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/jjmartres/ai-coding-agents/speckit.model-selectorgit clone --depth 1 https://github.com/jjmartres/ai-coding-agentsWrote 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/jjmartres/ai-coding-agents/speckit.model-selector)<a href="https://agentmods.dev/commands/jjmartres/ai-coding-agents/speckit.model-selector"><img src="https://agentmods.dev/badge/commands/jjmartres/ai-coding-agents/speckit.model-selector.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 | $0.00020 | $0.01833 |
| Opus 5 | $0.00010 | $0.00916 |
| Sonnet 5 | $0.00004 | $0.00367 |
| Haiku 4.5 | $0.00002 | $0.00183 |
Grade A, and why
speckit.model-selector scanned grade A with 1 finding 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 5d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://openrouter.ai/api/v1/models" \ How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SpecKit Model Selector
You are an AI agent that helps configure SpecKit workflow command with the optimal AI model for each step, sourced from the live OpenRouter model catalog.
Work through the steps below in order. Do not skip a step or proceed until the previous one is complete.
Step 1 — Verify SpecKit is installed locally
Check for the presence of SpecKit command files under .opencode/command/ in the current working directory.
Run:
ls .opencode/command/speckit.* 2>/dev/null || echo "NOT_FOUND"
- If the output is
NOT_FOUNDor the directory does not exist, stop immediately and tell the user:SpecKit command are not present under
.opencode/command/. Please runspecify init . --integration opencodefirst, then re-run this command. - If at least one
speckit.*file is found, list the discovered files and proceed to Step 2.
Step 2 — Read the SpecKit detailed workflow
Fetch the official SpecKit README from:
https://github.com/github/spec-kit#-detailed-process
Extract and summarise the 7 core workflow steps with their associated SpecKit command:
| # | Command | Purpose |
|---|---|---|
| 1 | /speckit.constitution |
Establish project governing principles |
| 2 | /speckit.specify |
Create functional specifications (user stories, requirements) |
| 3 | /speckit.clarify |
Clarify gaps before planning (structured Q&A) |
| 4 | /speckit.plan |
Generate technical implementation plan |
| 5 | (plan validation — free prompt) | Audit plan for missing pieces and over-engineering |
| 6 | /speckit.tasks |
Break plan into actionable, ordered, dependency-aware tasks |
| 7 | /speckit.implement |
Execute tasks and implement the feature |
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.
- 5d ago First seen · 168 lines · 20 tokens per session scan A 95593ca2fef1
speckit.model-selector is a command published in the GitHub repository jjmartres/ai-coding-agents (44 stars, last pushed 2mo ago), licensed MIT. It adds 20 tokens to every session and 1,833 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
rebase
Rebase the current branch on main, a local branch, or a remote branch (with fetch).
compete
NEVER workspace/[name].md (wrong) ALWAYS workspace/company/[name].md (correct).
ponytail
Switch ponytail intensity level (lite/full/ultra/off).
research-topic
Research a topic using multi-agent-researcher skill.
e2e
Generate and run end-to-end tests with Playwright. Creates test journeys, runs tests, captures screenshots/videos/traces, and uploads artifacts.
close-issue
../../knowledge/procedures/close-issue-procedure.md.