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- Alekspetrov
- Navigator
- Nav Diagnose
nav-diagnose_skill
- Python
142
GitHub Stars
1
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill alekspetrov/navigator --skill nav-diagnose- SKILL.md9.6 KB
Overview
This skill detects drops in human–AI collaboration quality and triggers a short diagnostic and re-anchoring flow to restore alignment. It auto-invokes after repeated corrections, context mix-ups, loop stagnation, or explicit user signals like "something seems off." The goal is to catch misalignment early and re-establish a clear, shared understanding before productivity degrades.
How this skill works
The skill monitors recent exchanges (typically the last 10–15 messages) for quality signals: repeated corrections, hallucination reports, context confusion, frustration language, and loop stagnation. When thresholds are met, it calculates a severity level, displays a concise diagnostic summary, and prompts the user to confirm a reconstructed goal or correct assumptions. After confirmation it applies the re-anchored instructions and can optionally log diagnostics for adaptive preferences.
When to use it
- Auto-trigger after 2+ identical corrections on the same topic
- When user says phrases like "something seems off" or "you're not getting this"
- When hallucination or references to non-existent artifacts appear
- If loop mode shows 3+ identical state iterations (stagnation)
- When context usage is high and quality indicators start degrading
Best practices
- Do not trigger on a single correction or when user provides new requirements
- Keep diagnostics brief and non-blaming; focus on concrete observations
- Confirm specific points (goal, constraints, technical context) rather than general agreement
- Offer immediate corrective steps and preventive suggestions (e.g., compact context)
- Log recurrent issues to user profile to reduce repeat friction
Example use cases
- User corrects plural REST naming twice; skill re-anchors on naming convention
- AI mixes features from two projects; skill prompts technical re-alignment and suggests clearing context
- User shows frustration with repeated outputs; skill pauses, summarizes issues, and asks for correction
- Looped debugging step repeats; skill detects stagnation and offers next-step options
- Hallucinated file or function is reported; skill flags high severity and requests clarification
FAQ
No. It avoids firing on single corrections or when the user is adding new requirements; it only triggers when quality indicators cross configured thresholds.
Can it remember preferences to avoid repeat issues?
Yes. When a nav-profile exists, diagnostics can be logged and recurring preferences suggested to persist expected behaviors.