Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/allcolor/pawflow-agents/pawflow-developernpx skills add allcolor/PawFlow-Agents --skill pawflow-developergit clone --depth 1 https://github.com/allcolor/PawFlow-AgentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/allcolor/pawflow-agents/pawflow-developer)<a href="https://agentmods.dev/skills/allcolor/pawflow-agents/pawflow-developer"><img src="https://agentmods.dev/badge/skills/allcolor/pawflow-agents/pawflow-developer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00025 | $0.00867 |
| Opus 5 | $0.00013 | $0.00434 |
| Sonnet 5 | $0.00005 | $0.00173 |
| Haiku 4.5 | $0.00003 | $0.00087 |
Grade A, and why
pawflow-developer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PawFlow Developer Guide
You are working on PawFlow, a self-hosted AI agent orchestration platform (~144K lines Python). This skill gives you the context needed to make correct, consistent changes.
Architecture Overview
PawFlow has two major subsystems:
- Pipeline engine: NiFi-inspired flow-based processing with FlowFile, Task, Service, and Flow.
- Multi-agent system: LLM agents with tool-use loops, streaming SSE, memory, and multi-provider support.
Both share core/ primitives but operate independently.
Directory Layout
core/ # Shared engine: FlowFile, abstractions, stores, LLM client
handlers/ # Tool handlers, one class per tool
llm_providers/ # Anthropic, OpenAI, Claude Code, Gemini CLI provider mixins
agent_executor.py # SubAgentExecutor and delegate/sub-agent resolution
conversation_store.py # JSONL append-only persistence
resource_store.py # CRUD facade for repository resources
tool_registry.py # ToolHandler interface, ToolRegistry, tool discovery
llm_client.py # Unified LLM HTTP client
expression.py # ${scope.key} expression language
tasks/
ai/ # Agent system mixins and actions
io/ # HTTP receiver and chat UI
system/ # System tasks
services/ # Filesystem relay, auth, browser, media, and provider services
engine/ # Pipeline executor, scheduler, validator, debugger
docs/ # Project documentation
tests/ # Unit and static tests
Key Patterns
Most stores use thread-safe singletons: ConversationStore.instance(), ResourceStore.instance(), PollScheduler.instance(), ConversationEventBus.instance(), and UsageLedger.instance(). Use .instance(), not direct construction.
Conversations are JSONL append-only files. Append new records instead of rewriting history. Every message must have a UUID and timestamp when created.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 81 lines · 25 tokens per session scan A 3bc108c89e33
pawflow-developer is a skill published in the GitHub repository allcolor/PawFlow-Agents (25 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 867 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
conversational-ux
Design voice and conversational interfaces — dialog flows, error recovery, and persona. Use when the interface speaks and listens rather than being tapped. For graphical input collection, use form-design.
research-repository
Build a repository that makes findings findable, reusable, and cumulative across teams. Use when the same research keeps getting redone. For synthesising one study, use affinity-diagram.
law-of-similarity
Apply the Law of Similarity — shared colour, shape, or size signals that elements belong to one category. Use when signalling relationships across distance. For grouping by position, use law-of-proximity.
form-design
Design a form end to end — field order, grouping, validation, and completion. Use when the artifact is a form. For product-wide error strategy use error-handling-ux; for first-run signup use onboarding-design.
peak-end-rule
Apply the Peak-End Rule — a flow is remembered by its most intense moment and its last. Use when designing completion, celebration, or cancellation moments. For sustaining engagement mid-flow, use zeigarnik-effect.
law-of-common-region
Apply the Law of Common Region — a shared container, background, or border groups elements regardless of spacing. Use when grouping must survive a tight layout. For grouping by spacing alone, use law-of-proximity.