research-industry-patterns
Skill2 starsChanged 4 months agoMITView on GitHub
Research how an industry, market, or domain actually does something and benchmark our situation against it - use when the user names a context (e.g. "Taiwan ecommerce", "testing principles") and wants common practice researched, asks how our approach compares to the norm, or whether a divergence from standard is a problem.
In this repository6 other files it gives its agents.
- add-eval-caseSkill
- defend-your-workSkill
- note-graduationSkill
- obsidian-captureSkill
- user-impactSkill
- writing-adrsSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 18
If either side is fuzzy, pull it from the conversation first.
- Distinctive: line 26
Keep the pass tight by default: a handful of targeted searches, and reason from result snippets.
- Distinctive: line 46
Do not cargo-cult: an industry standard we can consciously dismiss is a finding, not a to-do.
- Distinctive: line 51
Every researched pattern is placed against our situation, and every divergence carries a convention-or-constraint verdict with a dated …
≈ “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.
Files
Research industry patterns
Find what an industry actually does, then benchmark our situation against it. The deliverable is not a survey of the field - it is a placement of us within the field, and a verdict on every place we differ.
Pin the scope
Two things must be concrete before researching, or the report is unanchored:
- The context to research - a market plus a domain. "The software industry" is unresearchable; "trunk-based development for a small team" is.
- Our situation - what we currently do, or the decision we face, in real terms from this conversation.
If either side is fuzzy, pull it from the conversation first; ask once only if it cannot be recovered.
Research, don't recall - but stay tight
Go find current practice with real searches - pretraining recall is stale and carries no source. A genuine standard shows up when independent sources converge; one vendor's marketing or a single loud blog is a data point, not a norm. Date each finding, because a 2019 "best practice" may be today's anti-pattern.
Keep the pass tight by default: a handful of targeted searches, and reason from result snippets. Fetch a full page only to verify a specific load-bearing claim - full-page fetches are the main token cost, so spend them deliberately. Go deeper only when the user asks for depth or the decision is high-stakes enough to warrant it.
Benchmark against our situation
Structure the report around us, not the industry. For each pattern found, place us against it:
- Aligned - we already do this.
- Diverged - we do something different.
- Absent - the industry does it and we don't do it at all.
Convention or constraint
For every divergence, deliver the verdict this skill exists for: is the pattern a constraint or a convention?
- Constraint - it exists for a real reason, and violating it causes a specific failure. This is the headline; name the failure.
- Convention - everyone does it, but nothing breaks if we don't. Report it, then say it is safe to skip.
Do not cargo-cult: an industry standard we can consciously dismiss is a finding, not a to-do. A market norm may itself be a local convention - what Taiwan ecommerce assumes need not bind a global product - so judge the pattern's reason, never its popularity.
Done
Every researched pattern is placed against our situation, and every divergence carries a convention-or-constraint verdict with a dated source. Nothing is reported that we can neither act on nor consciously set aside.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
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.

