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 gnurio/nurijanian-skills --skill tech-sensemakinggit clone --depth 1 https://github.com/gnurio/nurijanian-skillsWrote 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/gnurio/nurijanian-skills/tech-sensemaking)<a href="https://agentmods.dev/skills/gnurio/nurijanian-skills/tech-sensemaking"><img src="https://agentmods.dev/badge/skills/gnurio/nurijanian-skills/tech-sensemaking/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/gnurio/nurijanian-skills/tech-sensemaking"><img src="https://agentmods.dev/badge/skills/gnurio/nurijanian-skills/tech-sensemaking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00146 | $0.01695 |
| Opus 5 | $0.00073 | $0.00847 |
| Sonnet 5 | $0.00029 | $0.00339 |
| Haiku 4.5 | $0.00015 | $0.00169 |
Grade A, and why
tech-sensemaking 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 12d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tech Sensemaking
Analyze a technology announcement through 4 strategic questions using Verbalized Sampling to produce diverse, non-obvious insights grounded in the user's specific context — whether that's a business, a product, a feature branch, a codebase, a personal project, or anything else.
Required Input
- Announcement: pasted text, URL, or description of the technology change.
- Context subject: what to analyze the announcement against (asked in Phase 2 if not obvious).
Workflow
Phase 1 — Intake
- Accept the announcement. If a URL, scrape it (Firecrawl, WebFetch, or browser tools).
- Write a neutral announcement summary (200-400 words):
- What was announced (capabilities, features, changes)
- What constraints or limitations were mentioned
- Availability and timeline
- What was NOT said (notable omissions)
- Present the summary to the user. Ask: "Does this capture the announcement accurately, or should I adjust anything before analysis?"
Phase 2 — Context Loading
Determine the context type and load accordingly. If the user hasn't specified what to analyze the announcement against, ask:
"What should I analyze this announcement against? For example:
- A business (prodmgmt.world, your startup, etc.)
- A product or feature (your SaaS, an open-source project, etc.)
- A codebase or feature branch (a repo you're building)
- A personal goal or project
- Something else?"
Context sources by type
| Context type | Where to look | What to extract |
|---|---|---|
| Business | Vault notes (qmd search), Context/ files if they exist, user description |
What it does, revenue model, competitive position, goals, constraints, team size |
| Product | README, product docs, vault notes, user description | What it does, target users, current capabilities, roadmap, tech stack |
| Codebase / feature branch | Source code, README, CLAUDE.md, recent commits/PRs | Architecture, dependencies, current problems, what's being built |
| Personal project | Vault notes, user description | Goals, constraints, timeline, what's been tried |
| Role / career | Vault notes, user description | Current role, skills, goals, industry, constraints |
| Generic / exploratory | User description, web research | Domain, key players, known constraints, relevant trends |
What ships with it
1 file 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.
- 12d ago First seen · 160 lines · 146 tokens per session scan A 210c93265c90
tech-sensemaking is a skill published in the GitHub repository gnurio/nurijanian-skills (105 stars, last pushed 29d ago), licensed MIT. It adds 146 tokens to every session and 1,695 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 2026-08-30.
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