agentmods.dev

database-lookup

A method for retrieving facts from documented public database APIs—interfaces that let software query named databases—with explicit filters, pagination, and source records.

In plain words
What is it for?
Use it to query public databases, retrieve complete filtered datasets, verify result counts, resolve identifiers, and record enough source information for someone else to repeat the lookup.
Why use it?
It helps produce repeatable, auditable lookups instead of relying on broad searches or guesses. This matters when small filtering differences can change scientific, regulatory, or financial results.

Skill for Claude CodeCodex

40k +1.4k yesterday B scan 50 tokens original MIT
View the source file K-Dense-AI/scientific-agent-skills
Install

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.

agentmods
npx agentmods add skills/k-dense-ai/scientific-agent-skills/database-lookup
Any agent
npx skills add K-Dense-AI/scientific-agent-skills --skill database-lookup
Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills

Made for: Claude Code, Codex.

Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,361 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 2 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00050 $0.06361
Opus 5 $0.00025 $0.03180
Sonnet 5 $0.00010 $0.01272
Haiku 4.5 $0.00005 $0.00636

Measured yesterday against content hash 3dfa2a009763, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

database-lookup scanned grade B with 2 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 yesterday.

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.

Sends data to an external URLmediumData exfiltration

A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.

| Open Targets | GraphQL endpoint | `curl -X POST -H "Content-Type: application/json" -d '{"query":"..."}' https://api.platform.opentargets.org/api/v4/graphql` |

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

These databases require HTTP POST and **will not work with WebFetch** (GET-only). Use `curl` via your platform's shell tool instead:
skills/database-lookup/SKILL.md · 387 lines

How it starts

The opening of the file, as written. The whole thing — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Database Lookup

This skill catalogs 78 public databases with documented API access patterns. Your job is to turn the user's intent into a reproducible retrieval: select the authoritative database(s), make bounded and rate-limited API calls, verify counts when completeness matters, and return results with enough provenance that another agent or human can repeat the lookup.

For complex biomedical retrievals, assume small filtering differences can change downstream conclusions. Prefer deterministic APIs, explicit identifiers, exhaustive pagination, and auditable logs over broad searching or plausible summaries.

Core Workflow

  1. Define the retrieval contract — Identify the target entity, accepted identifiers, organism/taxon/build/date constraints, filters, expected output fields, and whether the user needs an exhaustive dataset or a targeted lookup. If a required scientific constraint is missing and affects correctness, ask a clarifying question rather than guessing.

  2. Select authoritative database(s) — Use the database selection guide below. Prefer the primary database for the user's intent, then add cross-check databases only for identifier resolution, validation, or known coverage gaps. Do not fan out across many APIs just because they are available.

  3. Read the reference file and retrieval contract — Each database has a reference file in references/ with endpoint details, query formats, and example calls. Read the relevant file(s) and references/retrieval-contract.md before making API calls.

  4. Plan filter semantics before calling — Separate filters the API enforces server-side from filters that must be checked locally. Note identifier conversions, fields with ambiguous meanings, pagination strategy, rate limits, and any data-source conventions such as RefSeq vs GenBank or genome build.

  5. Make bounded API calls — See the Making API Calls section below. For exhaustive retrievals, count first when the API supports it, estimate cost, paginate or batch until retrieved counts reconcile, and fail visibly if the final dataset is incomplete. Ask for confirmation before a retrieval would exceed 10,000 records, 100 API calls, or the selected API's documented bulk-use guidance.

Read the full file on GitHub · 387 lines

Files

What ships with it

60 files 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.

Changes

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.

  1. yesterday First seen · 387 lines · 50 tokens per session scan B 3dfa2a009763

Subscribe to this mod's changes

database-lookup is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (40,390 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 6,361 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

DatabasesResearchScientificagent-skillsai-scientistbioinformaticschemoinformaticsclaudeclaude-skills
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