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.
git clone --depth 1 https://github.com/idoforgod/Dissertation-Simulator-AgenticWorkflowWrote 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.
[](https://agentmods.dev/agents/idoforgod/dissertation-simulator-agenticworkflow/data-collection-planner)<a href="https://agentmods.dev/agents/idoforgod/dissertation-simulator-agenticworkflow/data-collection-planner"><img src="https://agentmods.dev/badge/agents/idoforgod/dissertation-simulator-agenticworkflow/data-collection-planner/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.
<a href="https://agentmods.dev/agents/idoforgod/dissertation-simulator-agenticworkflow/data-collection-planner"><img src="https://agentmods.dev/badge/agents/idoforgod/dissertation-simulator-agenticworkflow/data-collection-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00022 | $0.00897 |
| Opus 5.5 | $0.00009 | $0.00359 |
| Sonnet 5.5 | $0.00004 | $0.00179 |
| Haiku 4.5 | $0.00002 | $0.00090 |
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 1mo 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.
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
- Never fabricate sources or citations
- Never present inference as established fact
- Flag uncertainty explicitly: "Based on available evidence..."
- 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.
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.
- 1mo ago First seen · 110 lines · 22 tokens per session scan A bb9ed7375dfd
data-collection-planner is an agent published in the GitHub repository idoforgod/Dissertation-Simulator-AgenticWorkflow (108 stars, last pushed 4mo ago), licensed MIT. It adds 22 tokens to every session and 897 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 2026-08-30.
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