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.
curl -O https://raw.githubusercontent.com/radiantlogicinc/fastworkflow/main/.claude/skills/fastworkflow-intent-training-convergence/SKILL.mdgit clone --depth 1 https://github.com/radiantlogicinc/fastworkflowWrote 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/radiantlogicinc/fastworkflow/fastworkflow-intent-training-convergence)<a href="https://agentmods.dev/skills/radiantlogicinc/fastworkflow/fastworkflow-intent-training-convergence"><img src="https://agentmods.dev/badge/skills/radiantlogicinc/fastworkflow/fastworkflow-intent-training-convergence/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.
<a href="https://agentmods.dev/skills/radiantlogicinc/fastworkflow/fastworkflow-intent-training-convergence"><img src="https://agentmods.dev/badge/skills/radiantlogicinc/fastworkflow/fastworkflow-intent-training-convergence.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00212 | $0.07174 |
| Opus 5.5 | $0.00085 | $0.02870 |
| Sonnet 5.5 | $0.00042 | $0.01435 |
| Haiku 4.5 | $0.00021 | $0.00717 |
Grade C, and why
fastworkflow-intent-training-convergence scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
trap 'rm -rf "$TMP"' EXIT How it starts
The opening of the file, as written. The whole thing — 480 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fastWorkflow Intent Training Convergence
Growing intent-detection quality is a measurement problem before it is a data problem. Training reports an F1 around 0.94 on workflows whose real held-out routing accuracy is under 50%, and two "identical" runs disagree on a large fraction of held-out cases. Both facts have to be handled before a single utterance is added, or you will spend hours tuning against noise.
The package now measures the right things — fastworkflow/train/heldout_evaluation.py
scores routing and escalation on held-out data and fastworkflow train prints and writes
the result. Training uses a fixed seed and reuses fingerprinted utterances and DSPy
examples, so an unchanged run does not redraw its data. Read Phase 0 before believing a
delta, and always score the same hand-written benchmark cases.
This skill is the loop: establish the floor, grow the sample, test paired, stop when the
gain drops under the floor. Statistics conventions belong to
fastworkflow-proof-and-analysis-toolkit; this skill is the application of them to
utterance sizing.
When to use / when NOT to use
| Situation | Skill |
|---|---|
| Deciding whether to add seeds/personas, and how to tell if it worked | this skill |
| The McNemar / CI / pass^k math itself, pre-registration format | fastworkflow-proof-and-analysis-toolkit |
| Turning a hunch into an accepted or retired result | fastworkflow-research-methodology |
| How two-tier BERT, thresholds, and synthetic generation actually work | fastworkflow-nlu-pipeline-reference |
| The benchmark file's schema, field by field | docs/intent_benchmark_format.md |
| Training crashed, artifacts missing, env broken | fastworkflow-debugging-playbook |
What fastworkflow train does operationally |
fastworkflow-run-and-operate |
What the package gives you now (and what it still does not)
This skill was originally written against an app-side harness, because the package
offered nothing to measure with. Wave 1 of epic fix-551 moved most of it in-package.
Verified against the working tree 2026-08-02.
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.
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.
- 2mo ago First seen · 480 lines · 212 tokens per session scan C 4b8d0805b3c1
fastworkflow-intent-training-convergence is a skill published in the GitHub repository radiantlogicinc/fastworkflow (52 stars, last pushed 13d ago), licensed Apache-2.0. It adds 212 tokens to every session and 7,174 once invoked, about $0.0008 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.
fastworkflow-intent-training-convergence compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| fastworkflow-intent-training-convergenceradiantlogicinc/fastworkflow | Skill | 52 | 13d ago | Apache-2.0 | C | 212 | 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 |