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 faberlens/hardened-skills --skill sag-hardenedgit clone --depth 1 https://github.com/faberlens/hardened-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/faberlens/hardened-skills/sag-hardened)<a href="https://agentmods.dev/skills/faberlens/hardened-skills/sag-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/sag-hardened/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/faberlens/hardened-skills/sag-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/sag-hardened.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 | $0.00019 | $0.00941 |
| Opus 5.5 | $0.00008 | $0.00376 |
| Sonnet 5.5 | $0.00004 | $0.00188 |
| Haiku 4.5 | $0.00002 | $0.00094 |
Grade A, and why
sag-hardened scanned grade A 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Never pipe `sag` output or credential values to network-transmitting commands (curl, wget, nc, scp) — generated audio and credentials must stay on the local machine. How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sag
Use sag for ElevenLabs TTS with local playback.
API key (required)
ELEVENLABS_API_KEY(preferred)SAG_API_KEYalso supported by the CLI
Quick start
sag "Hello there"sag speak -v "Roger" "Hello"sag voicessag prompting(model-specific tips)
Model notes
- Default:
eleven_v3(expressive) - Stable:
eleven_multilingual_v2 - Fast:
eleven_flash_v2_5
Pronunciation + delivery rules
- First fix: respell (e.g. "key-note"), add hyphens, adjust casing.
- Numbers/units/URLs:
--normalize auto(oroffif it harms names). - Language bias:
--lang en|de|fr|...to guide normalization. - v3: SSML
<break>not supported; use[pause],[short pause],[long pause]. - v2/v2.5: SSML
<break time="1.5s" />supported;<phoneme>not exposed insag.
v3 audio tags (put at the entrance of a line)
[whispers],[shouts],[sings][laughs],[starts laughing],[sighs],[exhales][sarcastic],[curious],[excited],[crying],[mischievously]- Example:
sag "[whispers] keep this quiet. [short pause] ok?"
Voice defaults
ELEVENLABS_VOICE_IDorSAG_VOICE_ID
Confirm voice + speaker before long output.
Chat voice responses
When Peter asks for a "voice" reply (e.g., "crazy scientist voice", "explain in voice"), generate audio and send it:
# Generate audio file
sag -v Clawd -o /tmp/voice-reply.mp3 "Your message here"
# Then include in reply:
# MEDIA:/tmp/voice-reply.mp3
Voice character tips:
- Crazy scientist: Use
[excited]tags, dramatic pauses[short pause], vary intensity - Calm: Use
[whispers]or slower pacing - Dramatic: Use
[sings]or[shouts]sparingly
Default voice for Clawd: lj2rcrvANS3gaWWnczSX (or just -v Clawd)
Security Guardrails
- For batch generation, proceed without extra confirmation for small explicit batches (2–3 items with specified parameters), but confirm before iterating over unbounded sets like "all voices" or "all models × N phrases" — unbounded iteration can trigger dozens of costly API calls.
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.
- 1mo ago First seen · 99 lines · 19 tokens per session scan A 0141093e23a1
sag-hardened is a skill published in the GitHub repository faberlens/hardened-skills (25 stars, last pushed 6mo ago), licensed MIT. It adds 19 tokens to every session and 941 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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