Borrowing it
Nothing to install: this file belongs to safety-quotient-lab/psychology-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/safety-quotient-lab/psychology-agent/main/.claude/commands/adjudicate.mdgit clone --depth 1 https://github.com/safety-quotient-lab/psychology-agentWrote 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/safety-quotient-lab/psychology-agent/adjudicate)<a href="https://agentmods.dev/commands/safety-quotient-lab/psychology-agent/adjudicate"><img src="https://agentmods.dev/badge/commands/safety-quotient-lab/psychology-agent/adjudicate/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/safety-quotient-lab/psychology-agent/adjudicate"><img src="https://agentmods.dev/badge/commands/safety-quotient-lab/psychology-agent/adjudicate.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 | $0.00000 | $0.01959 |
| Opus 5 | $0.00000 | $0.00979 |
| Sonnet 5 | $0.00000 | $0.00392 |
| Haiku 4.5 | $0.00000 | $0.00196 |
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 11d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adjudicate — Structured Decision Resolution
Resolve ambiguous decisions with 2+ options through iterative knock-on analysis, multi-axis comparison, and consensus-or-parsimony resolution.
Adjudication is the full decision-making unit: it composes /knock (single-
option effect tracing) with iterative learning (2-pass), structured comparison
(differentiating axes), and binding resolution (consensus or parsimony).
/knock = trace effects of ONE option/change through 10 orders
/adjudicate = 2-pass /knock on EACH option → compare → resolve
Arguments
Parse $ARGUMENTS to determine scope:
| Argument | Behavior |
|---|---|
| (empty) | Identify the decision from context, adjudicate |
[topic] |
Adjudicate the named decision |
inline |
Abbreviated: 4-order, no 2-pass, for S decisions |
full |
Full protocol: 10-order, 2-pass, for M/L decisions |
If scale is not specified, infer from context:
- XS/S decisions →
inline(4-order knock-on + parsimony + structural scan) - M/L decisions →
full(10-order, 2-pass, comparison, consensus/parsimony)
Protocol
Phase 1: Frame the Decision
- State the decision clearly in one sentence
- Identify 2-3 mutually exclusive options (never more than 4)
- For each option, state it as a concrete action, not a vague direction
- Classify: is this XS, S, M, or L effort/impact?
Phase 2: Knock-on Analysis (2-pass for M/L)
For each option, trace effects through 10 orders:
Order Confidence What it captures
─────────────────────────────────────────────────────────────────
1 certain Direct, immediate effect
2 certain–likely What systems/processes activate
3 likely What consumes Order 2's outputs
4 likely–possible Aggregate/systemic effects
5 possible What humans observe / trust changes
6 speculative How it compounds over time
7 structural Ecosystem/precedent effects —
how does this shape what others do?
What precedent for open-source,
community, or standards?
8 horizon Normative/structural effects —
what norms, expectations, or
constraints does this establish
or erode long-term?
9 emergent Properties arising from interaction
of multiple knock-on chains — not
predictable from individual orders
in isolation (INCOSE)
10 theory-revising Effects that falsify or require
modification of the theory that
justified the original decision
(Popper)
─────────────────────────────────────────────────────────────────
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.
- 11d ago First seen · 237 lines · 0 tokens per session scan A 2cded874b405
adjudicate is a command published in the GitHub repository safety-quotient-lab/psychology-agent (20 stars, last pushed 4mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,959 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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