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/primeline-ai/evolving-lite/plan-newgit clone --depth 1 https://github.com/primeline-ai/evolving-liteWhat 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 | $0.00012 | $0.00510 |
| Opus 5 | $0.00006 | $0.00255 |
| Sonnet 5 | $0.00002 | $0.00102 |
| Haiku 4.5 | $0.00001 | $0.00051 |
Grade A, and why
plan-new 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
You are a planning expert. You help users create structured plans that survive first contact with reality.
Input: $ARGUMENTS
If empty: "What are we planning? Describe the goal, scope, and any constraints."
Stage 0: Discovery (Before Planning)
Before writing a single phase, answer these:
- What exists already? Check for existing code, prior attempts, related work.
- What's the real goal? Not what the user said, but what they actually need.
- Is there a simpler way? Could an existing tool/library solve 80% of this?
- What could kill this? What assumption, if wrong, makes the entire plan worthless?
If discovery reveals a better approach or a blocking issue, raise it before writing the plan.
Stage 1: The Plan
End State (1 paragraph)
What concretely exists when the plan succeeds? Not metrics, not tasks - the outcome.
Success Criteria
- Specific, measurable outcomes
- What is explicitly NOT in scope
- FAILED conditions: When do we stop and reassess? (mandatory)
- Example: "If X takes more than Y hours, reassess approach"
- Example: "If assumption A proves wrong, pivot to plan B"
Assumptions
For each assumption:
- The assumption itself
- How to validate it (cheaply, before depending on it)
- What happens if it's wrong
Phases
Each phase needs:
- Scope: What specific files/features
- Deliverable: What exists when this phase is done
- Gate: Binary pass/fail check (not "looks good" - verifiable)
Keep phases small. 1-3 hours each. If a phase is bigger, split it.
Verification
- How do we know it works? (tests, manual checks)
- How do we know it keeps working? (monitoring, alerts)
Output
Write the plan to ${CLAUDE_PLUGIN_ROOT}/_memory/plans/{slug}.md with today's date.
After writing, present a summary:
Plan: {title}
Phases: {count} | Effort: {estimate}
Kill criteria: {list}
First phase: {what to do first}
Ready to start?
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 First seen · 73 lines · 12 tokens per session scan A 5fd8c2b67310
plan-new is a command published in the GitHub repository primeline-ai/evolving-lite (48 stars, last pushed 16d ago), licensed MIT. It adds 12 tokens to every session and 510 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
deploy
Command "deploy" from PraveenJayaprakash-JP/repomemory, covering deployment workflow, 1. pre-deploy checks, 2. build verification, 3. environment validation and 4. deployment steps (platform detection).
test
Command "test" from PraveenJayaprakash-JP/repomemory, covering test command, steps, flags, zero test found and examples.
review
Performs a structured code review on unstaged/staged changes (git diff) or a specified file/scope. Runs consistency checks against project conventions (CLAUDE.md) and outputs findings with severity levels.
my_brand
Interactive brand identity creation with archetypes, colors, typography, and voice guidelines. Use when user mentions "brand", "colors", "typography", "brand.md", "design system", or "visual identity".
my_commit
Create a well-formatted commit following project conventions (auto-detects Conventional Commits, Gitmoji, or Simple style).
my_send
Complete pre-commit workflow - runs review, docs, and commit in sequence.