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.
npx skills add etoyama/insight-blueprint --skill rq-problematizationgit clone --depth 1 https://github.com/etoyama/insight-blueprintWrote 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/skills/etoyama/insight-blueprint/rq-problematization)<a href="https://agentmods.dev/skills/etoyama/insight-blueprint/rq-problematization"><img src="https://agentmods.dev/badge/skills/etoyama/insight-blueprint/rq-problematization/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/skills/etoyama/insight-blueprint/rq-problematization"><img src="https://agentmods.dev/badge/skills/etoyama/insight-blueprint/rq-problematization.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 | $0.00148 | $0.02821 |
| Opus 5 | $0.00074 | $0.01411 |
| Sonnet 5 | $0.00030 | $0.00564 |
| Haiku 4.5 | $0.00015 | $0.00282 |
Grade A, and why
rq-problematization 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 11d 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/rq-problematization — Research Question Problematization
Generates impactful (interesting and influential) research questions from a research theme, grounded in Alvesson & Sandberg (2011) "Generating Research Questions Through Problematization" (Academy of Management Review, 36(2), 247-271) and the sister paper Sandberg & Alvesson (2011) "Ways of constructing research questions: gap-spotting or problematization?" (Organization, 18(1), 23-44).
Read references/alvesson-framework.md first to load the exact definitions of the
framework before running the workflow.
When to Use
- Have a research theme and want to generate impactful, novel research questions
- Want to question the taken-for-granted assumptions underlying prior research
- Need an angle that goes beyond incremental gap-filling
When NOT to Use
- Want to explore available data and frame a verifiable direction →
/analysis-framing - Hypothesis is already clear and only needs structuring into a design →
/analysis-design - Recording reasoning during an ongoing analysis →
/analysis-journal
Core Principle: problematization, not gap-spotting
This is the heart of the skill and the easiest point to fail. Most RQ generation collapses into gap-spotting — looking for "what is missing in the literature." That is safe but bland and rarely produces influential theory. Alvesson argues that theory becomes "interesting" only when we identify the assumptions that existing research takes for granted, then question and overturn them (cf. Davis 1971, "That's Interesting!").
Therefore, at every stage of the workflow, ask:
- "Is this merely pointing at an under-researched area?" (= gap-spotting)
- "Is this shaking an assumption the literature takes for granted?" (= problematization) If it is not the latter, redo it.
Workflow Overview
A flow mapped onto Alvesson's six-step logic. Copy the following checklist into your response at the start and track progress (a pattern recommended for complex workflows):
What ships with it
4 files 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.
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.
- 11d ago First seen · 255 lines · 148 tokens per session scan A 1b608fbab327
rq-problematization is a skill published in the GitHub repository etoyama/insight-blueprint (0 stars, last pushed 19d ago), licensed MIT. It adds 148 tokens to every session and 2,821 once invoked, about $0.0007 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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