What is fastworkflow-nlu-pipeline-reference?

A technical reference for the natural-language understanding pipeline in fastWorkflow, which turns user messages into commands and extracts their parameters.

Use it to inspect or change intent detection and parameter extraction, understand threshold values, and find where the relevant settings and matching logic are implemented.

It explains how the repository's intent classifiers, confidence thresholds, generated training examples, and matching layers work, reducing guesswork when these parts behave unexpectedly.

How radiantlogicinc/fastworkflow configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to radiantlogicinc/fastworkflow. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/radiantlogicinc/fastworkflow/main/.claude/skills/fastworkflow-nlu-pipeline-reference/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/radiantlogicinc/fastworkflow

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/radiantlogicinc/fastworkflow/fastworkflow-nlu-pipeline-reference/github.svg)](https://agentmods.dev/skills/radiantlogicinc/fastworkflow/fastworkflow-nlu-pipeline-reference)

Your own site

<a href="https://agentmods.dev/skills/radiantlogicinc/fastworkflow/fastworkflow-nlu-pipeline-reference"><img src="https://agentmods.dev/badge/skills/radiantlogicinc/fastworkflow/fastworkflow-nlu-pipeline-reference/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/radiantlogicinc/fastworkflow/fastworkflow-nlu-pipeline-reference"><img src="https://agentmods.dev/badge/skills/radiantlogicinc/fastworkflow/fastworkflow-nlu-pipeline-reference.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 300 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 9,413 The whole file, excluding the scripts and references it only reads on demand.

Security scan C 1 finding. A grade says what 26 rules found in the file — not that it is safe.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00300	$0.09413
Opus 5.5	$0.00120	$0.03765
Sonnet 5.5	$0.00060	$0.01883
Haiku 4.5	$0.00030	$0.00941

Measured 13d ago against content hash 4db2f40cc311, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade C, and why

fastworkflow-nlu-pipeline-reference scanned grade C with 1 finding 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 13d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/show_intent_thresholds.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

in-process, or `rm -rf ~/.dspy_cache/ ./.dspy_cache/` (dir verified via

.claude/skills/fastworkflow-nlu-pipeline-reference/SKILL.md · 509 lines

How it starts

The opening of the file, as written. The whole thing — 509 lines — stays where its author put it; the contents beside it link to each section on GitHub.

fastWorkflow NLU Pipeline Reference

Everything below is verified against source at v2.22.2 (commit c33b9a5), 2026-07-09, except the passages marked "2026-08-02", which were re-verified against the working tree after wave 1 of epic fix-551 (held-out evaluation, determinism, artifact versioning, and the reserved-label split) landed. File paths are repo-relative. Trust this document over CLAUDE.md and README where they disagree — known doc rot is listed at the end.

2026-08-02 — the biggest change since this skill was written. wildcard is no longer a catch-all. fastworkflow/nlu_labels.py splits it into two reserved labels with different meanings and different training rules; §2 has the detail. Anything you remember about "the wildcard class" is at best half right now.

fastworkflow/model_pipeline_training.py is under active edit as R1 is wired in, so the line numbers below drift. Prefer the symbol names; grep -n for them.

When to use / when NOT to use

You need...	Use
How intent detection / parameter extraction actually work here; what a threshold means; where a magic number lives	This skill
A symptom-to-fix triage table for a live failure	`fastworkflow-debugging-playbook`
Every env var with defaults and consumers	`fastworkflow-config-and-flags`
tau-bench / tau2-bench harness, pass^k, simulator mechanics	`fastworkflow-taubench-reference`
Why the architecture is shaped this way; invariants	`fastworkflow-architecture-contract`
Running train/build/CLI commands operationally	`fastworkflow-run-and-operate`
Measuring model quality instead of eyeballing	`fastworkflow-diagnostics-and-tooling`
Growing seeds/personas and judging whether run B beat run A	`fastworkflow-intent-training-convergence`
Variance/pass^k math, calibration analysis recipes	`fastworkflow-proof-and-analysis-toolkit`

Glossary (one line each, used throughout)

Term	Meaning here
fine-tuning	Continuing training of a pretrained model's weights on your small labeled dataset
logits	Raw per-class scores from the classifier head, before normalization
softmax confidence	`max(softmax(logits))` — the top class's probability; this repo's only "confidence" signal
calibration	How well confidence tracks actual correctness. This repo tunes decision thresholds on held-out data but never calibrates the probabilities themselves (no temperature scaling)
embedding	Fixed-length vector representing text; here the DistilBERT `[CLS]` token's last hidden state (`cache_matching.py:51`)
cosine similarity	Angle-based vector similarity in [~-1, 1]; 1.0 = same direction
Levenshtein distance	Minimum single-character edits between strings; "normalized" = divided by the longer length, so 0.0 = identical
LabelEncoder	sklearn utility mapping label strings ↔ integer class ids (the ONLY load-bearing sklearn use in intent detection)
weighted F1	Per-class harmonic mean of precision/recall, averaged weighted by class frequency
NDCG@3	Ranking score: 1.0 if the true label is ranked 1st, discounted by 1/log2(rank+1) if 2nd/3rd, 0 if absent from top-3
DSPy	Framework that compiles typed "Signatures" (input/output field specs) into LLM prompts via modules like `ChainOfThought`
ChainOfThought	DSPy module that makes the LLM emit reasoning before the output fields
LabeledFewShot	DSPy optimizer that stuffs labeled examples into the prompt (few-shot = examples-in-prompt)
litellm	Client library exposing one API over many LLM providers via `provider/model` strings
NOT_FOUND	String sentinel (env var `NOT_FOUND`, value `"NOT_FOUND"`) marking an unextracted parameter

Read the full file on GitHub · 509 lines

Files

What ships with it

4 files 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.

13d ago Changed 4db2f40cc311
1mo ago Changed · +3 lines c85cd16b423a
2mo ago First seen · 506 lines · 300 tokens per session scan C ca8e9cd0cd5f

Subscribe to this mod's changes

fastworkflow-nlu-pipeline-reference is a skill published in the GitHub repository radiantlogicinc/fastworkflow (52 stars, last pushed 13d ago), licensed Apache-2.0. It adds 300 tokens to every session and 9,413 once invoked, about $0.0012 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Data and AI

fastworkflow-nlu-pipeline-reference next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
fastworkflow-nlu-pipeline-referenceradiantlogicinc/fastworkflow	Skill	52	13d ago	Apache-2.0	C	300	Claude Code
agent-platform-model-registrygoogle/skills	Skill	22k	yesterday	Apache-2.0	A	60	Claude Code, Codex
foundry-config-setupmicrosoft/agent-framework	Skill	15k	yesterday	MIT	A	65	Claude Code, Codex
google-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle/skills	Skill	22k	yesterday	Apache-2.0	A	132	Claude Code, Codex
training-checkwanshuiyin/Auto-claude-code-research-in-sleep	Skill	19k	3d ago	MIT	A	35	Claude Code
nemo-automodel-launcher-configNVIDIA/skills	Skill	3.9k	yesterday	Apache-2.0	A	30	Claude Code
finetuningmicrosoft/skills	Skill	3.2k	yesterday	MIT	A	132	Claude Code

fastworkflow: Skill for Claude Code