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 analysis-framinggit 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/analysis-framing)<a href="https://agentmods.dev/skills/etoyama/insight-blueprint/analysis-framing"><img src="https://agentmods.dev/badge/skills/etoyama/insight-blueprint/analysis-framing/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/analysis-framing"><img src="https://agentmods.dev/badge/skills/etoyama/insight-blueprint/analysis-framing.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 | $0.00059 | $0.02018 |
| Opus 5 | $0.00030 | $0.01009 |
| Sonnet 5 | $0.00012 | $0.00404 |
| Haiku 4.5 | $0.00006 | $0.00202 |
Grade A, and why
analysis-framing 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 8d 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analysis-framing --- Analysis Framing Explorer
Explores the .insight/ directory to discover available data, existing analyses,
and domain knowledge, helping the user frame a well-grounded hypothesis direction.
Does NOT create hypotheses --- that is /analysis-design's responsibility.
When to Use
- Vague analysis theme, need to explore available data
- Want to discover what data and existing analyses are available
- Starting a new analysis direction, need grounding in existing context
When NOT to Use
- Hypothesis is already clear --- use
/analysis-design - Recording reasoning during analysis --- use
/analysis-journal - Structured reflection on completed analysis --- use
/analysis-reflection
Workflow
Step 1: Receive Theme
Accept the analysis theme from $ARGUMENTS or ask the user for one.
- If
$ARGUMENTSis provided, use it as the theme - If the theme is vague or overly broad (e.g., "データ分析", "社会問題"):
- Present 2-3 candidate directions based on available data in
.insight/catalog/ - Ask the user to select or refine a direction before proceeding
- Do NOT depend on development-partner for theme narrowing --- handle it independently
- Present 2-3 candidate directions based on available data in
- If forwarded from development-partner: accept the framing context from the conversation and build upon it
- If an RQ Brief is present in the conversation (forwarded from
/rq-problematization): use itsテーマas the theme and its検証の方向性as the seed direction. The research questions are theory-driven and not yet grounded — your job is to ground them in available data. Carry the central assumption forward into the Direction Dialogue (Step 4). - If
.insight/directory does not exist: inform the user that the project is not initialized and guide them to runinsight-blueprint init. Stop the workflow here.
Step 2: Domain Exploration (Agentic Search)
Explore .insight/ directories to gather theme-relevant information.
Do NOT use MCP tools. Use only Glob, Read, and Grep via Agent tool.
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.
- 8d ago First seen · 189 lines · 59 tokens per session scan A f736b5d025da
analysis-framing is a skill published in the GitHub repository etoyama/insight-blueprint (0 stars, last pushed 16d ago), licensed MIT. It adds 59 tokens to every session and 2,018 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 2026-08-31.
Other skills, from other repositories
safe-email-operations
Use email through the bundled mcp-email-server MCP server, diagnose bounded non-secret state, and hand account or credential setup to a user-operated CLI or authenticated local UI.
rival-search-mcp
Deterministic deep research via RivalSearchMCP. 9 tools: 5-engine web search (DuckDuckGo/Bing/Yahoo/Mojeek/Wikipedia), 9-platform social search (Reddit/HN/StackOverflow/Dev.to/Medium/ProductHunt/Bluesky/Lobste.rs/Lemmy), 5-source news (Google/Bing/Guardian/GDELT/DDG), 5 academic DBs…
mcp-musescore
Use the ghchen99/mcp-musescore MCP server to inspect, navigate, compose, and edit an open MuseScore score through its QML WebSocket plugin.
naver-search-mcp
Use for Korean web search, Naver News, Blog, Cafe, Image, Knowledge iN, Encyclopedia, Local search, and Naver DataLab search-trend and shopping-insight analysis through the published npm MCP server.
ocr-expert
You are connected to OCR-MCP, a FastMCP 3.4+ server with 14 OCR backends. This skill teaches you how to use it effectively.
skills
Generate a complete SVG file from a natural language description using SEP-1577 multi-step sampling.