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.
git clone --depth 1 https://github.com/Owl-Listener/ai-design-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/commands/owl-listener/ai-design-skills/calibrate-tone)<a href="https://agentmods.dev/commands/owl-listener/ai-design-skills/calibrate-tone"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/calibrate-tone/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/commands/owl-listener/ai-design-skills/calibrate-tone"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/calibrate-tone.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 | $0.00011 | $0.00423 |
| Opus 5 | $0.00005 | $0.00211 |
| Sonnet 5 | $0.00002 | $0.00085 |
| Haiku 4.5 | $0.00001 | $0.00042 |
Grade A, and why
calibrate-tone 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.
What it actually says
You are developing a tone calibration system. Use only skills from the system-behavior-shaping plugin. Follow this process:
Step 1: Define Tone Dimensions
Using tone-calibration:
- List all relevant tone dimensions for this product
- Define the scale for each dimension (e.g., formality: 1-5)
- Provide anchor examples at each end of each scale
Step 2: Map Contexts
Identify all contexts where the AI operates:
- Task types (creative, analytical, administrative, learning)
- User states (onboarding, deep work, troubleshooting, returning)
- Emotional states (calm, frustrated, excited, anxious)
- Content sensitivity levels (casual, professional, sensitive, critical)
Step 3: Build the Tone Matrix
Using tone-calibration:
- For each context, set values across all tone dimensions
- Identify conflicts (where contexts overlap with different needs)
- Resolve conflicts with priority rules
Step 4: Design Tone Transitions
Using tone-calibration and behavioral-consistency:
- Define how tone shifts between contexts
- Specify transition pacing (gradual vs. immediate)
- Identify jarring transitions to avoid
- Design bridging language for necessary sharp shifts
Step 5: Cultural Overlay
Using cultural-adaptation:
- Identify cultural variations that affect tone settings
- Define how the tone matrix adapts across cultural contexts
- Specify user controls for cultural tone preferences
Step 6: Test with Scenarios
Write 10 test scenarios spanning different contexts and verify the tone matrix produces appropriate behavior for each.
Output
Deliver a complete tone calibration system:
- Tone dimension definitions with scales and anchors
- Context inventory
- Full tone matrix (contexts × dimensions)
- Transition rules and bridging language
- Cultural adaptation layer
- 10 test scenarios with expected tone outputs
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 · 44 lines · 11 tokens per session scan A 8ff1f5354835
calibrate-tone is a command published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 11 tokens to every session and 423 once invoked, about $0.0001 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 2026-08-30.
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