate-from-spec"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-taxonomy-creation-plugin/generate-from-spec.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 | $0.00030 | $0.00644 |
| Opus 5 | $0.00015 | $0.00322 |
| Sonnet 5 | $0.00006 | $0.00129 |
| Haiku 4.5 | $0.00003 | $0.00064 |
Grade A, and why
generate-from-spec 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 9d 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Taxonomy from Spec
Build a custom taxonomy from a user's natural-language or structured specification. Elicit the domain, scope, schema, and generate a structured, reviewed taxonomy ready for load or export.
When to use
- "Create a taxonomy of LLM evaluation types"
- "I need product categories for [domain]"
- "Generate a content classification scheme for…"
- "Build me a list of [entity types] with [fields]"
Inputs to gather
- Domain/purpose: What is this taxonomy for? (e.g., "types of software testing", "product categories for an e-commerce site")
- Expected size: How many entries? (rough order of magnitude)
- Fields per entry: What columns? (e.g., code, label, description, parent_code, metadata JSON)
- Hierarchy? Flat or parent/child tree? (defer to
hierarchical-taxonomiesif complex) - Required exhaustiveness: Complete/canonical list vs. representative sample?
Procedure
- Elicit and confirm the spec: Ask clarifying questions about domain, scope, fields. Write a brief spec block to show the user you understand the requirement.
- Generate the taxonomy:
- For small sets (≤50 entries): Write directly as a structured list (dict of code → label, description, optional parent_code).
- For larger or specialized sets (>50 entries, domain-specific): Suggest spawning a research subagent or conducting web research to ensure comprehensive coverage; do not guess.
- Assign stable codes: Use snake_case slugs derived from the label (e.g., "Instruction Following" →
instruction_following), or suggest numeric IDs if the user prefers. Ensure codes are URL-safe and unique. - Structure the output:
- Write to
data/<name>/<name>.csvwith columns:code(PK),label,description,source(how the entry was generated/sourced),generated_at(ISO 8601 timestamp). - Also write
data/<name>/<name>.json(array of objects, same fields). - Include deterministic ordering: alphabetical by code, or by user-specified priority.
- Write to
- Provide review guidance: Print the generated list for the user to review before load. Highlight any entries that seem uncertain or out-of-scope. Never auto-load without explicit approval.
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.
- 9d ago First seen · 48 lines · 30 tokens per session scan A b8866e33c041
generate-from-spec is a skill published in the GitHub repository danielrosehill/Claude-Taxonomy-Creation-Plugin (3 stars, last pushed 4mo ago), licensed MIT. It adds 30 tokens to every session and 644 once invoked, about $0.0002 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 2026-08-31.
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