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 nitishagar/lumen --skill seo-checkgit clone --depth 1 https://github.com/nitishagar/lumenWrote 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/nitishagar/lumen/seo-check)<a href="https://agentmods.dev/skills/nitishagar/lumen/seo-check"><img src="https://agentmods.dev/badge/skills/nitishagar/lumen/seo-check/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/nitishagar/lumen/seo-check"><img src="https://agentmods.dev/badge/skills/nitishagar/lumen/seo-check.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 | $0.00044 | $0.00271 |
| Opus 5.5 | $0.00018 | $0.00108 |
| Sonnet 5.5 | $0.00009 | $0.00054 |
| Haiku 4.5 | $0.00004 | $0.00027 |
Grade A, and why
seo-check 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 16d 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
seo-check — lumen audit + fix loop
- Take the target URL (ask if unclear) and call the
lumen_audit_siteMCP tool withresponse_format: "concise". - Work the returned
topRulesgroups in order — they are ranked by severity, then by how many pages they affect. Each group carries afixHintandsampleUrls; apply fixes in the user's source files. - Re-run the audit after fixing and report the score delta. Cite the provenance fields lumen returns (provider, kind, retrievedAt) for any number you quote — never invent a metric lumen did not return.
- If CWV/authority legs report
not-configured, tell the user to runlumen doctorfor guided key setup. Being unconfigured is not an error.
Honesty rules: lumen reports what it measured on the pages it fetched. Skipped pages are unknown, not passing — do not present an incomplete audit as a clean bill of health.
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.
- 16d ago First seen · 22 lines · 44 tokens per session scan A 490c4b3c2524
seo-check is a skill published in the GitHub repository nitishagar/lumen (0 stars, last pushed 6d ago), licensed Apache-2.0. It adds 44 tokens to every session and 271 once invoked, about $0.0002 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 2026-09-30.
Other skills, from other repositories
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…
clinicaltrials-database
Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel…
openalex-database
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly…
glycoengineering
Analyze and engineer protein glycosylation. Scan sequences for N-glycosylation sequons (N-X-S/T), predict O-glycosylation hotspots, and access curated glycoengineering tools (NetOGlyc, GlycoShield, GlycoWorkbench). For glycoprotein engineering, therapeutic antibody optimization, and vaccine design.
pysam
Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.