Borrowing it
Nothing to install: this file belongs to IGVF-DACC/igvf-portal-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/IGVF-DACC/igvf-portal-mcp/main/.claude/skills/igvf-facet-filter/SKILL.mdgit clone --depth 1 https://github.com/IGVF-DACC/igvf-portal-mcpWrote 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/igvf-dacc/igvf-portal-mcp/igvf-facet-filter)<a href="https://agentmods.dev/skills/igvf-dacc/igvf-portal-mcp/igvf-facet-filter"><img src="https://agentmods.dev/badge/skills/igvf-dacc/igvf-portal-mcp/igvf-facet-filter/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/igvf-dacc/igvf-portal-mcp/igvf-facet-filter"><img src="https://agentmods.dev/badge/skills/igvf-dacc/igvf-portal-mcp/igvf-facet-filter.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 | $0.00025 | $0.00566 |
| Opus 5 | $0.00013 | $0.00283 |
| Sonnet 5 | $0.00005 | $0.00113 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
igvf-portal-facet-filter 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 12d 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
Use the igvf_portal_facets MCP tool to fetch a summary of the IGVF item type provided in $ARGUMENTS.
Call: igvf_portal_facets(type=["$ARGUMENTS"])
Note on filtering scope
Facets only cover a select subset of fields. Filtering is not limited to facet fields — any field or embedded field available on the item type can be used as a filter. Use igvf_portal_get_endpoint_params to discover the full set of filterable fields for the collection.
Step 1 — Progressive disclosure (facet names only)
The facets response can be very large. Do NOT dump all facet values at once. Instead:
- Show the total item count.
- Show only the list of available facet names/titles (field name + title, no term values yet) — skip facets with 0 or 1 terms. This gives the user a menu to choose from without overwhelming output.
- Note that additional fields beyond the facets are also filterable (via endpoint params).
- Ask the user which facet(s) they want to explore or filter on, or whether they want to see all available filter fields.
Step 2 — Expand on demand
When the user picks one or more facets:
- Show the top term values and counts only for those selected facets.
- Ask if they want to apply a filter value, explore another facet, or fetch results.
If the user asks to see all filterable fields, call igvf_portal_get_endpoint_params for the collection and present the full field list.
Step 3 — Iterative filtering
When the user picks a filter value, call igvf_portal_facets again with the chosen field_filters applied:
- Show the updated total count.
- Show the facet name list again (updated distribution), skipping single-value facets.
- Repeat the loop: user picks facets to expand → picks filter values → re-query.
Step 4 — Fetch results
Once the user is satisfied with filters, fetch the actual records using igvf_portal_search or igvf_portal_get_collection with the accumulated filters.
This is an iterative loop: total + facet menu → user picks facets to expand → show values → user picks filter → re-query → repeat until user wants results.
Keep the output concise. If $ARGUMENTS is empty, ask the user which item type they want to summarize and mention they can call igvf_portal_list_item_types to see options.
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.
- 12d ago First seen · 46 lines · 25 tokens per session scan A 11d90c978889
igvf-portal-facet-filter is a skill published in the GitHub repository IGVF-DACC/igvf-portal-mcp (2 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 566 once invoked, about $0.0001 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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