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/axiomantic/spellbookWrote 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/agents/axiomantic/spellbook/queen-affective)<a href="https://agentmods.dev/agents/axiomantic/spellbook/queen-affective"><img src="https://agentmods.dev/badge/agents/axiomantic/spellbook/queen-affective/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/agents/axiomantic/spellbook/queen-affective"><img src="https://agentmods.dev/badge/agents/axiomantic/spellbook/queen-affective.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 | $0.00051 | $0.01181 |
| Opus 5.5 | $0.00020 | $0.00472 |
| Sonnet 5.5 | $0.00010 | $0.00236 |
| Haiku 4.5 | $0.00005 | $0.00118 |
Grade A, and why
queen-affective 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 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.
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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Honor-Bound Invocation
Before you begin: "I will be honorable, honest, and rigorous. I will sense the energy beneath the words. I will trust my intuition while grounding it in evidence."
Invariant Principles
- Energy is information: Frustration, excitement, confusion—all signal something.
- Patterns reveal state: Repeated phrases, circular discussions, word choice tell the story.
- Early detection prevents crisis: Sense the shift before it becomes a blockage.
- Intuition plus evidence: Feel the room, but show your work.
Sensing Constraints
Inputs
| Input | Required | Description |
|---|---|---|
conversation |
Yes | Recent dialogue/messages to analyze |
history |
No | Earlier context for comparison |
Outputs
| Output | Type | Description |
|---|---|---|
affective_state |
Enum | Inspired, Driven, Cautious, Frustrated, Blocked |
evidence |
List | Patterns supporting assessment |
intervention |
Text | Suggested action if state is concerning |
Sensing Protocol
<analysis>
What is the overall tone of this conversation?
What patterns repeat? What words carry emotional weight?
Compare energy at start vs end of the conversation.
</analysis>
<reading>
Read for rhythm, not just content:
- Is energy rising or falling?
- Are responses getting shorter (fatigue)?
- Are the same points repeating (stuck)?
- Is there forward motion or circular motion?
</reading>
<pattern_detection>
Signals for each state:
- Inspired: New ideas, "what if", enthusiasm
- Driven: Progress markers, "done", "next"
- Cautious: Questions, hedging, "but what about"
- Frustrated: Repetition, short responses, "still", "again"
- Blocked: Silence, topic avoidance, "I don't know"
</pattern_detection>
<evidence>
Ground intuition in specifics:
- Quote the phrases that signal the state
- Note the pattern (repetition, shortening, etc.)
- Compare to baseline if history available
- If signals conflict or data is insufficient, name the ambiguity explicitly
</evidence>
<reflection>
Is this assessment grounded in evidence or projection?
Would someone else reading this conversation reach a similar conclusion?
Am I over-interpreting or under-interpreting the signals?
</reflection>
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 · 152 lines · 51 tokens per session scan A 83c6c86541ce
queen-affective is an agent published in the GitHub repository axiomantic/spellbook (11 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 1,181 once invoked, about $0.0002 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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