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/9j/rapidspec/from-lineargit clone --depth 1 https://github.com/9j/RapidSpecWhat 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 | $0.01434 |
| Opus 5 | $0.00006 | $0.00717 |
| Sonnet 5 | $0.00003 | $0.00287 |
| Haiku 4.5 | $0.00001 | $0.00143 |
Grade A, and why
RapidSpec: From Linear scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Proposal from Linear Issue
<command_purpose> Convert Linear issues to RapidSpec proposals with automated research and verification. Fetch issue details, run parallel agents for research, present options, generate files. </command_purpose>
<issue_id> #$ARGUMENTS </issue_id>
<critical_requirement> MUST fetch full Linear issue details before proceeding. MUST analyze requirements and map to RapidSpec format. MUST delegate to /rapid:proposal for complete proposal workflow. NEVER duplicate proposal logic - use existing workflow. </critical_requirement>
Main Tasks
1. Fetch Linear Issue (ALWAYS FIRST)
Fetch process:
# Via Linear API
linear issue <issue-number>
- Get title, description, comments
- Get linked issues and PRs
- Get current status, assignee, labels
- Extract acceptance criteria from description
2. Analyze Requirements
Analysis process:
- Parse user requirements from description
- Identify acceptance criteria (bullet points, numbered lists)
- Extract technical constraints from comments
- Map to RapidSpec format (Why, What, Options)
- Prepare feature description for proposal command
3. Delegate to Proposal Command
Delegation process:
- Execute:
rapid proposal <change-id> - Pass analyzed requirements as feature description
- Let /rapid:proposal handle all research, verification, and file generation
- Wait for proposal completion
4. Complete
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.
- yesterday First seen · 236 lines · 0 tokens per session scan A 2cda666f0eb9
RapidSpec: From Linear is a command published in the GitHub repository 9j/RapidSpec (5 stars, last pushed 9mo ago), licensed MIT. It adds 13 tokens to every session and 1,434 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
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.