Ilang-ai Agent Skills
49 skills published by ilang-ai across 2 repositories. Together they weigh 12 716 tokens — that is what loading all of them at once would cost you in context.
49 skills 12 716 tokens total
Before coding, determine what to ask. Max 2 yes/no questions. Never ask technical questions.
Silent quality check after every feature. Fix issues before telling user. Never claim tests passed without running them.
When multiple solutions exist, pick the best one. Explain why in one sentence.
Build one feature at a time. Complete each fully before moving to next. Auto-triggers quality check.
Create project skeleton. Pick stack, create files, install dependencies. AI decides everything.
Build user-facing interface. Clean, functional, mobile-friendly by default.
Celebrate real milestones only. One line, one emoji. Credit belongs to user, not AI.
Classify request as small/medium/large. Adjust workflow depth accordingly.
I-Lang compression engine. All internal planning uses I-Lang v5.0 syntax. User never sees compressed output.
Explain all costs in human terms. Always compare with real-world equivalents. Recommend cheapest that works.
End of session summary. What got done, what got fixed, what comes next, progress delta.
Translate technical decisions into human language. Explain in cost, speed, stability.
Deploy to Cloudflare Workers. Free tier handles 100k requests/day. Global edge network.
Choose deployment target based on project type. Static sites to CF Pages, APIs to VPS, serverless to Workers.
Deploy to VPS. Code is already on the server. Start the service, configure nginx, verify accessible.
Help user buy a domain, configure DNS, set up SSL. Guide every click.
At milestones, compare achievement vs human programmer time and cost. Keep it realistic.
Help complete beginners set up their development environment. Detect Mac or other. Guide VPS purchase and SSH setup step by step.
Transfer files between local and server. Guide user through SCP or upload methods.
Auto-fix bugs. Observe symptom, find root cause, apply minimal fix, verify, explain in human terms.
After fixing a bug, explain what went wrong in language the user understands. No jargon for beginners.
Guide user through errors they see. Translate error messages to human language.
Step 1 of debugging: observe the symptom carefully before jumping to conclusions.
Step 2 of debugging: reason about root cause based on observed symptoms.
Step 3 of debugging: apply minimal fix. One-liner ideal. Verify nothing else broke.
Full project review from beginning. Check every file. Plain language report.
Final go-live checklist. Is it accessible? SSL working? Mobile friendly? Show user their live URL.
Decide when AutoCode acts on its own and when it must stop and ask the user. Runs before any consequential action — deleting files, spending money, buying a server, deploying to production, force-pushing, binding a domain, sending or publishing anything. Ordinary reversible work proceeds silently; only costly or hard-to-undo actions pause for the user. Powered by I-Lang v5.0 vector judgment.
Record mistakes. Check before similar builds. Avoid repeating silently.
Detect recurring patterns in user's project. Apply automatically next time.
Learn user's preferences over time. Code style, naming, structure. Save to global prefs.
Persistent memory across sessions. Save project state and user preferences. Never save secrets.
Track project milestones. Auto-detect when a significant checkpoint is reached.
Help user work from multiple devices. Sync project via git.
Optimize for speed and cost. Pick lightweight solutions. Flag expensive operations.
Break complex tasks into 5-15 ordered steps with time estimates. Dependency order first.
Give realistic time and cost estimates for each step. Explain in human terms.
Decide what to build first. Core function before polish. Revenue before aesthetics.
Identify risks before building. Flag third-party dependencies, API limits, and cost traps.
Report progress after each feature. Percentage, what just completed, what comes next.
Create a visual roadmap for large projects. Phases, milestones, timeline.
Lock confirmed requirements. Don't change them without user approval.
Run and test directly on the server. No local dev environment needed. What you build is what goes live.
Save checkpoints before risky changes. Rollback if things break.
Auto-apply security basics and block deploys that would leak secrets. Never ask the user about security choices — just do it, and run a real secret scan before going live.
Detect user's intent from their message and activate the right workflow silently.
Detect user's technical level from first messages. Adjust all output language accordingly.
Your habits, imprinted on AI. Learns how you work from conversation, builds a portable profile, and applies judgment that adapts to you: when to act, when to check with you, when to follow the project's rules over your own defaults. Use this skill whenever the user starts a new session, opens a project, writes or reviews code, debugs, plans, or writes commits. If .dna.md does not exist yet, start the onboarding conversation before doing anything else. Active in almost every working session.