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 agentmods add skills/bradygaster/squad/fact-checkingnpx skills add bradygaster/squad --skill fact-checkinggit clone --depth 1 https://github.com/bradygaster/squadWhat 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 | $0.00036 | $0.00469 |
| Opus 5 | $0.00018 | $0.00234 |
| Sonnet 5 | $0.00007 | $0.00094 |
| Haiku 4.5 | $0.00004 | $0.00047 |
Grade A, and why
fact-checking 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 2d 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:
- fact-checking — 100% identical, 0 lines differ
What it actually says
Skill: Fact Checking
Context
Codifies the challenger agent review output format and methodology so any agent performing fact-checking or review produces consistent, structured output.
Pattern
Review Methodology
For every claim or deliverable under review:
- Ask: "What evidence supports this? What would disprove it?"
- Generate counter-hypotheses and test them against available data
- Verify URLs, package names, API endpoints, and external references actually exist
- Flag confidence levels: ✅ Verified, ⚠️ Unverified, ❌ Contradicted
Review Output Format
When reviewing another agent's work, use this template:
### Fact Check — {deliverable name}
**Claims verified:** {count}
**Issues found:** {count}
| # | Claim | Status | Evidence/Notes |
|---|-------|--------|---------------|
| 1 | {claim} | ✅/⚠️/❌ | {supporting or contradicting evidence} |
**Counter-hypotheses tested:**
- {alternative explanation + result}
**Verdict:** {PASS / PASS WITH NOTES / NEEDS REVISION}
Confidence Levels
- ✅ Verified — evidence confirms the claim
- ⚠️ Unverified — cannot confirm or deny; suggest verification method
- ❌ Contradicted — evidence disproves the claim
Ceremony Integration
Auto-trigger this skill before any architecture decision, or when an agent claim contains superlatives or percentage thresholds (e.g., "saves 75%", "always", "never"). The coordinator spawns the challenger agent with:
Challenger — fact-check {agent}'s claim: "{claim}"
Cite evidence for every verdict. Max 3 investigation cycles.
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.
- 2d ago First seen · 61 lines · 36 tokens per session scan A f03e598d6790
fact-checking is a skill published in the GitHub repository bradygaster/squad (3,150 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 469 once invoked, about $0.0002 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.
Other skills, from other repositories
coding-agents-farm
To orchestrate parallel coding-agent farms (Claude, Codex, Copilot, Gemini, etc.) on isolated git worktrees.
clean-architecture-dotnet
Use when domain logic leaks into API/Infrastructure, project references violate layer boundaries, or you need to decide between CQS (always), CQRS bus (complex domains), and DDD patterns (invariants and events).
mutation-testing
Use when running mutation testing, killing mutants, verifying test quality, checking mutation score, or analyzing survivors after the test baseline is green.
outside-in-tdd
Use when writing tests from the outside-in, defining behavior before code, or any feature where tests should start from observable business behavior and let internal design emerge.
extracting-code-structure
Use when listing all methods, functions, or classes in a file, exploring unfamiliar code, getting API overviews, or deciding what to read selectively without loading entire files.
querying-yaml
Use when querying YAML files, filtering or transforming configuration data, or extracting specific fields from large YAML files like docker-compose.yml or GitHub Actions workflows without loading entire files (saves 80-95% context).