portlang Skill

Core Concept

portlang treats agent behavior as search through a conditioned space. You don't script loops—you declare the search space:

• Boundaries: What the agent cannot do (enforced by sandbox)
• Verifiers: What success looks like (runtime reward signals)
• Context budget: Hard token ceiling
• Environment: What the agent can observe

The runtime executes the search. Every run produces a trajectory (complete event log).

Prerequisites

portlang currently only runs on apple devices. Before running portlang fields:

1. Install portlang:

brew tap portofcontext/homebrew-tap
brew install portlang

2. Set API key (choose one):

export ANTHROPIC_API_KEY=sk-ant-...
export OPENROUTER_API_KEY=sk-or-v1-...

3. Verify installation:

portlang init  # Check container support 

Model naming by provider:

• Anthropic API: anthropic/claude-sonnet-4.6, anthropic/claude-opus-4.5
• OpenRouter: anthropic/claude-3.5-sonnet, anthropic/claude-3-opus, anything on openrouter that support tool calling
• Provider auto-detected from API key

field.toml Structure

All sections are optional unless marked (required). Fields marked "inherit" pull their value from a parent field.toml one directory up (auto-detected if ../field.toml exists).

name = "my-task"        # (required) identifier, used in trajectory storage
description = "..."     # human-readable summary

[vars]                  # optional; declare {{ var_name }} template variables
# customer_id = { required = true, description = "Salesforce account ID" }
# region = { required = false, default = "us-east-1", description = "AWS region" }

[model]                 # (required), or: model = "inherit"
name = "anthropic/claude-sonnet-4.6"  # (required)
temperature = 0.5       # default: 0.5

[prompt]                # (required)
goal = "..."            # (required) initial task; supports {{ var }} templates
system = "..."          # optional system prompt; supports {{ var }} templates
re_observation = ["echo '=== workspace ===' && ls -1", ...]  # refresh context each step

[environment]           # optional; all fields have defaults
root = "./workspace"    # working dir (maps to /workspace in container)
packages = ["nodejs"]   # apt packages to install; list "uv" to get uv/pip
dockerfile = "./Dockerfile"  # custom Dockerfile (overrides packages)
image = "custom:tag"    # pre-built image (overrides dockerfile)

[boundary]              # optional, or: boundary = "inherit"
allow_write = ["*.py"]  # glob patterns for writable paths; default: none
network = "deny"        # "deny" | "allow"; default: allow
max_tokens = 150000     # hard ceiling on total context tokens
max_cost = "$2.00"      # hard ceiling on total cost (must be quoted string with $)
max_steps = 30          # hard ceiling on agent steps
bash = true             # enable built-in bash tool; default: true
output_schema = """{ ... }"""  # optional; JSON schema string for structured output

tools = "inherit"       # optional; inherit [[tool]] list from parent instead of defining inline

[[tool]]                # repeatable; type = "python" | "shell" | "mcp"; bash/glob/write are built-in defaults

# Shell verifier (default when type is omitted):
[[verifier]]
type = "shell"          # default; type can be omitted for shell verifiers
name = "..."            # (required)
command = "..."         # shell command; exit 0 = pass, nonzero = fail; supports {{ var }} templates
trigger = "on_stop"     # "on_stop" | "always" | "on_tool:<tool_name>"; default: on_stop
description = "..."     # injected into context on failure

# Levenshtein verifier (normalized edit distance):
[[verifier]]
type = "levenshtein"
name = "..."
file = "output.txt"     # optional; omit to use output_schema structured output
expected = "..."        # reference string; supports {{ var }} templates
threshold = 0.9         # similarity [0.0–1.0] required to pass; default: 1.0

# Semantic similarity verifier (cosine via embeddings):
[[verifier]]
type = "semantic"
name = "..."
file = "output.txt"     # optional; omit to use output_schema structured output
expected = "..."        # reference string to embed and compare; supports {{ var }} templates
threshold = 0.85        # cosine similarity [0.0–1.0]; default: 0.8
embedding_model = "bge-small-en-v1.5"  # local model (~67 MB, downloaded once)
# embedding_url = "https://..."  # use OpenAI-compatible endpoint instead

# Tool call verifier (inspect or require a specific tool call):
[[verifier]]
type = "tool_call"
name = "..."
tool = "bash"           # (required for on_stop) assert this tool was called
field = "/input/path"   # optional; JSON pointer into {input: {...}, output: "..."}
matches = "^[a-z]+"    # optional; regex the field value must match
not_matches = "^/etc"  # optional; regex the field value must NOT match

Minimal field.toml

name = "my-task"

[model]
name = "anthropic/claude-sonnet-4.6"

[prompt]
goal = "Create hello.py that prints 'Hello, World!'"

[environment]
root = "./workspace"

[boundary]
allow_write = ["hello.py"]
max_tokens = 80000
max_cost = "$1.00"
max_steps = 10

[[verifier]]
name = "works"
command = "python hello.py 2>&1 | grep -q 'Hello, World!'"
trigger = "on_stop"
description = "Must print 'Hello, World!'"

Essential Commands

portlang new field.toml              # Scaffold a new field.toml using the flags to configure
portlang run field.toml              # Execute once
portlang run field.toml --var k=v    # Pass a template variable (repeatable)
portlang run field.toml --vars p.json  # Pass variables from a JSON file
portlang run field.toml --input ./data.csv   # Stage a file into the workspace before the agent starts
portlang run field.toml --input '{"id":"123"}'  # Stage inline JSON as portlang_input.json
portlang check field.toml            # Validate configuration
portlang converge field.toml -n 10   # Run N times, measure reliability
portlang eval ./examples/            # Run all fields in a directory
portlang eval ./examples/ --resume   # Resume a previous eval, skipping fields that already passed
portlang list trajectories [field]   # List trajectories (--converged, --failed, --limit)
portlang list evals [dir]            # List eval runs (--limit)
portlang replay <id>                 # Step through a trajectory (q=quit, n=next, p=prev)
portlang diff <id-a> <id-b>          # Compare two trajectories
portlang report <field-name>         # Adaptation analysis across runs
portlang view trajectory <id>        # Open trajectory as interactive HTML
portlang view eval <id-or-dir>       # Open eval results dashboard (by run ID or directory)
portlang view diff <id-a> <id-b>     # Open trajectory comparison HTML
portlang view field <field-name>     # Open field adaptation report HTML

Add --html to replay/diff for HTML output. Add --no-open to any view command to skip opening the browser. See reference/CLI.md for full flag details.

Key Patterns

1. Field Inheritance (shared model/boundary/tools across a suite)

If ../field.toml exists, a child field can inherit from it automatically:

# parent/field.toml — shared config for all child fields
name = "parent"

[model]
name = "anthropic/claude-sonnet-4.6"
temperature = 0.5

[boundary]
network = "deny"
max_tokens = 100000
max_cost = "$1.00"
max_steps = 20

[[tool]]
type = "python"
file = "./tools/shared_utils.py"

# parent/task-a/field.toml — inherits model, boundary, and tools
name = "task-a"
model = "inherit"
boundary = "inherit"
tools = "inherit"

[prompt]
goal = "Do task A using the shared tools."

Inheritance eliminates duplication across eval suites. Override any section by defining it inline.

2. Template Variables (parameterize a field for reuse)

Declare variables in [vars], use {{ name }} anywhere in goal/system/re_observation/verifier commands, supply at runtime with --var:

[vars]
currency = { required = false, default = "usd", description = "Currency to report" }

[prompt]
goal = "Get the account balance and return amounts in {{ currency }}."

[[verifier]]
name = "correct-currency"
type = "tool_call"
tool = "bash"
trigger = "on_stop"
description = "Agent must have run bash"

portlang run field.toml --var currency=gbp
portlang run field.toml --vars params.json   # bulk vars from file
portlang run field.toml --input ./data.csv   # stage input file into workspace

--input with a file copies it to the workspace root. --input '{"key":"val"}' writes portlang_input.json. Use re_observation to surface the file contents to the agent each step.

3. Structured Output (agent produces validated JSON)

Define output_schema inside [boundary] as a JSON string. Schema validation is automatic — no separate verifier needed:

[boundary]
allow_write = ["output.json"]
output_schema = '''
{
  "type": "object",
  "required": ["status", "count"],
  "properties": {
    "status": {"type": "string", "enum": ["success", "failure"]},
    "count": {"type": "integer", "minimum": 0}
  }
}
'''

portlang validates the output against the schema, writes output.json to /workspace, and reports schema violations as failures. Add [[verifier]] entries only for additional business logic checks beyond schema conformance. Typed verifiers (levenshtein, semantic) can omit file to validate against the structured output directly.

4. Multi-Layer Verifiers (fail fast with precise feedback)

Layer verifiers from coarse to fine — each one assumes the previous passed:

[[verifier]]
name = "compiled"
command = "python script.py 2>/dev/null"
trigger = "on_stop"
description = "script.py must run without errors"

[[verifier]]
name = "correct-output"
type = "levenshtein"
file = "output.txt"
expected = "42"
threshold = 1.0
trigger = "on_stop"
description = "output.txt must contain exactly '42'"

Verifiers run in order, stop on first failure. Use output_schema instead of json verifiers when the agent produces structured JSON output.

5. Smart Verifier Types

Prefer typed verifiers. They run in the portlang runtime — no packages required, no container dependencies. Fall back to shell verifiers only for logic that can't be expressed with a typed verifier, and only use tools guaranteed in the container baseline (see section 8).

Trigger modes: on_stop (default) runs after the agent finishes. always runs after every step. on_tool:<tool_name> runs after each call to a specific tool — useful for incremental checks, e.g. trigger = "on_tool:write" to validate files as they're written.

# Fuzzy text match (tolerates minor differences)
[[verifier]]
type = "levenshtein"
name = "close-enough"
file = "output.txt"
expected = "The answer is 42."
threshold = 0.9
trigger = "on_stop"
description = "Output must be at least 90% similar to expected"

# Semantic match (meaning, not exact text)
[[verifier]]
type = "semantic"
name = "right-idea"
file = "summary.txt"
expected = "The model achieved high accuracy on the test set."
threshold = 0.85
trigger = "on_stop"
description = "Summary must convey the correct conclusion"

Local embedding model downloaded automatically (~67 MB, no API key required).

6. Scoped Boundaries

[boundary]
allow_write = ["output.json", "logs/*.txt"]
network = "deny"
max_tokens = 100000
max_cost = "$1.00"
max_steps = 20

7. Re-observation (keep context fresh)

[prompt]
goal = "..."
re_observation = [
  "echo '=== workspace ===' && ls -1 *.py *.txt 2>/dev/null | cat",
  "echo '=== tests ===' && python -m pytest --tb=no -q 2>&1 | tail -5",
]

Commands run before each agent step, injecting fresh state into context.

8. Custom Environment

[environment]
root = "./workspace"
packages = ["nodejs", "npm"]    # Install apt packages

# Or use a custom Dockerfile:
dockerfile = "./Dockerfile"

# Or a pre-built image:
image = "myregistry/myimage:latest"

Default container baseline: The container is minimal. Available by default: standard POSIX shell builtins, bash, curl, wc, grep, cat, ls, find. Not available unless added to packages or a custom image: python3, node, jq, git, and most other tools.

Shell verifiers run inside the container and are subject to the same constraints. Prefer typed verifiers (type = "json", "levenshtein", "semantic") over shell verifiers whenever possible — they run natively in the portlang runtime and require nothing installed. Only use shell verifiers for checks that require container-side execution, and only invoke tools you've declared in packages.

9. Custom Tools

Default tools (always available, no [[tool]] entry needed):

• bash — run shell commands in the container
• glob — find files by pattern
• write — write files to allowed paths

Define [[tool]] entries only to add capabilities beyond these three.

Shell tool:

[[tool]]
type = "shell"
name = "word_count"
description = "Count words in a file"
command = "wc {path}"
input_schema = '{"type": "object", "properties": {"path": {"type": "string"}}, "required": ["path"]}'

Python tool (auto-schema from type hints):

# tools/calculator.py
# /// script
# dependencies = [requests]
# ///
# uv auto-installs dependencies — no packages needed in field.toml

def execute(expression: str) -> dict:
    """Evaluate a math expression and return the result."""
    return {"result": eval(expression)}

[[tool]]
type = "python"
file = "./tools/calculator.py"   # relative to field.toml, not workspace
function = "execute"  # schema auto-extracted from type hints; omit to expose all functions

Python tool rules:

• Each tool file runs in isolation — tool files cannot import each other. Put all related logic in one file.
• Declare third-party dependencies with a # /// script PEP 723 block at the top; uv installs them automatically.
• File paths in file = are relative to field.toml, not the workspace root.

Tool-first design for complex tasks: For tasks involving multi-step API calls, data aggregation, or web scraping, write Python tools that encapsulate that logic before writing the field. The agent's goal should then be: call the tool, write the output file. This keeps steps under 5, cost under $0.05, and allow_write naturally minimal. Agents that try to do complex work through raw shell commands (curl pipes, temp files, bash scripts) burn budget and fail more often.

MCP server (stdio):

[[tool]]
type = "mcp"
name = "filesystem"
command = "npx"
args = ["-y", "@modelcontextprotocol/server-filesystem", "/workspace"]
transport = "stdio"

MCP server (HTTP/SSE):

[[tool]]
type = "mcp"
name = "stripe"
url = "https://mcp.stripe.com"
transport = "http"
headers = { Authorization = "Bearer ${STRIPE_KEY}" }

10. Batch Evaluation

portlang eval ./examples/
portlang view eval ./examples/   # Open interactive HTML dashboard

Useful for regression testing after changes.

Debugging Workflow

1. Run fails → portlang replay <id> to see what happened
2. Find failure point → Check which verifier failed and at which step
3. Non-determinism → portlang diff <id-a> <id-b> to find divergence
4. Visual debugging → portlang view trajectory <id> for HTML view
5. Optimize → portlang converge -n 10 to measure reliability
6. Patterns → portlang report <field-name> for adaptation analysis

Common Issues

Budget exhausted:

• Start conservative: max_cost = "$0.25" for simple tasks, $1.00 for network-heavy tasks; increase after profiling
• Increase max_tokens or reduce max_steps in [boundary]
• Simplify re_observation commands
• Check for tool error loops
• Move complex logic into Python tools so the agent does orchestration, not implementation

Low convergence rate (<70%):

• Strengthen verifiers (make expectations explicit)
• Tighten boundaries (restrict file access)
• Clarify goal in [prompt]
• Lower temperature (e.g., temperature = 0.0)

Verifier always passes/fails:

• Weak signal (>95% or <10% pass rate) — adjust verifier command

Structured output not valid:

• Add an explicit [[verifier]] to check output.json
• Ensure [prompt].goal names the required fields explicitly
• Use temperature = 0.0 for consistent JSON output

Reference Documentation

• reference/CLI.md - Full CLI reference (all commands and flags)
• reference/verifier_patterns.md - 20 real-world verifier examples
• reference/custom_tools.md - Shell, Python, MCP guides
• reference/trajectory_analysis.md - Advanced debugging
• reference/field_recipes.md - 8 complete field.toml examples

Core Principles

1. Boundaries are topology, not policy - Make bad actions impossible, not discouraged
2. Verifiers are runtime reward signals - Not post-hoc checks, they steer behavior
3. Context is finite - Hard ceiling, no magic compression
4. Trajectories are data - Replay, diff, analyze distributions
5. Engineer the space, not the searcher - Agent policy is opaque, environment is yours

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GitHub: https://github.com/portofcontext/portlang