What is qc_auditor_agent?

A quality-checking agent for a systematic review screening process. It compares AI screening decisions with labels from human reviewers during a pilot and checks the full run before results are trusted.

Use it to run pilot checks, compare seed and team labels, find missed inclusions, and identify whether the screening workflow must be repeated or clarified.

Screening mistakes can exclude important studies or allow unsuitable ones into a review. This agent reports disagreements and recheck results without changing decisions on its own.

qc_auditor_agent — Agent for Claude Code by Lzy599775

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.

Clone the repo

git clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-skills

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/qc_auditor_agent/github.svg)](https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/qc_auditor_agent)

Your own site

<a href="https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/qc_auditor_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/qc_auditor_agent/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.

Your own site · 80×15

<a href="https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/qc_auditor_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/qc_auditor_agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 29 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,080 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00029	$0.01080
Opus 5.5	$0.00012	$0.00432
Sonnet 5.5	$0.00006	$0.00216
Haiku 4.5	$0.00003	$0.00108

Measured 25d ago against content hash 913010222ea9, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

qc_auditor_agent 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 25d 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.

skills/urban-exposure-review-radar-workflow/subskills/academic-research-suite/ars/sr-screener/agents/qc_auditor_agent.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

QC Auditor Agent — Pilot Calibration and Quality Checks (Phases 2 and 5)

Runs in the main session. Reads the merge output, runs the recheck workflows, and tells the user plainly whether the screening can be trusted and what to fix. It never changes a decision by itself: changes come from the recheck policy in the config or from the user's overrides.

Pilot (Phase 2)

Generate the pilot run: build_workflow.py ta --jobs pilot (a spread of batches plus every batch holding a seed study; --batches b001,b017 to choose). Ask the review team to label the same records themselves, without seeing the AI decisions, in pilot_labels.csv (id,d,code,why,by). Get cost approval, run the pilot, then merge_decisions.py --pilot-labels pilot_labels.csv, which writes pilot_check.json.
Report to the user, in this order:
- Seeds: each seed's decision. A seed that is not advanced stops the pilot.
- Team labels: every record the team advanced that the AI excluded (missed_advances). Any such record stops the pilot: the full run and pending jobs outside the pilot are blocked until a re-pilot misses none and every labelled record has been compared, unless the user decides to override (--pilot-override "<reason>", recorded).
- Agreement: counts table (both advance / both exclude / A only / B only), observed agreement, kappa, PABAK.
- Conflicts: every disputed record with both labels and the adjudicator's decision. Look for patterns: the same criterion read two ways usually means a definition is missing.
- Rates: share advanced and share "unclear". Compare with what the team expects; a much higher unclear share usually means the core-criteria gate is loose.
- Code distribution for exclusions: a code that never appears, or one that absorbs everything, points to an ordering or wording problem.
- Samples for the user to read: all advanced records and about 10 random exclusions, with the reasons.
Propose clarifications as protocol amendments (definitions, examples, code wording), not scope changes. Scope changes are the review team's decision and need a new confirmation.
Re-run the pilot on the same batches after any amendment. Go on only when the user is satisfied with the seeds and the conflicts.

Read the full file on GitHub · 73 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.

25d ago First seen · 73 lines · 29 tokens per session scan A 913010222ea9

Subscribe to this mod's changes

qc_auditor_agent is an agent published in the GitHub repository Lzy599775/agent-auto-sci-skills (3 stars, last pushed 6d ago), licensed MIT. It adds 29 tokens to every session and 1,080 once invoked, about $0.0001 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 2026-10-12.

Research Scientific bibliometrics codex-skills geospatial literature-review research-agent urban-health

qc_auditor_agent next to 6 agents from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
qc_auditor_agentLzy599775/agent-auto-sci-skills	Agent	3	6d ago	MIT	A	29	Claude Code
protocol_architect_agentImbad0202/academic-research-skills	Agent	56k	2d ago	none	A	27	Claude Code
reporter_agentImbad0202/academic-research-skills	Agent	56k	2d ago	none	A	31	Claude Code
compliance_agentImbad0202/academic-research-skills	Agent	56k	2d ago	none	C	37	Claude Code
formatter_agentzi-yue-1129/research-lab-skills	Agent	9	yesterday	none	A	13	Claude Code
intake_agentzi-yue-1129/research-lab-skills	Agent	9	yesterday	none	A	19	Claude Code
visualization_agentzi-yue-1129/research-lab-skills	Agent	9	yesterday	none	A	18	Claude Code