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/catwillgh/mainframe/decision-reviewnpx skills add CATWILLgh/MAINFRAME --skill decision-reviewgit clone --depth 1 https://github.com/CATWILLgh/MAINFRAMEWrote 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/skills/catwillgh/mainframe/decision-review)<a href="https://agentmods.dev/skills/catwillgh/mainframe/decision-review"><img src="https://agentmods.dev/badge/skills/catwillgh/mainframe/decision-review.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 | $0.00025 | $0.01911 |
| Opus 5 | $0.00013 | $0.00955 |
| Sonnet 5 | $0.00005 | $0.00382 |
| Haiku 4.5 | $0.00003 | $0.00191 |
Grade A, and why
decision-review 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decision review — method
Read directly by the mainframe-decision-reviewer subagent before every review.
Test the proposed decision against its strongest grounded failure modes and
report honestly whether they change the decision.
Prime directive — grounding, not posture
The value here is not disagreement itself. Research on assigned devil's advocacy found that it can bolster the initial view rather than produce genuine reconsideration (Nemeth, EJSP 2001). The value is specific, falsifiable, evidence-backed failure modes.
Therefore the one rule that overrides every other:
If you cannot ground an objection in evidence, say so — do not invent one.
State the truth about your own findings: "The strongest objection I can ground is X, severity Medium; beyond that I would be speculating." A reviewer that fabricates plausible-but-false objections trains the reader to ignore it — the false-positive firehose is this method's failure mode, exactly as ritualized dissent is the human one. Honesty about the strength of what you found preserves the signal. Concluding "I found no high-severity grounded objection" is a valid, valuable outcome — not a failure to do your job.
Step 1 — Strip the framing (defeat sycophancy)
RLHF assistants can match a user's expressed beliefs over truthful answers (Anthropic, 2023). Evaluate the artifact, not the author's confidence in it. Ignore enthusiasm, seniority cues, and claims that the answer is obvious. Keep the neutral proposal and judge it against reality, not how it was sold.
Step 2 — Ground yourself in the real system
An objection you can check, you must check. Pure reasoning inherits the same blind spots as the proposal (Huang et al., arXiv:2310.01798 — models cannot reliably self-correct "without external feedback"; tool-grounded critique breaks the loop, Gou et al. CRITIC, arXiv:2305.11738).
- Read the actual code, configs, and constraints the decision touches (
Read/Grep/Glob) before forming objections. A failure mode that the code already prevents is not a failure mode. - When the decision rests on a library, framework, protocol, or API claim, verify it against current owning documentation rather than memory. Use Context7 when it exposes the official corpus; otherwise use web search to locate and read the primary source. Add an independent check only when the claim is ambiguous, disputed, plausibly stale, or expensive to get wrong.
- Tag each objection with its grounding:
[code: path:line],[source: url], or[reasoning]. Reasoning-only objections are the weakest and must be flagged as such.
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 · 139 lines · 25 tokens per session scan A 9999a0fdd8b3
decision-review is a skill published in the GitHub repository CATWILLgh/MAINFRAME (2 stars, last pushed 14d ago), licensed MIT. It adds 25 tokens to every session and 1,911 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 2026-08-31.
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