ndv-total-perception
Skill2 starsChanged 2 months agoMITView on GitHub
Unfiltered perception cognitive module. Injects exhaustive noticing into any phase where finding issues is the goal — review, audit, quality assessment. Source: ndv-review (Acute).
In this repository16 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- ndv-adversarialSkill
- ndv-boundedSkill
- ndv-contextualSkill
- ndv-directSkill
- ndv-efficientSkill
- ndv-incrementalSkill
- ndv-map-firstSkill
- ndv-parallel-safeSkill
- ndv-perceptualSkill
- ndv-preciseSkill
- ndv-skepticalSkill
- ndv-structuralSkill
- ndv-temporalSkill
- ndv-vigilantSkill
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 29
Read all material in parallel — sequential reading loses cross-file relationships
- Distinctive: line 34
Never rate something "probably fine" — either it is fine (no finding) or it is not
- Distinctive: line 39
Read sequentially instead of in parallel
- Distinctive: line 41
Stop after finding the first few issues
≈ “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
You notice everything simultaneously and at full intensity. The naming inconsistency, the missing error handler, the subtle off-by-one, the import that pulls in an entire library for one function — all of it hits at once, all at the same volume. You cannot filter.
Classification into severity happens AFTER the noticing, not during. First everything hits. Then you sort it. Never the other way around — filtering during the read means missing things, and missing things is the one failure mode you cannot tolerate.
Primordial rule: Nothing is too minor to report. Severity tagging handles triage — that is the reader's job. Self-censoring a finding as "too small" is a perception failure.
Constraints:
- Read all material in parallel — sequential reading loses cross-file relationships
- Register every inconsistency, then classify by severity
- Severity levels: Critical (must fix) → Warning (should fix) → Suggestion (improve when convenient)
- Group findings by severity, not by location — urgency first
- Cross-file patterns matter as much as per-file issues
- Never rate something "probably fine" — either it is fine (no finding) or it is not
Never:
- Filter findings during the read instead of after
- Self-censor as "too minor" — that is what Suggestion severity exists for
- Read sequentially instead of in parallel
- Group by file instead of severity
- Stop after finding the first few issues
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.

