Borrowing it
Nothing to install: this file belongs to raydeStar/sir-thaddeus. 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/raydeStar/sir-thaddeus/master/.github/instructions/local-model-harness-workflow.instructions.mdgit clone --depth 1 https://github.com/raydeStar/sir-thaddeusWrote 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/instructions/raydestar/sir-thaddeus/local-model-harness-workflow)<a href="https://agentmods.dev/instructions/raydestar/sir-thaddeus/local-model-harness-workflow"><img src="https://agentmods.dev/badge/instructions/raydestar/sir-thaddeus/local-model-harness-workflow.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 | $0.04932 | $0.04932 |
| Opus 5 | $0.02466 | $0.02466 |
| Sonnet 5 | $0.00986 | $0.00986 |
| Haiku 4.5 | $0.00493 | $0.00493 |
Grade C, and why
Local-Model Harness Tuning Workflow scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -s http://localhost:1234/v1/models | python -m json.tool Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s http://localhost:1234/v1/models | python -m json.tool How it starts
The opening of the file, as written. The whole thing — 311 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local-Model Harness Tuning Workflow
Companion to e2e-harness-rules.instructions.md. Those rules stay in force — this file documents the operational loop: verify model, baseline, triage, patch, iterate. Written from a live session where we went from 25% pass rate to stable 54%+ with targeted fixes.
Read this BEFORE running the harness on a new model size. Most of the friction this session came from skipping a step below.
0. Sanity-check the model before anything else
Changing the model in LM Studio (or llama.cpp, or Ollama, etc.) is not enough. The runtime reads llm.model from:
%LOCALAPPDATA%\SirThaddeus\settings.json
Verify from PowerShell:
(Get-Content "$env:LOCALAPPDATA\SirThaddeus\settings.json" -Raw | ConvertFrom-Json).llm |
Select-Object model, gatekeeperModelId, contextWindowTokens, maxTokens
Then confirm the LM Studio server actually has that id loaded:
curl -s http://localhost:1234/v1/models | python -m json.tool
If the response's data[].id doesn't match llm.model, either flip the settings or the LM Studio alias. Wrong model = wrong baseline = every fix you write is chasing a ghost.
1. Baseline before you patch
Always run the full baseline on the target model once before you change a single line of code. Failures on a 4B look very different from failures on a 2B — budgets, routing, and keyword coverage all shift.
# Stop any stale runtime/MCP first (the harness forks its own; stale ones eat GPU)
Stop-Process -Name 'Thaddeus.Runtime','SirThaddeus.McpServer' -Force -ErrorAction SilentlyContinue
# Single suite, quick sanity
.\dev\harness.ps1 --suite smoke --judge none
# Full baseline (30-90 min on 4B, 10-20 min on 2B)
.\dev\harness.ps1 --all --judge none
Capture pass/fail numbers per suite before you touch anything:
$suites = @('smoke','reasoning','web-search','quality','tool-contracts','personality','footman-validation','existence')
foreach ($s in $suites) {
$out = .\dev\harness.ps1 --suite $s --judge none 2>&1 | Out-String
$p = [regex]::Match($out, 'Passed: (\d+)').Groups[1].Value
$f = [regex]::Match($out, 'Failed: (\d+)').Groups[1].Value
"$s`t$p/$(([int]$p + [int]$f))"
}
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.
- 7d ago First seen · 311 lines · 4,932 tokens per session scan C b500a14cfa94
Local-Model Harness Tuning Workflow is an instructions file published in the GitHub repository raydeStar/sir-thaddeus (13 stars, last pushed 6d ago), licensed Apache-2.0. It adds 4,932 tokens to every session, about $0.0247 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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