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envisioningenvisioning/skills/skills/technology-scan/SKILL.md

technology-scan

Skill2 starsChanged 51 days agoMITView on GitHub

Map publicly documented technologies inside a defined scope — hardware, software, applications, ethics & security — with readiness level, impact and investment scores, each grounded in open sources. Use when the user asks "what technologies exist for", "technology landscape", "tech scan", "TRL of", "who is building", or wants a radar of capabilities in a field. Stage: sense.

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If the user hasn't stated it, ask for it in one line and wait.

    line 19

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Technology Scan

A scan, run against the Technology Scan playbook. The steps live in ../../core/scan.md; the categories, metrics, sourcing rules and boundary live in ../../core/playbooks/technology-scan.md. Read both before starting, plus ../../core/envisioning.md and ../../core/grounding.md.

Audience: Innovation, R&D, technology strategy.

Boundary

This scan needs a scan scope — a named industry, sector, technical field, region, or combination (e.g. "grid-scale storage, EU", "computer vision in agriculture"). Capabilities, not STEEP trends.

If the user hasn't stated it, ask for it in one line and wait. If a briefing exists (scope writes one), use its boundary verbatim.

Run

  1. Ground on the boundary — reuse prior cards as the starting corpus.
  2. Generate per category, following the playbook's what counts line and the writing rules in scan.md. Assign exactly one category.
  3. Merge near-duplicates per the playbook's merging line; freeze, don't delete.
  4. ground each card using the playbook's sourcing line.
  5. Score every card on each playbook metric, comparatively.
  6. Persist cards and the scan index per scan.md §5.

Output

The scan index, then one line: wrote <n> cards + index to <path or folder> · <mode>.

Example: /technology-scan "non-invasive glucose monitoring"

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Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

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  • It worked
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  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

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