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feat: add OpenProse plugin skills

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Peter Steinberger
2026-01-23 00:49:32 +00:00
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# OpenProse Examples
These examples demonstrate workflows using OpenProse's full feature set.
## Available Examples
### Basics (01-08)
| File | Description |
| --------------------------------- | -------------------------------------------- |
| `01-hello-world.prose` | Simplest possible program - a single session |
| `02-research-and-summarize.prose` | Research a topic, then summarize findings |
| `03-code-review.prose` | Multi-perspective code review pipeline |
| `04-write-and-refine.prose` | Draft content and iteratively improve it |
| `05-debug-issue.prose` | Step-by-step debugging workflow |
| `06-explain-codebase.prose` | Progressive exploration of a codebase |
| `07-refactor.prose` | Systematic refactoring workflow |
| `08-blog-post.prose` | End-to-end content creation |
### Agents & Skills (09-12)
| File | Description |
| ----------------------------------- | ------------------------------------ |
| `09-research-with-agents.prose` | Custom agents with model selection |
| `10-code-review-agents.prose` | Specialized reviewer agents |
| `11-skills-and-imports.prose` | External skill imports |
| `12-secure-agent-permissions.prose` | Agent permissions and access control |
### Variables & Composition (13-15)
| File | Description |
| -------------------------------- | ----------------------------------- |
| `13-variables-and-context.prose` | let/const bindings, context passing |
| `14-composition-blocks.prose` | Named blocks, do blocks |
| `15-inline-sequences.prose` | Arrow operator chains |
### Parallel Execution (16-19)
| File | Description |
| ------------------------------------ | ----------------------------------------- |
| `16-parallel-reviews.prose` | Basic parallel execution |
| `17-parallel-research.prose` | Named parallel results |
| `18-mixed-parallel-sequential.prose` | Combined parallel and sequential patterns |
| `19-advanced-parallel.prose` | Join strategies, failure policies |
### Loops (20)
| File | Description |
| ---------------------- | --------------------------------------- |
| `20-fixed-loops.prose` | repeat, for-each, parallel for patterns |
### Pipelines (21)
| File | Description |
| ------------------------------ | ----------------------------------------- |
| `21-pipeline-operations.prose` | map, filter, reduce, pmap transformations |
### Error Handling (22-23)
| File | Description |
| ----------------------------- | -------------------------------------- |
| `22-error-handling.prose` | try/catch/finally patterns |
| `23-retry-with-backoff.prose` | Resilient API calls with retry/backoff |
### Advanced Features (24-27)
| File | Description |
| ------------------------------- | --------------------------------- |
| `24-choice-blocks.prose` | AI-selected branching |
| `25-conditionals.prose` | if/elif/else patterns |
| `26-parameterized-blocks.prose` | Reusable blocks with arguments |
| `27-string-interpolation.prose` | Dynamic prompts with {var} syntax |
### Orchestration Systems (28-31)
| File | Description |
| ------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `28-gas-town.prose` | Multi-agent orchestration ("Kubernetes for agents") with 7 worker roles, patrols, convoys, and GUPP propulsion |
| `29-captains-chair.prose` | Full captain's chair pattern: coordinating agent dispatches subagents for all work, with parallel research, critic review cycles, and checkpoint validation |
| `30-captains-chair-simple.prose` | Minimal captain's chair: core pattern without complexity |
| `31-captains-chair-with-memory.prose` | Captain's chair with retrospective analysis and session-to-session learning |
### Production Workflows (33-38)
| File | Description |
| ---------------------------- | ---------------------------------------- |
| `33-pr-review-autofix.prose` | Automated PR review with fix suggestions |
| `34-content-pipeline.prose` | End-to-end content creation pipeline |
| `35-feature-factory.prose` | Feature implementation automation |
| `36-bug-hunter.prose` | Systematic bug detection and analysis |
| `37-the-forge.prose` | Build a browser from scratch |
| `38-skill-scan.prose` | Skill discovery and analysis |
### Architecture Patterns (39)
| File | Description |
| ---------------------------------- | ---------------------------------------------------------------------------------------------------- |
| `39-architect-by-simulation.prose` | Design systems through simulated implementation phases with serial handoffs and persistent architect |
### Recursive Language Models (40-43)
| File | Description |
| ----------------------------- | ------------------------------------------------------------------- |
| `40-rlm-self-refine.prose` | Recursive refinement until quality threshold - the core RLM pattern |
| `41-rlm-divide-conquer.prose` | Hierarchical chunking for inputs beyond context limits |
| `42-rlm-filter-recurse.prose` | Filter-then-process for needle-in-haystack tasks |
| `43-rlm-pairwise.prose` | O(n^2) pairwise aggregation for relationship mapping |
### Meta / Self-Hosting (44-48)
| File | Description |
| --------------------------------- | ------------------------------------------------------ |
| `44-run-endpoint-ux-test.prose` | Concurrent agents testing the /run API endpoint |
| `45-plugin-release.prose` | OpenProse plugin release workflow (this repo) |
| `46-workflow-crystallizer.prose` | Reflective: observes thread, extracts workflow, writes .prose |
| `47-language-self-improvement.prose` | Meta-level 2: analyzes .prose corpus to evolve the language itself |
| `48-habit-miner.prose` | Mines AI session logs for patterns, generates .prose automations |
## The Architect By Simulation Pattern
The architect-by-simulation pattern is for designing systems by "implementing" them through reasoning. Instead of writing code, each phase produces specification documents that the next phase builds upon.
**Key principles:**
1. **Thinking/deduction framework**: "Implement" means reasoning through design decisions
2. **Serial pipeline with handoffs**: Each phase reads previous phase's output
3. **Persistent architect**: Maintains master plan and synthesizes learnings
4. **User checkpoint**: Get plan approval BEFORE executing the pipeline
5. **Simulation as implementation**: The spec IS the deliverable
```prose
# The core pattern
agent architect:
model: opus
persist: true
prompt: "Design by simulating implementation"
# Create master plan with phases
let plan = session: architect
prompt: "Break feature into design phases"
# User reviews the plan BEFORE the pipeline runs
input user_approval: "User reviews plan and approves"
# Execute phases serially with handoffs
for phase_name, index in phases:
let handoff = session: phase-executor
prompt: "Execute phase {index}"
context: previous_handoffs
# Architect synthesizes after each phase
resume: architect
prompt: "Synthesize learnings from phase {index}"
context: handoff
# Synthesize all handoffs into final spec
output spec = session: architect
prompt: "Synthesize all handoffs into final spec"
```
See example 39 for the full implementation.
## The Captain's Chair Pattern
The captain's chair is an orchestration paradigm where a coordinating agent (the "captain") dispatches specialized subagents for all execution. The captain never writes code directly—only plans, coordinates, and validates.
**Key principles:**
1. **Context isolation**: Subagents receive targeted context, not everything
2. **Parallel execution**: Multiple subagents work concurrently where possible
3. **Continuous criticism**: Critic agents review plans and outputs mid-stream
4. **80/20 planning**: 80% effort on planning, 20% on execution oversight
5. **Checkpoint validation**: User approval at key decision points
```prose
# The core pattern
agent captain:
model: opus
prompt: "Coordinate but never execute directly"
agent executor:
model: sonnet
prompt: "Execute assigned tasks precisely"
agent critic:
model: sonnet
prompt: "Review work and find issues"
# Captain plans
let plan = session: captain
prompt: "Break down this task"
# Parallel execution with criticism
parallel:
work = session: executor
context: plan
review = session: critic
context: plan
# Captain validates
output result = session: captain
prompt: "Validate and integrate"
context: { work, review }
```
See examples 29-31 for full implementations.
## The Recursive Language Model Pattern
Recursive Language Models (RLMs) are a paradigm for handling inputs far beyond context limits. The key insight: treat the prompt as an external environment that the LLM can symbolically interact with, chunk, and recursively process.
**Why RLMs matter:**
- Base LLMs degrade rapidly on long contexts ("context rot")
- RLMs maintain performance on inputs 2 orders of magnitude beyond context limits
- On quadratic-complexity tasks, base models get <0.1% while RLMs achieve 58%
**Key patterns:**
1. **Self-refinement**: Recursive improvement until quality threshold
2. **Divide-and-conquer**: Chunk, process, aggregate recursively
3. **Filter-then-recurse**: Cheap filtering before expensive deep dives
4. **Pairwise aggregation**: Handle O(n²) tasks through batch decomposition
```prose
# The core RLM pattern: recursive block with scope isolation
block process(data, depth):
# Base case
if **data is small** or depth <= 0:
output session "Process directly"
context: data
# Recursive case: chunk and fan out
let chunks = session "Split into logical chunks"
context: data
parallel for chunk in chunks:
do process(chunk, depth - 1) # Recursive call
# Aggregate results (fan in)
output session "Synthesize partial results"
```
**OpenProse advantages for RLMs:**
- **Scope isolation**: Each recursive call gets its own `execution_id`, preventing variable collisions
- **Parallel fan-out**: `parallel for` enables concurrent processing at each recursion level
- **State persistence**: SQLite/PostgreSQL backends track the full call tree
- **Natural aggregation**: Pipelines (`| reduce`) and explicit context passing
See examples 40-43 for full implementations.
## Running Examples
Ask Claude to run any example:
```
Run the code review example from the OpenProse examples
```
Or reference the file directly:
```
Execute examples/03-code-review.prose
```
## Feature Reference
### Core Syntax
```prose
# Comments
session "prompt" # Simple session
let x = session "..." # Variable binding
const y = session "..." # Immutable binding
```
### Agents
```prose
agent name:
model: sonnet # haiku, sonnet, opus
prompt: "System prompt"
skills: ["skill1", "skill2"]
permissions:
read: ["*.md"]
bash: deny
```
### Parallel
```prose
parallel: # Basic parallel
a = session "A"
b = session "B"
parallel ("first"): # Race - first wins
parallel ("any", count: 2): # Wait for N successes
parallel (on-fail: "continue"): # Don't fail on errors
```
### Loops
```prose
repeat 3: # Fixed iterations
session "..."
for item in items: # For-each
session "..."
parallel for item in items: # Parallel for-each
session "..."
loop until **condition** (max: 10): # Unbounded with AI condition
session "..."
```
### Pipelines
```prose
items | map: # Transform each
session "..."
items | filter: # Keep matching
session "..."
items | reduce(acc, x): # Accumulate
session "..."
items | pmap: # Parallel transform
session "..."
```
### Error Handling
```prose
try:
session "..."
catch as err:
session "..."
finally:
session "..."
session "..."
retry: 3
backoff: "exponential" # none, linear, exponential
throw "message" # Raise error
```
### Conditionals
```prose
if **condition**:
session "..."
elif **other condition**:
session "..."
else:
session "..."
```
### Choice
```prose
choice **criteria**:
option "Label A":
session "..."
option "Label B":
session "..."
```
### Blocks
```prose
block name(param): # Define with parameters
session "... {param} ..."
do name("value") # Invoke with arguments
```
### String Interpolation
```prose
let x = session "Get value"
session "Use {x} in prompt" # Single-line
session """ # Multi-line
Multi-line prompt with {x}
"""
```
## Learn More
See `compiler.md` in the skill directory for the complete language specification.