Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Randroids-Dojo/skills/plugin install spiralWrote 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/randroids-dojo/skills/spiral-init)<a href="https://agentmods.dev/commands/randroids-dojo/skills/spiral-init"><img src="https://agentmods.dev/badge/commands/randroids-dojo/skills/spiral-init.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 | $0.00010 | $0.00381 |
| Opus 5 | $0.00005 | $0.00191 |
| Sonnet 5 | $0.00002 | $0.00076 |
| Haiku 4.5 | $0.00001 | $0.00038 |
Grade A, and why
spiral-init 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.
What it actually says
/spiral init
Bootstrap the structural-discipline scaffold into the current repo.
When to use
At the start of a fresh project, before any feature code. Writes the canonical files (AGENTS.md plus the docs/ ledger set, including docs/DEPENDENCY_LEDGER.md and the qualitative-gate docs) so an autonomous PR loop can run against the repo.
Refuses to run if AGENTS.md already exists. Use /spiral audit on existing repos instead.
How to invoke
Ask the user three questions via AskUserQuestion before running:
- Project name. Used in headers and substituted as
{{PROJECT_NAME}}. - One-line pitch. Substituted as
{{PITCH}}. Used inAGENTS.mdand the GDD index. - Stack. Substituted as
{{STACK}}. Used inAGENTS.mdRule 3. Free text. Examples: "Next.js + Three.js + Vercel KV", "Godot 4.x + GDScript", "Rust + axum + Postgres".
Then run:
bash ${CLAUDE_PLUGIN_ROOT}/scripts/init.sh "<name>" "<pitch>" "<stack>"
After the script completes:
- Print the list of written files.
- Tell the user the next step is to draft the first GDD section under
docs/gdd/<n>-<title>.md. - Suggest
/randroid:loop implementonce the first GDD section exists.
Output
The script prints every written file path, plus a one-line note for the user about next steps.
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 Changed · +6 lines · +10 tokens per session d14f6e2e260d
- 7d ago First seen · 34 lines · 0 tokens per session scan A fe4f511bcb5c
spiral-init is a command published in the GitHub repository Randroids-Dojo/skills (46 stars, last pushed 7d ago), licensed MIT. It adds 10 tokens to every session and 381 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
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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.