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 commands/geoloeg-ist/agents-reverse-engineer/settingsgit clone --depth 1 https://github.com/GeoloeG-IsT/agents-reverse-engineerWhat 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 | $0.00014 | $0.00879 |
| Opus 5 | $0.00007 | $0.00439 |
| Sonnet 5 | $0.00003 | $0.00176 |
| Haiku 4.5 | $0.00001 | $0.00088 |
Grade A, and why
gsd:settings 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 3d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Updates .planning/config.json with workflow preferences and model profile selection.
1. Validate Environment
ls .planning/config.json 2>/dev/null
If not found: Error - run /gsd:new-project first.
2. Read Current Config
cat .planning/config.json
Parse current values (default to true if not present):
workflow.research— spawn researcher during plan-phaseworkflow.plan_check— spawn plan checker during plan-phaseworkflow.verifier— spawn verifier during execute-phasemodel_profile— which model each agent uses (default:balanced)
3. Present Settings
Use AskUserQuestion with current values shown:
AskUserQuestion([
{
question: "Which model profile for agents?",
header: "Model",
multiSelect: false,
options: [
{ label: "Quality", description: "Opus everywhere except verification (highest cost)" },
{ label: "Balanced (Recommended)", description: "Opus for planning, Sonnet for execution/verification" },
{ label: "Budget", description: "Sonnet for writing, Haiku for research/verification (lowest cost)" }
]
},
{
question: "Spawn Plan Researcher? (researches domain before planning)",
header: "Research",
multiSelect: false,
options: [
{ label: "Yes", description: "Research phase goals before planning" },
{ label: "No", description: "Skip research, plan directly" }
]
},
{
question: "Spawn Plan Checker? (verifies plans before execution)",
header: "Plan Check",
multiSelect: false,
options: [
{ label: "Yes", description: "Verify plans meet phase goals" },
{ label: "No", description: "Skip plan verification" }
]
},
{
question: "Spawn Execution Verifier? (verifies phase completion)",
header: "Verifier",
multiSelect: false,
options: [
{ label: "Yes", description: "Verify must-haves after execution" },
{ label: "No", description: "Skip post-execution verification" }
]
}
])
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.
- 3d ago First seen · 137 lines · 14 tokens per session scan A 03fa0fcd6583
gsd:settings is a command published in the GitHub repository GeoloeG-IsT/agents-reverse-engineer (20 stars, last pushed 26d ago), licensed MIT. It adds 14 tokens to every session and 879 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 2026-08-30.
Other commands, from other repositories
auto-reason
Subjective self-refinement with blind judging, Borda aggregation, and provider-agnostic model routing.
deep-research
Deep research — produce a cited decision-grade research report and artifact bundle.
goals
Goal-oriented mission entrypoint — set, view, or drive long-running objectives through the mission system.
launch-worker
Manually launch headless workers against one or more GitHub issues.
check-routines
Check and safely repair aidevops routine scheduler health.
local-permissions-check
Audit local host/runtime permissions for aidevops on macOS, Linux, Windows, and WSL.