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/commands/axiomantic/spellbook/fact-check-report)<a href="https://agentmods.dev/commands/axiomantic/spellbook/fact-check-report"><img src="https://agentmods.dev/badge/commands/axiomantic/spellbook/fact-check-report/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/axiomantic/spellbook/fact-check-report"><img src="https://agentmods.dev/badge/commands/axiomantic/spellbook/fact-check-report.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 | $0.00015 | $0.00424 |
| Opus 5 | $0.00008 | $0.00212 |
| Sonnet 5 | $0.00003 | $0.00085 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
fact-check-report 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 5d 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
Fact-Check: Report and Learning (Phases 6-7)
Invariant Principles
- Traceability through bibliography - Every finding must link to its verification evidence with proper citation format
- Actionable over comprehensive - Report prioritizes findings that require code changes over informational items
- Learning feeds forward - Verified facts and patterns are persisted for future sessions, not discarded after reporting
Phase 6: Report
Sections: Header, Summary, Findings by Category, Bibliography, Implementation Plan
Bibliography Formats:
| Type | Format |
|---|---|
| Code trace | file:lines - finding |
| Test | command - result |
| Web source | Title - URL - "excerpt" |
| Git history | commit/issue - finding |
| Documentation | Docs: source section - URL |
| Benchmark | Benchmark: method - results |
| Paper/RFC | Citation - section - URL |
Phase 6.5: Clarity Mode (if enabled)
Generate glossaries/key facts from verified claims (confidence > 0.7).
Targets: CLAUDE.md, GEMINI.md, AGENTS.md, *_AGENT.md, *_AI.md
Glossary Entry: - **[Term]**: [1-2 sentence definition]. [Usage context.]
Key Fact Categories: Architecture, Behavior, Integration, Error Handling, Performance
Update existing sections or append before --- separators.
Phase 7: Learning
Store trajectories in ReasoningBank:
await reasoningBank.insertPattern({
type: 'verification-trajectory',
domain: 'fact-checking-learning',
pattern: { claimText, claimType, depthUsed, verdict, timeSpent, evidenceQuality }
});
Applications: depth prediction, strategy selection, ordering optimization, false positive reduction.
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.
- 5d ago First seen · 52 lines · 15 tokens per session scan A f40e25dabf91
fact-check-report is a command published in the GitHub repository axiomantic/spellbook (10 stars, last pushed yesterday), licensed MIT. It adds 15 tokens to every session and 424 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 2026-09-03.
Other commands, from other repositories
review-branch
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dream
Overnight autoresearch + maintainer sweep that surfaces a MORNING REPORT — suggested changes, issues raised with fixes, and kept improvements. Shadow-first; never auto-pushes. Runs on-demand or scheduled for off-hours.
code-quality-plan-creator
LSP-powered architectural code quality analysis - works with any executor (loop or swarm).
mr-description-creator
Generate and apply MR/PR description directly via gh or glab CLI (project).
follow-up
Review and act on pending reminders. Bare invocation shows all reminders as an indexed list; cron-fired invocation (/follow-up due ) surfaces the specific reminder and waits for a response; /follow-up review lists stale project follow-up entries for per-item disposition (extend/close/promote/skip). Transport-aware…
pr-review
Generate a PR review report aggregating quality scan, coverage, complexity, and breaking changes.