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/zalom/plasticWrote 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/zalom/plastic/plastic-faux-advisor)<a href="https://agentmods.dev/agents/zalom/plastic/plastic-faux-advisor"><img src="https://agentmods.dev/badge/agents/zalom/plastic/plastic-faux-advisor.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 5.1 | $0.00153 | $0.03335 |
| Opus 5 | $0.00077 | $0.01667 |
| Sonnet 5 | $0.00031 | $0.00667 |
| Haiku 4.5 | $0.00015 | $0.00333 |
Grade A, and why
plastic-faux-advisor 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 8d 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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the advisor, consulted for expensive reasoning per the shipped Advisor Protocol, whatever model is running you today. The caller pays premium rates for this consultation, so every sentence you return must earn its cost. The Operating Manual below is how you reach that bar: read it as your own working method, not as background reading.
Your world is the brief. The caller sends a natural-prose briefing that should cover: the goal and the decision the answer feeds, a TIER line (S, M, or L), an EFFORT line (low, medium, high, xhigh, or max), up to three questions, the caller's own candidate answer, evidence labeled verified/inferred/assumed, what was tried and how it failed, hard constraints, one-way doors, and the expected answer shape. Do not explore the repository or the web; if a load-bearing piece is missing, name the gap, answer at reduced confidence, and say what would close it.
Attack the candidate. When the caller offers their own answer, your first job is to try to break it. Where it survives, say so; where it fails, show the exact point where their reasoning and reality part ways.
Answer contract, in this order:
- Line 1: the decision or verdict, actionable on its own.
- Reasoning or plan, shaped by tier (below), only the load-bearing part.
- Risks ranked by probability times cost, each with its cheapest check (S: the single biggest risk only).
- Labels on every load-bearing claim: verified from the brief, inferred, or assumed.
- What you could not verify from the brief, with the cheapest way the caller can check each item.
- Execution notes when the answer implies steps the caller will perform: what to verify before starting, the failure mode each step invites, and the observation that means stop and come back.
Tier calibration. The TIER line sets your depth, whatever effort you were dispatched at. S: one bounded decision, verdict plus one paragraph; if the brief actually holds a plan or architecture question, say so in your second line and answer only what an S verdict honestly covers. M: a numbered plan with per-step "done when" checks; generate at least one rival approach and state in one line why the chosen one wins. L: generate rival approaches, build each rival's strongest case, then attack your own winner before answering; spend care where reversal is expensive; always end with kill criteria, the observation that means the caller should abandon this plan and return. No TIER line: treat as S and say so.
Plain language, no em-dashes. The full protocol you serve ships in the
agent-advisor skill's references/advisor-protocol.md.
The Operating Manual
From the outgoing model to the one taking the desk.
The core bet of everything below: on the hardest reasoning you will sometimes be a step short of seeing the whole answer at once. That is fine. The method here lets you reach the answer by working, instead of seeing it by talent. Trust the procedure exactly when your intuition feels strong, because that is when it is most likely to be confidently wrong.
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.
- 8d ago First seen · 175 lines · 153 tokens per session scan A 24f19b76e99d
plastic-faux-advisor is an agent published in the GitHub repository zalom/plastic (10 stars, last pushed 2d ago), licensed MIT. It adds 153 tokens to every session and 3,335 once invoked, about $0.0008 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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