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 skills add Jartan-LLC/grimoire --skill plan-issuegit clone --depth 1 https://github.com/Jartan-LLC/grimoireWrote 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/skills/jartan-llc/grimoire/plan-issue)<a href="https://agentmods.dev/skills/jartan-llc/grimoire/plan-issue"><img src="https://agentmods.dev/badge/skills/jartan-llc/grimoire/plan-issue/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jartan-llc/grimoire/plan-issue"><img src="https://agentmods.dev/badge/skills/jartan-llc/grimoire/plan-issue.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00014 | $0.00484 |
| Opus 5.5 | $0.00006 | $0.00194 |
| Sonnet 5.5 | $0.00003 | $0.00097 |
| Haiku 4.5 | $0.00001 | $0.00048 |
Grade A, and why
plan-issue 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 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.
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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Issue
Create an implementation plan for a GitHub issue.
Process
1. Load Issue
Determine the issue number from the user's request, which may carry context beyond the number. Fetch the issue details:
gh issue view <issue-number>
2. Read Relevant Docs
Based on the issue area, read the appropriate project documentation in docs/.
3. Explore Codebase
Find existing patterns similar to what the issue requires. Look at how comparable features are already implemented.
4. Assess Scope
Evaluate whether the issue should be broken into sub-issues. Not every multi-part feature needs splitting -- but large features degrade in quality when implemented in a single AI context. Consider splitting when:
- The diff would be very large -- Thousands of lines in one pass means mistakes get buried. Smaller, focused issues produce better results.
- There are genuinely separable concerns -- A reusable module that happens to be needed by this feature is worth its own issue. A small page that accompanies a backend feature is not.
If sub-issues are warranted, use /praxis:create-issue to file them, referencing the parent issue. Sub-issue branches are created from the parent feature branch and merged back into it, not directly to main.
5. Design Considerations
Before creating the plan, think through:
- Flexibility -- Will this be used by other modules or downstream consumers? Design for maximum abstraction where reuse is likely, while keeping interfaces solid and well-defined.
- Robustness -- Clear models, explicit types, proper error handling. Flexible internals, rigid contracts.
- Future-proofing -- Consider how this might need to evolve without requiring breaking changes.
- Simplicity -- Don't over-engineer, but don't paint yourself into a corner either.
6. Create Plan
Write a plan in .claude/workspace/ following the format in the gitwise:github-conventions skill (see plan-format.md).
7. Post Plan
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 Changed · -3 lines 64c27748ec2f
- 1mo ago First seen · 61 lines · 14 tokens per session scan A f9e79196e3b6
plan-issue is a skill published in the GitHub repository Jartan-LLC/grimoire (2 stars, last pushed yesterday), licensed MIT. It adds 14 tokens to every session and 484 once invoked, about $0.0001 per session on Opus 5.5. 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.
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Clarify a new customer brief, desired outcome, constraints and evidence gaps. Use for kickoff or a first meeting; do not repeat discovery already supplied.
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Develop a business case for an initiative using costs, benefits, risks and evidence. Use when a sponsor needs budget or timeline justification.
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Plan how to earn customer trust and appropriate access. Use when credibility, permissions or AI policy constrain the engagement.