entity-workup

entity-workup is a skill for Claude Code, Codex from ajbarea/ariadne. It costs 57 tokens per session (1,162 once invoked), scanned A, original, MIT.

An investigation workflow for researching a person, organization, or other entity in connected graph and database records. It gathers relationships and attributes, then produces a cited analytical note.

In plain words
What is it for?
Use it to trace organizational relationships, reporting lines, locations, communications, roles, employers, clearance, or last-seen records when the relevant read-only data sources are available.
Why use it?
It helps keep an investigation evidence-based by separating relationship data from entity details and avoiding claims that were not retrieved.

Skill for Claude CodeCodex

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/ajbarea/ariadne/entity-workup
Any agent
npx skills add ajbarea/ariadne --skill entity-workup
Clone the repo
git clone --depth 1 https://github.com/ajbarea/ariadne

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for entity-workup

README.md
[![agentmods](https://agentmods.dev/badge/skills/ajbarea/ariadne/entity-workup.svg)](https://agentmods.dev/skills/ajbarea/ariadne/entity-workup)
Your own site
<a href="https://agentmods.dev/skills/ajbarea/ariadne/entity-workup"><img src="https://agentmods.dev/badge/skills/ajbarea/ariadne/entity-workup.svg" alt="Measured on agentmods" height="20"></a>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,162 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 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 $0.00057 $0.01162
Opus 5 $0.00028 $0.00581
Sonnet 5 $0.00011 $0.00232
Haiku 4.5 $0.00006 $0.00116

Measured 4d ago against content hash 25ef3abb8666, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

entity-workup 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 4d 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.

.claude/skills/entity-workup/SKILL.md · 77 lines

How it starts

The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Entity workup

You are an intelligence analyst's harness. Given a target entity or organizational node, produce a concise, fully cited analytic note. Use the read-only graph tools (mcp__neo4j__get_neo4j_schema, mcp__neo4j__read_neo4j_cypher) and, when they are available, the read-only relational tools (mcp__postgres__list_schemas, mcp__postgres__get_object_details, mcp__postgres__execute_sql). Never assert a fact you did not retrieve.

Loop: gather → act → verify → synthesize

  1. Gather. Learn each store's shape: get_neo4j_schema for the graph, and — if the relational tools are available — list_schemas / get_object_details for the tables. Locate the target in each store (match by name/id/alias). If it is absent everywhere, say so and stop.
  2. Act — route by question. Use the graph for relationships, hierarchy, the REPORTS_TO chain, co-location, and communication. Use the relational store for per-entity attributes and records (role, clearance, employer, last-seen). For free-text / email-body evidence: when the mcp__ariadne__hybrid_search tool is available, prefer it — call it with a natural-language query; it RRF-fuses full-text + semantic search and returns ranked passages tagged with their ids; cite facts you use as [cite:gN]. Fall back to execute_sql with content_tsv @@ websearch_to_tsquery('english', '<terms>') ordered by ts_rank(...) only when mcp__ariadne__hybrid_search is not registered. Resolve the same entity across stores by its shared key (name / alias). Prefer several focused, read-only queries over one giant one.
  3. Verify & reconcile. Re-query any decisive link before relying on it. When the graph and the relational store agree on a connection, the corroboration across modalities makes it stronger — say so. When they conflict, flag the disagreement explicitly and weigh sources by reliability rather than silently picking one. Hunt for non-obvious, cross-source connections — a tie visible only by combining stores (or a multi-hop graph path of length ≥ 3) — these are the highest-value findings the analyst would miss by manual pivoting.
  4. Synthesize. Write the note from note-template.md. For the most decisive finding, run a brief analysis of competing hypotheses: state 1–2 alternative explanations and weigh the cited evidence for and against each before committing to the one you favor. Keep every judgment proportionate to its evidence — flag a possible modeling artifact (an edge whose direction may be undirected, a unit-level fact used for a person) as a caveat, do not build a decisive claim on it. Draw out the implications ("so what") of the key findings, and state your analytic confidence in the decisive finding and its basis (ICD-203: confidence-in-basis is distinct from the likelihood word in a judgment).

Read the full file on GitHub · 77 lines

Files

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.

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. 4d ago First seen · 77 lines · 57 tokens per session scan A 25ef3abb8666

Subscribe to this mod's changes

entity-workup is a skill published in the GitHub repository ajbarea/ariadne (2 stars, last pushed 10d ago), licensed MIT. It adds 57 tokens to every session and 1,162 once invoked, about $0.0003 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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