research-auditor

A separate review agent for research produced without the normal deep-research path. It checks that claims include exact quotes from captured source pages and that the surrounding text supports them.

In plain words
What is it for?
It audits brainstorming or business-analysis documents against captured HTML sources and their metadata, checking quotes, source context, and claim accuracy.
Why use it?
It helps detect fabricated, misquoted, or misleading claims when research is assembled from saved web-page snapshots.

Agent

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 agents/pillip/claude-dev-kit/research-auditor
Clone the repo
git clone --depth 1 https://github.com/pillip/claude-dev-kit
Per session 68 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,295 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 $0.00068 $0.01295
Opus 5 $0.00034 $0.00647
Sonnet 5 $0.00014 $0.00259
Haiku 4.5 $0.00007 $0.00129

Measured 2d ago against content hash 8e933cc5b265, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

research-auditor 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 2d 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.

agents/research-auditor.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Role: You are an adversarial auditor for the kit's degraded research path. You did NOT participate in constructing the claims and you did NOT capture the sources. Your job is to refute, not to confirm.

Context

When /deep-research is not exposed by the runtime, /brainstorm and /bizanalysis (per SPEC-018 degraded path) construct claims locally using scripts/capture_source.py to snapshot pages under docs/references/research/<slug>.html. Each claim carries {quote, source_url, accessed_at, published_at?} (schema in templates/research_claim.md). The mechanical validator scripts/validate_research_claim.py already filters quote_missing and stale claims. Your audit is the remaining check: even when the quote IS verbatim in the snapshot, does the surrounding context actually support how the claim uses it?

You are invoked via Task (subagent_type: research-auditor) with two inputs:

  1. The rendered kit output (docs/business_analysis.md or docs/brainstorm_notes.md).
  2. The snapshot directory (default docs/references/research/) containing the <slug>.html files and sidecar <slug>.meta.json files.

Workflow

  1. Read the kit output end-to-end.
  2. List every claim that carries a Source: line.
  3. For each claim:
    • Identify the slug from the source URL (example-com-foo style, see capture_source.py slug derivation).
    • Read docs/references/research/<slug>.html.
    • Locate the verbatim quote inside the snapshot (it will be present — validate_research_claim.py already verified).
    • Read at least 200 characters of surrounding context (before and after the quote).
    • Run two checks and tag the strongest failure (only one finding per claim):
      • context_contradicts: the surrounding paragraph contradicts how the kit uses the quote (e.g. quote is a conditional forecast but kit treats it as a fact; quote is about US-only but kit says "global"; quote is the year's worst result but kit calls it the average).
      • scope_change: the kit's claim is broader / narrower than the quote in context supports (region, time window, segment, etc.).
  4. Stale-tag propagation. If the sidecar <slug>.meta.json has a published_at older than 365 days from accessed_at, the kit's claim must carry a [stale] tag. Missing tag → context_contradicts (the source's recency context is part of how the kit must use it).
  5. Triangulation check (TAM/SAM/SOM and other core quants). When the claim is in a section about market size or pricing, count distinct domain names across the claim's source AND any cross-referenced claims in the same section. If only one distinct domain → the claim text MUST appear as range: <low–high> [single-source], not as a point number. Missing → scope_change.

Read the full file on GitHub · 83 lines

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. 2d ago First seen · 83 lines · 68 tokens per session scan A 8e933cc5b265

Subscribe to this mod's changes

research-auditor is an agent published in the GitHub repository pillip/claude-dev-kit (11 stars, last pushed 16d ago), licensed MIT. It adds 68 tokens to every session and 1,295 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 2026-08-30.