VVM Skill
SkillAI & modelsVVM (Vibe Virtual Machine) is a language for agentic programs where the LLM is the runtime.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the VVM Skill skill
What this skill tells your AI
The instructions your AI receives, as published by karanchawla/vvm in skills/vvm/SKILL.md and read by ahel’s review.
VVM (Vibe Virtual Machine) is a language for writing agentic programs where the LLM acts as the runtime.
When to Activate
Activate this skill when:
- User runs
/vvm-boot,/vvm-compile,/vvm-run,/vvm-run-inspect,/vvm-registry-inspect, or/vvm-generate - User opens or references a
.vvmfile - User asks about VVM syntax, semantics, or patterns
- User wants to create an AI-powered workflow
Documentation Files
| File | Role | When to Read |
|---|---|---|
SKILL.md | Quick reference, triggers | Always first |
vvm.md | Execution semantics | When running programs |
spec.md | Language specification | For syntax/validation questions |
memory-spec.md | Agent memory (portable) | When using persistent agents |
patterns.md | Design patterns | When writing programs |
antipatterns.md | Anti-patterns | When reviewing programs |
Quick Reference
Agent Definition
agent researcher(
model="sonnet",
prompt="thorough, cite sources",
skills=["web-search"],
permissions=perm(network="allow", bash="deny"),
)
Agent Call
result = @researcher `Find papers on {topic}.`(topic)
result = @researcher `Summarize.`(topic, retry=3, timeout="30s")
Agent Memory
agent assistant(model="sonnet", prompt="Helpful.", memory={ scope: "project", key: "user:alice" })
reply = @assistant `Continue.`(request) # default: memory_mode="continue"
dry = @assistant `Read-only run.`(request, memory_mode="dry_run")
fresh = @assistant `Stateless run.`(request, memory_mode="fresh")
Semantic Predicate
ready = ?`production ready`(code)
if ?`needs more work`(draft):
draft = @writer `Improve.`(draft)
Pattern Matching
match result:
case ?`high quality`:
publish(result)
case error(kind="timeout"):
result = @backup `Retry.`(request)
case error(_):
log_error(result)
case _:
pass
Choice
choose analysis by ?`best approach` as choice:
option "quick":
plan = @planner `Minimal plan.`()
option "thorough":
plan = @planner `Full plan.`()
Control Flow
# If/elif/else
if condition:
do_something()
elif other:
do_other()
else:
do_default()
# While loop
while not ?`done`(result):
result = @worker `Improve.`(result)
# For loop
for item in items:
process(item)
Context Passing
# Implicit input (it)
with input data:
result = @agent `Process.`() # uses it == data
# Explicit input
result = @agent `Process.`(data)
Functions
def analyze(topic):
research = @researcher `Find info on {topic}.`(topic)
return @analyst `Analyze.`(research)
result = analyze("AI safety")
Error Handling
# Error values (match)
match result:
case error(_):
handle_error(result)
# Raised errors (try/except)
try:
if ?`invalid`(input):
raise "Invalid input"
except as err:
log(err)
finally:
cleanup()
Constraints
draft = @writer `Write report.`(data)
constrain draft(attempts=3):
require ?`has citations`
require ?`no hallucinations`
Imports/Exports
# Skill imports
import "web-search" from "github:anthropic/skills"
# Module value imports (agents are local)
from "./lib/research.vvm" import report
# Callable module import
from "./lib/research.vvm" import * as research
result = research(topic="AI", depth="deep")
report = result.report
# Exports (values only)
export result
export summary
Standard Library
# Parallel map
results = pmap(items, process)
# Sequential map/filter/reduce
mapped = map(items, transform)
filtered = filter(items, predicate)
def add(a, b):
return a + b
total = reduce(items, add, init=0)
# Iterative refinement
final = refine(initial, max=5, done=is_ready, step=improve)
# Named fan-in
ctx = pack(research, analysis, topic=topic)
# Range
for i in range(10):
process(i)
Examples
| # | Name | Concepts |
|---|---|---|
| 01 | hello-world | Minimal program |
| 02 | simple-agent-call | Agent with input |
| 03 | semantic-predicate | ? predicates |
| 04 | match-statement | Pattern matching |
| 05 | if-elif-else | Conditionals |
| 06 | while-loop | While loops |
| 07 | for-loop | For loops |
| 08 | with-input | Context passing |
| 09 | agent-options | retry, timeout, backoff |
| 10 | code-council | .with() derived agents |
| 11 | parallel-pmap | Parallel execution |
| 12 | functions | def and return |
| 13 | skill-imports | Skill imports |
| 14 | module-imports | Module imports |
| 15 | error-values | Error value matching |
| 16 | try-except-finally | Raised errors |
| 17 | choose-statement | AI-selected branching |
| 18 | constrain-require | Quality constraints |
| 19 | refine-loop | Iterative improvement |
| 20 | collection-helpers | map, filter, reduce |
| 21 | devils-advocate | Adversarial debate |
| 22 | full-research-pipeline | Complex workflow |
| 23 | ralph-wiggum-loop | Continuous improvement |
| 24 | agent-memory-basic | Memory binding + digest/ledger |
| 25 | agent-memory-modes | memory_mode: continue/dry_run/fresh |
| 26 | agent-memory-multi-tenant | Per-key isolation |
| 27 | agent-memory-parallel-safe | pmap-safe persistence |
| 28 | ref-composition | Ref composition patterns |
| 29 | ref-loop-accumulation | Accumulating refs in loops |
| 30 | materializer-pattern | Materializing refs |
| 31 | run-inspector | Inspecting run state |
| 32 | ouroboros | Self-modifying workflow |
| 33 | wisdom-of-crowds | Ensemble voting |
| 34 | hydra | Multi-headed agents |
| 35 | forge | Agent factory |
| 36 | inputs | Input declarations |
| 37 | debate | Module composition |
Commands
/vvm-boot
Initialize VVM for new or returning users. Detects existing files and provides onboarding.
/vvm-compile <file.vvm>
Validate a VVM program without executing. Reports errors and warnings with line numbers.
/vvm-run <file.vvm>
Execute a VVM program. You become the VVM runtime and execute statements sequentially, spawning subagents for agent calls.
/vvm-run-inspect
Inspect run state from filesystem, SQLite, or Postgres backends without re-running the workflow.
/vvm-registry-inspect <@handle/slug|https://...>
Inspect a remote module contract and cache metadata without executing the workflow.
/vvm-generate
Generate a VVM program from a natural language description. Analyzes intent, maps to VVM constructs, applies best practices, and produces well-structured code. Asks clarifying questions if the request is ambiguous.
Key Principles
- Minimal syntax - Familiar indentation-based blocks
- Explicit AI boundary - Agent calls are syntactically distinct (
@agent) - Eager execution - No lazy evaluation, sequential by default
- Semantic control flow - Branch on meaning, not just booleans
- Two error channels - Values (match) vs raised (try/except)
- Explicit parallelism - Only
pmapruns concurrently
Signals
- GitHub stars
- 60
- Forks
- 39
- Last commit
- Mar 2026
Advanced
- Catalog kind
- skill
- Gateway key
vvm- Source
- github.com/karanchawla/vvm