skillflow-py 1.0.3

Config-agnostic LLM pipeline graph executor — turn skills into flows

Skillflow

Config-agnostic LLM pipeline graph executor. Define multi-agent pipelines as YAML DAGs — skillflow handles traversal, tool execution, checkpoints, recovery, and event streaming on SQLite.

Install

pip install skillflow-py      # PyPI
pip install -e ~/skillflow    # from repo (editable)

Or clone and use the install script, which also registers CLI commands:

git clone https://github.com/linxuhao/SkillFlow.git
bash skillflow/scripts/install.sh

CLI commands registered in ~/.local/bin/:

Command	Description
skillflow-lint	Validate pipeline YAML files (one-shot)
skillflow-run	Stateless pipeline runner (agent calls via CLI)
skillflow-convert	Convert a skill description → pipeline YAML

skillflow-lint configs/*.yaml               # one-shot validation
skillflow-run --graph pipeline.yaml --action next   # start a pipeline (returns JSON)
skillflow-run --action submit --run-id <id> --result '{"key": "val"}'
skillflow-convert my_skill.md -o pipeline.yaml

PyPI publish

pip install build twine
python3 -m build
twine upload dist/*

Getting Started

Skillflow runs pipelines in two modes.

Framework Mode

Skillflow is embedded in a host application. The host drives the loop — skillflow handles traversal, tool execution, and state. The host only executes agent steps via StepRunner.

from skillflow import SkillFlow, PipelineGraph, StepResult

graph = PipelineGraph.from_yaml("tests/fixtures/minimal_1step.yaml")

sf = SkillFlow(":memory:")
sf.register_graph(graph)
sf.register_agent_config("echo_agent", model="host")

run_id = sf.create_run("minimal_1step")
sf.start_run(run_id)

while True:
    sf.advance_run(run_id)
    claimed = sf.claim_next_step(run_id)
    if claimed is None:
        break  # completed or paused
    # Host StepRunner executes the agent step here
    sf.confirm_step(claimed.token, StepResult(outputs={}, flags={}))

In framework mode, all tools auto-execute inline — skillflow runs native tools and custom tools without involving the host agent.

Config reference: tests/fixtures/minimal_1step.yaml.

Runner Mode

Runner mode is the language-agnostic interface. An LLM agent (or any program) drives a pipeline by calling skillflow-run as a CLI tool. Each invocation is a fresh process — only the SQLite DB persists state.

Pass --graph once with --action start. The graph path is stored in the DB. All subsequent calls use --run-id to reconnect — no --graph needed.

# 1. Start a pipeline — pass --graph once, get the first step back
$ skillflow-run --graph pipeline.yaml --action start
{"status": "in_progress", "run_id": "abc123", "step": "analyze", "instruction": "..."}

# 2. Submit work for the current step (no --graph needed)
$ skillflow-run --action submit --run-id abc123 \
    --result '{"issues": [{"file": "app.py", "severity": "high"}]}'
{"status": "in_progress", "run_id": "abc123", "step": "summarize", "instruction": "..."}

# 3. When a checkpoint step completes, the run pauses
{"status": "paused", "checkpoint_label": "Review Summary — approve to commit, reject to revise"}

# 3a. Human approves (no --graph needed)
$ skillflow-run --action approve --run-id abc123
{"status": "in_progress", "run_id": "abc123", "step": "apply_fixes", ...}

# 3b. Or human rejects with feedback
$ skillflow-run --action reject --run-id abc123 \
    --feedback "Severity of bare except should be high, not medium"

# 4. Loop continues until the pipeline completes
{"status": "completed", "steps_completed": 3, "outputs": {...}}

The agent loop in pseudocode:

resp = run("--graph", graph, "--action", "start")
while resp.status == "in_progress":
    # Steps with output.fixed require files written to output_dir before submit
    if resp.expected_files:
        for fname in resp.expected_files:
            write_file(resp.output_dir / fname, generate_content(resp))
    work = do_the_work(resp.instruction, resp.tools)
    resp = run("--action", "submit", "--run-id", resp.run_id,
               "--result", json(work))
    # If validation failed, resp.validation_error is set and resp.step repeats
    if resp.validation_error:
        fix_the_error(resp.validation_error)
        continue
    if resp.status == "paused":
        show_checkpoint_to_human(resp.checkpoint_label)
        if human_approves:
            resp = run("--action", "approve", "--run-id", resp.run_id)
        else:
            resp = run("--action", "reject", "--run-id", resp.run_id,
                       "--feedback", human_feedback)
# resp.status == "completed" — pipeline done

Response fields beyond status:

Field	When present	Meaning
output_dir	Steps with output.fixed	Write expected files here before calling submit
expected_files	Steps with output.fixed	File names to create (e.g. ["findings.json"])
validation_error	Submit rejected by validator	Why the previous submit failed — fix and re-submit
tools	Agent steps	Write helpers (write_*, create_*, append_*) with format specs

Runner mode delegates unknown tools to the agent (resp.tool_name / resp.tool_params). For programmatic use, the same API is available via SkillTool.

Node Types

Type	Description
agent	LLM step — host app executes via StepRunner protocol
tool	Auto-executed by skillflow (native), or delegated to agent in runner mode (custom)
gate	Auto-resolved using match conditions against step output flags
loop	Iterates over a JSON list from a workspace file, instantiating sub-steps per item

Transition Matching

Five match strategies. See tests/fixtures/dpe_full.yaml for a complete pipeline using all of them:

match: { field: "passed", value: true }                          # step output flags
match: { from_file: "review_verdict.json", field: "passed", value: true }  # output file
match: { from: "checkpoint", value: "approved" }                 # checkpoint routing
match: { _error: true }                                          # error handler
# (no match key)                                                 # always match

Context Injection

context:
  - source: { step: "1" }
  - source: { step: "2", mode: "interfaces" }
  - source: { config: "meta", output: "brief.md" }
  - source: { tool: "dir_tree" }

Checkpoints

Agent steps can pause for human approval (tests/fixtures/checkpoint_cycle.yaml):

sf.reject_checkpoint(run_id, "draft", "Add more detail to the analysis")

Output Validation

Steps declare validation specs auto-executed by skillflow. See tests/fixtures/skill_review.yaml for inline JSON Schema validation, or tests/fixtures/lifecycle_hooks.yaml for syntax_lint + py_compile validators.

Available validators: json_schema, syntax_lint, py_compile, pytest, file_exists.

Lifecycle Hooks

Steps with output.mode: "write" can trigger deliver and post-deliver hooks. See tests/fixtures/lifecycle_hooks.yaml:

lifecycle:
  on_deliver:
    tool: "repo_apply"
    params:
      source_dir: "$STEP_DIR"
    on_failure: "retry"
    max_retries: 2
  after_deliver:
    - tool: "syntax_lint"
      files: ["*.py"]

Error Handling

Steps declare max_retries and an _error transition. See tests/fixtures/error_handler.yaml.

Feedback Loopback

Tool failures can inject output into the next step's inputs (feedback: true). See plugins/skill_converter/skill_converter.yaml — the validate_design step feeds lint errors into fix_issues.

End Conditions

Four termination strategies, combined with and/or. See tests/fixtures/end_conditions.yaml and tests/fixtures/dpe_full.yaml:

end_conditions:
  combinator: or
  conditions:
    - type: node_reached
      node: "5_review"
      result: "completed"
    - type: max_total_steps
      limit: 200
    - type: max_run_duration_seconds
      limit: 3600
    - type: flag_match
      flag: { fatal_error: true }

Stale Claim Recovery

Built into advance_run. Claims older than stale_threshold_seconds (default 300) are auto-reset:

sf = SkillFlow("pipeline.db", stale_threshold_seconds=300)

Event Streaming

All state transitions are written to skillflow_outbox. Poll for real-time notifications:

events = sf.drain_outbox(batch_size=50)
for event in events:
    print(event.event_type, event.payload)
sf.ack_outbox([e.id for e in events])

In-process subscribers via NotificationBus:

from skillflow import NotificationBus

bus = NotificationBus()
bus.subscribe("step_completed", lambda n: print(n.payload))
sf = SkillFlow(":memory:", notification_bus=bus)

Tools

Native (13 built-in)

Tool	Description
read_file	Read a file with line numbers
write	Write content to workspace
list_tree	List directory structure
dir_tree	Context tree for prompt injection
json_schema	Validate JSON against inline schema
syntax_lint	Syntax check via ruff
py_compile	Python bytecode compile
pytest	Run pytest on test files
repo_apply	Copy files to repo + git commit
repo_validate	Multi-tool repo validation
draft_commit	Move draft files to final dir + commit
file_exists	Check files matching glob patterns
notify	Send user-visible notifications

Custom tools

Host apps add tool directories. Each tool: {name}/tool.yaml + {name}/impl.py. Function name must match directory name.

from skillflow.tool_loader import ToolLoader

loader = ToolLoader()
loader.add_tools_dir("my_app/tools")
sf = SkillFlow(":memory:", tool_loader=loader)

Use Cases

1. Framework mode — embed skillflow in your app

Use skillflow as a library. Read the Getting Started section above and the fixture examples in tests/fixtures/.

from skillflow import SkillFlow, PipelineGraph
graph = PipelineGraph.from_yaml("my_pipeline.yaml")
sf = SkillFlow(":memory:")
sf.register_graph(graph)
# ... drive the loop with claim_next_step / confirm_step

2. Agent mode — convert skills to pipelines

skillflow-convert is a thin wrapper that calls skillflow-run with the built-in converter pipeline. The agent drives it the same way:

# Start conversion with a skill description
$ skillflow-convert --desc "Code review skill..." --action start
{"status": "in_progress", "run_id": "abc123", "step": "analyze_skill", "instruction": "..."}

# Submit analysis, continue through design → lint → fix → done (no --desc needed)
$ skillflow-convert --action submit --run-id abc123 --result '{"analysis": {...}}'

On completion, the generated pipeline YAML is at ~/.skillflow/workspaces/skill-converter/.../skill_pipeline.yaml.

Agent manuals (the tool schema + rules) are shipped in the package:

Plugin	Manual	Load via
skill_runner	Actions, response format, rules	load_agent_guide() from skillflow.plugins.skill_runner
skill_converter	Step-by-step: analyze → design → lint → fix	load_agent_guide() from skillflow.plugins.skill_converter

Inject these into the agent's system prompt so it knows how to call the CLI tools.

skillflow-lint pipeline.yaml                             # one-shot config validation
skillflow-run --graph pipeline.yaml --action start        # start a pipeline (returns JSON, --graph only once)
skillflow-convert --desc "..." --action start             # start a conversion

Linter (skillflow.plugins.linter)

Framework utility. Validates pipeline YAML — used as a skillflow tool (skillflow_lint) inside the converter's feedback loop, or standalone:

skillflow-lint tests/fixtures/skill_review.yaml
skillflow-lint configs/*.yaml

Package

src/skillflow/
├── core.py              # SkillFlow orchestrator (create/claim/confirm/advance)
├── graph.py             # PipelineGraph, StepNode, Transition, GraphResolver
├── tool_loader.py       # Dynamic tool schema + implementation loading
├── context.py           # ContextResolver: cross-config, step, tool sources
├── step_validation.py   # StepValidator: multi-tool output validation
├── write_tools.py       # Constrained write tool generation from output.fixed
├── workspace.py         # Per-step atomic staging directories
├── validation.py        # Optional external-schema output validation
├── recovery.py          # Stale claim recovery
├── schema.py            # SQLite DDL + migrations
├── exceptions.py        # SkillFlowError hierarchy
├── outbox.py            # OutboxConsumer for event polling
├── notifications.py     # NotificationBus for in-process subscribers
├── agent_registry.py    # Agent config registry + schema resolution
├── plugins/             # Built-in plugins
│   ├── linter/          # Config validator + skillflow_lint tool
│   ├── skill_runner/    # SkillTool — interactive pipeline facade
│   └── skill_converter/ # Skill description → pipeline YAML
└── tools/               # Native tools (13)
    ├── read_file/       ├── write/          ├── list_tree/
    ├── dir_tree/        ├── json_schema/    ├── syntax_lint/
    ├── py_compile/      ├── pytest/         ├── repo_apply/
    ├── repo_validate/   ├── draft_commit/   ├── file_exists/
    └── notify/

Tests

pytest tests/ -v       # 306 tests
pytest plugins/ -v     # 21 plugin tests