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/orlando-japan/claude-code-setting/spec-proposegit clone --depth 1 https://github.com/orlando-japan/claude-code-settingWrote 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/orlando-japan/claude-code-setting/spec-propose)<a href="https://agentmods.dev/commands/orlando-japan/claude-code-setting/spec-propose"><img src="https://agentmods.dev/badge/commands/orlando-japan/claude-code-setting/spec-propose.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 | $0.00013 | $0.00275 |
| Opus 5 | $0.00006 | $0.00138 |
| Sonnet 5 | $0.00003 | $0.00055 |
| Haiku 4.5 | $0.00001 | $0.00028 |
Grade A, and why
spec-propose 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.
What it actually says
You are starting a new spec-driven change for: $ARGUMENTS
Follow the OpenSpec propose workflow to produce:
- A proposal (
proposal.md) — rationale, scope, non-goals - A specification (
spec.md) — requirements with concrete scenarios - A design doc (
design.md) — technical approach, alternatives considered, rejected options and why - A task checklist (
tasks.md) — ordered, each task small enough to verify independently
Company conventions on top of OpenSpec:
- Place the change folder under
openspec/changes/<yyyy-mm-dd>-<slug>/ - Each task in
tasks.mdmust include a verification line: the exact command or check that proves it's done - The design doc must list at least one rejected alternative with the reason for rejection — if you can't think of one, the problem isn't clear enough
- Cross-link any related existing specs in
openspec/specs/
Before writing anything, ask any clarifying questions the feature description doesn't answer. Don't guess.
After producing the artifacts, show the user a summary and wait for approval before running /spec:apply.
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 · 25 lines · 13 tokens per session scan A 4fa3fb87a435
spec-propose is a command published in the GitHub repository orlando-japan/claude-code-setting (2 stars, last pushed 3mo ago), licensed MIT. It adds 13 tokens to every session and 275 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.