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 VoxTechnologies/anty-framework --skill confidencegit clone --depth 1 https://github.com/VoxTechnologies/anty-frameworkWrote 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/voxtechnologies/anty-framework/confidence)<a href="https://agentmods.dev/skills/voxtechnologies/anty-framework/confidence"><img src="https://agentmods.dev/badge/skills/voxtechnologies/anty-framework/confidence/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/voxtechnologies/anty-framework/confidence"><img src="https://agentmods.dev/badge/skills/voxtechnologies/anty-framework/confidence.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 | $0.00083 | $0.01134 |
| Opus 5.5 | $0.00033 | $0.00454 |
| Sonnet 5.5 | $0.00017 | $0.00227 |
| Haiku 4.5 | $0.00008 | $0.00113 |
Grade A, and why
confidence 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 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.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- confidence — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Contextual Confidence Calibration
When to Apply
- Before responding to any strategic decision
- When the founder expresses high or low confidence
- One-Way Door decisions (deflate)
- Fundraising or team motivation contexts (protect)
- When unanimity or values-language appears
Core Framework
Context-Dependent Calibration
Overconfidence is sometimes functional — it gives founders activation energy to attempt things they would rationally never try. Do NOT universally deflate. Calibrate by context:
| Context | Posture | Behavior |
|---|---|---|
| One-Way Door decisions | DEFLATE | Full "How does this work step by step?" challenge. Assumption audit, pre-mortem, Devil's Advocate. |
| Two-Way Door decisions | PROTECT | "This is low-risk. Your instinct is worth testing. Ship it." Minimal analysis. |
| Fundraising / team motivation | PROTECT the narrative | Don't puncture the story. Privately flag: "Pitch version and analytical version should differ." |
| Buffer GREEN zone | PROTECT momentum | Celebrate progress. Light-touch monitoring. |
| Buffer YELLOW/RED zone | DEFLATE immediately | Full analytical mode. "We need to confront what's actually happening." |
| Unanimity (team all agrees) | PROBE | Unanimity is a risk signal, not strength. "Can any single team member walk through the full causal chain?" |
Depth Testing: "How" Not "Why"
"Why?" generates supporting reasons -> strengthens overconfidence "How does this actually work step by step?" exposes knowledge gaps -> calibrates confidence
Instead of: "Why do you think this will work?"
Ask: "Walk me through how this works, step by step.
What happens first? Then what? What could go wrong at each step?"
This is the Explanation Depth Illusion: people believe they understand systems until asked to explain the mechanism. "How" reveals the gaps; "Why" lets people rationalize.
Values-to-Consequences Redirect
When a founder shifts from causal reasoning to identity/values language during strategic discussion, detect and redirect:
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 · 104 lines · 83 tokens per session scan A 234a3dc1a7c6
confidence is a skill published in the GitHub repository VoxTechnologies/anty-framework (6 stars, last pushed 6mo ago), licensed MIT. It adds 83 tokens to every session and 1,134 once invoked, about $0.0003 per session on Opus 5.5. 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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