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 dbcls/togomcp --skill research-article-analysisgit clone --depth 1 https://github.com/dbcls/togomcpWrote 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/dbcls/togomcp/research-article-analysis)<a href="https://agentmods.dev/skills/dbcls/togomcp/research-article-analysis"><img src="https://agentmods.dev/badge/skills/dbcls/togomcp/research-article-analysis/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/dbcls/togomcp/research-article-analysis"><img src="https://agentmods.dev/badge/skills/dbcls/togomcp/research-article-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00147 | $0.02692 |
| Opus 5.5 | $0.00059 | $0.01077 |
| Sonnet 5.5 | $0.00029 | $0.00538 |
| Haiku 4.5 | $0.00015 | $0.00269 |
Grade A, and why
research-article-analysis 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Article Analysis (claim validation via TogoMCP SPARQL)
Validate a paper claim-by-claim by running structured SPARQL against five RDF
databases — ChEBI, Rhea, Reactome, UniProt, GO — and citing the query
results (exact formulas, equations, EC numbers, GO definitions), not the paper's
prose or keyword-search text. The driver is the TogoMCP MCP tools
(run_sparql, the search_* tools, get_MIE_file).
The full phased checklist, ID-tracking templates, scoring rubric, and anti-skipping safeguards live in references/research_article_analysis.md — read it for the deliverable structure and discipline. This SKILL.md is the operational path: the SPARQL that actually works (the printed templates have two bugs — see Gotchas), the verified tool calls, and the traps.
Files under references/ are fetched with get_workflow(name="research-article-analysis", path="references/...") (when this skill is installed locally, read them as files instead).
Prerequisites
- The TogoMCP tools (
run_sparql,search_*,get_MIE_file) drive this skill. - Network access from that server to
rdfportal.org.
Warm-up check (confirm connectivity before trusting the templates)
Before relying on the templates, send the ChEBI structure query below through the
run_sparql tool and confirm it returns a row — that proves the endpoint is
reachable and the (chemrof-namespace) pattern still resolves. If it returns
nothing, that database's query type is broken (endpoint down or schema drift) —
re-read its MIE via get_MIE_file and fix the pattern below before relying on it.
Run the analysis (phases, with verified queries)
Follow the phase discipline in the reference doc: 2A select databases (5
rules), 2B get_MIE_file(database=...) for each, 2C keyword-search to
get IDs, 2D run 2+ SPARQL per database using those IDs, 3A reconstruct the
DB-backed logical flow (results → conclusion) + 3B triage the gaps, 4 score
flow soundness, then deliver the four-part FINAL SUMMARY (main claim /
conclusion / DB-backed flow / gap analysis). Drive 2D with the TogoMCP
run_sparql tool. The query types below were all
run live (bile-acid example) and returned data — copy these, not the reference
doc's printed templates.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · +3 lines ecba1d11bed0
- 1mo ago First seen · 174 lines · 147 tokens per session scan A 1b926442dbe5
research-article-analysis is a skill published in the GitHub repository dbcls/togomcp (19 stars, last pushed today), licensed MIT. It adds 147 tokens to every session and 2,692 once invoked, about $0.0006 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.
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