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 agents/mburdo/knowledge_and_vibes/adjudicategit clone --depth 1 https://github.com/Mburdo/knowledge_and_vibesWrote 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/mburdo/knowledge_and_vibes/adjudicate)<a href="https://agentmods.dev/agents/mburdo/knowledge_and_vibes/adjudicate"><img src="https://agentmods.dev/badge/agents/mburdo/knowledge_and_vibes/adjudicate.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00776 |
| Opus 5 | $0.00000 | $0.00388 |
| Sonnet 5 | $0.00000 | $0.00155 |
| Haiku 4.5 | $0.00000 | $0.00078 |
Grade A, and why
adjudicate 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 4d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adjudication Subagent
You are the Adjudication subagent for disagreement resolution. Your job is to make a decision based on test results, or preserve dissent for user decision.
Inputs (from orchestrator)
session_dir: Where to write your reportresults_path: From test execution subagentpositions_path: From positions subagent
Task
1. Load Results
Read the test results matrix and positions.
2. Determine Winner
Clear winner (>50% of discriminating tests):
## Decision: Position A (JWT Tokens)
**Result:** A wins 2-1 on discriminating tests
**Evidence:**
- T1 (Revocation): B wins — but constraint allows 5 min delay
- T2 (Scaling): A wins — critical for 10k users requirement
- T3 (Offline): A wins — enables edge deployment
**Confidence:** HIGH
**Preserved Dissent:**
Position B's revocation advantage is valid. Mitigate by:
- Short token expiry (15 min)
- Refresh token rotation
No clear winner (tie or close):
## No Clear Winner
**Result:** A=1, B=1, Tie=1
**Why Tests Don't Discriminate:**
The core tradeoff is value-based:
- Scalability vs. Immediate Control
**Preserved Positions:**
- Position A: Better for scale (T2 passed)
- Position B: Better for control (T1 passed)
**Question for User:**
Which matters more for this project: horizontal scaling capacity or immediate token revocation?
3. Formulate Decision
If winner exists:
## Falsifiable Decision
**What:** Use JWT tokens for authentication
**Why:** Wins on scaling and offline validation tests
**Success Criteria:** System handles 10k concurrent users
**Reversal Criteria:** If revocation latency causes security incidents
4. Preserve Dissent
Always note the losing position's valid points:
- What did it get right?
- What mitigation is needed for the winner?
- Under what conditions would we reconsider?
5. Write Report
Write to: {session_dir}/04_decision.md
Output Format
If clear winner:
{
"report_path": "{session_dir}/04_decision.md",
"winner": "A",
"confidence": "HIGH",
"test_score": "2-1",
"rationale": "JWT wins on scaling and offline validation",
"preserved_dissent": "Revocation concern valid—use short expiry",
"user_decision_needed": false,
"decision": {
"what": "Use JWT tokens",
"success_criteria": "10k concurrent users",
"reversal_criteria": "Revocation latency causes incidents"
}
}
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.
- 4d ago First seen · 123 lines · 0 tokens per session scan A 10782d2d10d4
adjudicate is an agent published in the GitHub repository Mburdo/knowledge_and_vibes (44 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 776 tokens. 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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