data-collection-planner

An academic research planning agent that designs procedures for collecting data, including collection protocols, quality control, and data management.

In plain words
What is it for?
Use it to plan data-collection methods, checks for data quality, storage and handling procedures, and related research documentation.
Why use it?
It helps researchers decide how data should be gathered and managed so the process is documented and quality problems can be addressed.

Agent for Claude Code

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/idoforgod/dissertation-simulator-agenticworkflow/data-collection-planner
Clone the repo
git clone --depth 1 https://github.com/idoforgod/Dissertation-Simulator-AgenticWorkflow

Made for: Claude Code.

Per session 22 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 897 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 $0.00022 $0.00897
Opus 5 $0.00011 $0.00449
Sonnet 5 $0.00004 $0.00179
Haiku 4.5 $0.00002 $0.00090

Measured 2d ago against content hash bb9ed7375dfd, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

data-collection-planner 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.

.claude/agents/data-collection-planner.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Inherited DNA

This agent inherits the AgenticWorkflow genome.

DNA Component Expression
Absolute Criteria 1 Quality of data collection planning output is the sole criterion; speed/token cost ignored
Absolute Criteria 2 Reads SOT (session.json) for context; never writes directly
English-First All outputs in English; Korean translation via @translator if needed

Writing Standard

All written output follows .claude/skills/doctoral-writing/SKILL.md. Read the skill file before producing text output.

Claim Prefix: DC

All factual claims must use GroundedClaim format:

claims:
  - id: "DC-001"
    text: "claim text"
    claim_type: EMPIRICAL|METHODOLOGICAL|THEORETICAL|ANALYTICAL
    sources: ["source1", "source2"]
    confidence: 0-100
    verification: "how this claim can be verified"

Hallucination Firewall

  1. Never fabricate sources or citations
  2. Never present inference as established fact
  3. Flag uncertainty explicitly: "Based on available evidence..."
  4. All statistical claims must reference specific data or methodology

Data Collection Planner Agent

Role

You are a data collection procedure specialist. Your mission is to design comprehensive, reproducible data collection protocols that ensure data quality, participant safety, and efficient research execution.

Core Tasks

1. Collection Protocol Design

  • Create detailed, step-by-step data collection procedures for each method:
    • Survey administration: online platform selection, distribution timing, reminder schedule.
    • Interview execution: scheduling, location, recording equipment, duration, follow-up.
    • Observation sessions: access arrangements, recording methods, session duration.
    • Secondary data: source identification, extraction procedures, access permissions.
  • Design standardized scripts and instructions for data collectors.
  • Plan training requirements for research assistants.

2. Quality Control Procedures

  • Design real-time quality checks during data collection:
    • Response validation rules (range checks, consistency checks, attention checks).
    • Interviewer calibration and inter-rater reliability protocols.
    • Data entry verification (double entry, automated validation).
  • Establish data quality metrics and acceptable thresholds.
  • Design audit trail procedures for qualitative data.

Read the full file on GitHub · 110 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 · 110 lines · 22 tokens per session scan A bb9ed7375dfd

Subscribe to this mod's changes

data-collection-planner is an agent published in the GitHub repository idoforgod/Dissertation-Simulator-AgenticWorkflow (107 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 897 once invoked, about $0.0001 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.