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 skills add RasaHQ/rasa-agent-skills --skill rasa-rephrasing-responsesgit clone --depth 1 https://github.com/RasaHQ/rasa-agent-skillsWrote 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/rasahq/rasa-agent-skills/rasa-rephrasing-responses)<a href="https://agentmods.dev/skills/rasahq/rasa-agent-skills/rasa-rephrasing-responses"><img src="https://agentmods.dev/badge/skills/rasahq/rasa-agent-skills/rasa-rephrasing-responses/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/rasahq/rasa-agent-skills/rasa-rephrasing-responses"><img src="https://agentmods.dev/badge/skills/rasahq/rasa-agent-skills/rasa-rephrasing-responses.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 | $0.00057 | $0.00949 |
| Opus 5 | $0.00028 | $0.00475 |
| Sonnet 5 | $0.00011 | $0.00190 |
| Haiku 4.5 | $0.00006 | $0.00095 |
Grade A, and why
rasa-rephrasing-responses 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 11d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rephrasing Responses in Rasa
The Contextual Response Rephraser uses an LLM to dynamically rewrite templated responses, making them sound more natural and context-aware while preserving the original meaning. It reads conversation history and user input to ensure rephrasings fit the context.
Workflow
- Enable the rephraser in
endpoints.yml(see "Endpoints configuration"). - Decide the rephrasing scope — per-response or all responses (see "Rephrasing scope").
- Add
metadata: rephrase: True/Falseon individual responses as needed (see "Domain-side metadata"). - Optionally customize the prompt globally or per response (see "Prompt customization").
Endpoints configuration
Enable the rephraser by adding nlg: type: rephrase to endpoints.yml.
Configure the LLM model and temperature. Lower temperature (default 0.3) produces more predictable rephrasings; higher temperature produces more variable output but risks altering meaning.
nlg:
type: rephrase
llm:
model_group: my_llm # optional, defaults to the default model
model_groups:
- id: my_llm
models:
- provider: <your-provider> # e.g. openai, azure, self-hosted
model: <your-llm-model>
temperature: 0.3 # 0.0–2.0, default 0.3
Conversation history
Two modes for how conversation history is included in the rephrasing prompt.
Summary mode (default) — summarizes history using an additional LLM call. No extra config needed.
Transcript mode — keeps the last n turns as-is. Set summarize_history: False and
optionally adjust max_historical_turns (default 5).
nlg:
type: rephrase
summarize_history: False
max_historical_turns: 5
Rephrasing scope
Specific responses only (default)
No endpoints.yml change needed. Add metadata: rephrase: True on each response you
want rephrased.
responses:
utter_greet:
- text: "Hey! How can I help you?"
metadata:
rephrase: True
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.
- 11d ago First seen · 140 lines · 57 tokens per session scan A 60c3cb393d3d
rasa-rephrasing-responses is a skill published in the GitHub repository RasaHQ/rasa-agent-skills (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 57 tokens to every session and 949 once invoked, about $0.0003 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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