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 bostonaholic/rpikit --skill writing-plansgit clone --depth 1 https://github.com/bostonaholic/rpikitWrote 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/bostonaholic/rpikit/writing-plans)<a href="https://agentmods.dev/skills/bostonaholic/rpikit/writing-plans"><img src="https://agentmods.dev/badge/skills/bostonaholic/rpikit/writing-plans/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/bostonaholic/rpikit/writing-plans"><img src="https://agentmods.dev/badge/skills/bostonaholic/rpikit/writing-plans.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 80 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- medium Excessive Agency · line 213 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 393 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00032 | $0.02894 |
| Opus 5 | $0.00016 | $0.01447 |
| Sonnet 5 | $0.00006 | $0.00579 |
| Haiku 4.5 | $0.00003 | $0.00289 |
Grade A, and why
writing-plans 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 11d 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 — 418 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planning Phase
Create an implementation plan for: $ARGUMENTS
Purpose
Planning transforms research findings into actionable implementation strategy. A good plan enables disciplined execution by breaking work into granular tasks with clear verification criteria. Plans serve as contracts between human and AI, ensuring alignment before code is written.
Process
1. Check for Research
Look for existing research at: docs/plans/YYYY-MM-DD-<topic>-research.md
(Search for files matching *-<topic>-research.md pattern)
If research exists:
- Read and reference the research findings
- Build the plan on documented context
- Link to research in plan document
If no research exists:
- Ask if research should be conducted first
- For high-stakes tasks, recommend research first
- For low-stakes tasks, use the file-finder agent to locate relevant files:
Task tool with subagent_type: "file-finder"
Prompt: "Find files related to [task]. Goal: [what will be implemented]"
2. Define Success Criteria
Before planning tasks, establish what "done" looks like:
- Functional requirements (what it does)
- Non-functional requirements (performance, security)
- Acceptance criteria (how to verify)
Use AskUserQuestion to clarify requirements if needed.
3. Classify Stakes
Determine implementation risk level:
| Stakes | Characteristics | Planning Rigor |
|---|---|---|
| Low | Isolated change, easy rollback, low impact | Brief plan |
| Medium | Multiple files, moderate impact, testable | Standard plan |
| High | Architectural, hard to rollback, wide impact | Detailed plan |
Document the classification and rationale in the plan.
4. Break Down Tasks
Decompose work into granular, verifiable steps.
Identify target files:
Use file paths from research document, or if unavailable, use the file-finder agent to locate files for each task area:
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.
- 11d ago First seen · 418 lines · 32 tokens per session scan A b9add9872015
writing-plans is a skill published in the GitHub repository bostonaholic/rpikit (20 stars, last pushed 4d ago), licensed MIT. It adds 32 tokens to every session and 2,894 once invoked, about $0.0002 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.
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Analyze CSV files in the workspace and summarize insights.
run_jinx
Execute a jinx by name (already loaded on the team) or by filesystem path (e.g. a freshly createdjinx that isn't yet registered), passing input values as a JSON object. Returns the jinx's output field or the full context dict. Use this to run a jinx you just wrote without exiting the session.