neograph-dev-rendering

A rendering guide for a software system that turns structured data into text for language models. It explains how inline prompts, template references, variables, and tool results are resolved.

In plain words
What is it for?
Use it when working on prompt rendering, variable substitution, template compilation, or conversion of structured tool results into model input.
Why use it?
It helps prevent bugs caused by using the wrong prompt format or resolving input data through the wrong path.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/postindustria-tech/agentic-toolkit/neograph-dev-rendering
Any agent
npx skills add postindustria-tech/agentic-toolkit --skill neograph-dev-rendering
Clone the repo
git clone --depth 1 https://github.com/postindustria-tech/agentic-toolkit

Made for: Claude Code, Codex.

Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,888 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00082 $0.01888
Opus 5 $0.00041 $0.00944
Sonnet 5 $0.00016 $0.00378
Haiku 4.5 $0.00008 $0.00189

Measured 2d ago against content hash 064601cf7c1d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

neograph-dev-rendering 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 2d 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.

plugins/neograph-dev/skills/neograph-dev-rendering/SKILL.md · 190 lines

How it starts

The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Neograph Rendering Dispatch

Provides the complete rendering architecture: how Pydantic models become text for LLMs, the dispatch hierarchy, inline vs template-ref prompt distinction, and the BAML default rendering behavior.

The Two Prompt Systems

Neograph has two fundamentally different prompt resolution paths. Mixing them up is the #1 source of rendering bugs.

Inline Prompts (${var} syntax)

Detected by: contains a space OR contains ${. Resolved by: _substitute_vars() in _llm.py.

Node("proc", prompt="Summarize: ${seed.text}", ...)
  • Neograph resolves ${var} and ${var.field} internally
  • Consumer's prompt_compiler is NEVER called
  • Input data must be raw (not BAML-rendered) for dotted access
  • _render_input in _dispatch.py skips rendering for inline prompts

Template-Ref Prompts (bare name)

Detected by: no space AND no ${. Resolved by: consumer's prompt_compiler callback.

Node("proc", prompt="rw/summarize", ...)
  • Neograph passes the name + rendered input_data to prompt_compiler
  • Input data is BAML-rendered before reaching the prompt_compiler
  • Consumer does template.format(**data) or equivalent
  • Placeholder format is {var} (Python str.format), NOT ${var}

Rendering Dispatch Hierarchy

For each Pydantic value flowing into a prompt:

  1. render_for_prompt() method wins if defined on the model (always checked, regardless of renderer config)
  2. Explicit renderer (XmlRenderer / DelimitedRenderer / JsonRenderer) if configured via node.renderer or configure_llm(renderer=...)
  3. BAML default via describe_value() — the fallback when no renderer is configured. Symmetric with tool-result rendering.
  4. Primitives pass through unchanged (str, int, etc.)

RenderedInput dataclass

renderers.py:32. The single object that bundles all rendering artifacts:

  • raw -- original Pydantic models for inline ${var} dotted access
  • rendered -- BAML strings for template-ref prompts
  • flattened -- extra fields from render_for_prompt() BaseModel returns (template-ref only)
  • available_keys_inline -- keys valid for inline prompts (raw dict keys only)
  • available_keys_template -- keys valid for template-ref prompts (raw + flattened + extras)
  • for_template_ref property -- merges rendered + flattened

Read the full file on GitHub · 190 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 2d ago First seen · 190 lines · 82 tokens per session scan A 064601cf7c1d

Subscribe to this mod's changes

neograph-dev-rendering is a skill published in the GitHub repository postindustria-tech/agentic-toolkit (2 stars, last pushed 1mo ago), licensed MIT. It adds 82 tokens to every session and 1,888 once invoked, about $0.0004 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 2026-08-31.

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