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 skills/kimgoetzke/coding-agent-configs/improve-a-plannpx skills add kimgoetzke/coding-agent-configs --skill improve-a-plangit clone --depth 1 https://github.com/kimgoetzke/coding-agent-configsWrote 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/kimgoetzke/coding-agent-configs/improve-a-plan)<a href="https://agentmods.dev/skills/kimgoetzke/coding-agent-configs/improve-a-plan"><img src="https://agentmods.dev/badge/skills/kimgoetzke/coding-agent-configs/improve-a-plan.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 | $0.00034 | $0.00825 |
| Opus 5 | $0.00017 | $0.00413 |
| Sonnet 5 | $0.00007 | $0.00165 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
improve-a-plan 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 6d 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Phase 1: Locate the plan
Resolve the target plan using the first matching strategy:
- Context inference — check your conversation context for a plan folder path from a prior
planning-modeorplanningskill invocation in this session. If found, use it directly and confirm with the user (e.g. "Improving plan2026-03-24 auth refactor— correct?"). Proceed on confirmation; if the user says no, fall through to the next strategy. - User argument — if the user provided an argument, resolve it as a number or folder name against the folders inside
{repo root}/.ai/planning/e.g.{repo root}/.ai/planning/2026-01-20 improve-mfa-flow/. Do not read the contents of any plan folders until the plan has been resolved. - Single plan — if only one plan folder exists, select it automatically.
- Multiple plans — list folders newest first in a numbered list and ask the user to choose. Do not read the contents of any plan folder until the plan has been resolved.
- No plans — tell the user no plans were found and suggest creating one with the
planning-modeorplanningskill.
Phase 2: Improve the plan
- If you haven't already, read the planning documents now
- Tell the user that they generally have the following options:
1. Adversarial review in a fresh session
Start a new session and ask me to critique the plan with fresh eyes (no bias from having written it).
Something like: "Read the plan in <location of plan> and the findings. Identify assumptions, gaps, and areas likely to break during implementation." A separate session has clean context and is more likely to spot issues than the one that wrote the plan.
2. Dry-run in plan mode
Use Shift+Tab to switch to plan mode, then ask me to walk through the plan as if implementing it — reading the actual
source files, checking types exist, verifying method signatures match what the plan assumes. This surfaces mismatches
between the plan and reality (e.g. a method signature that's slightly different, a missing dependency, a type that
doesn't exist yet).
3. Explicit assumption check
Ask me to list every assumption the plan makes (e.g. about an external API) and verify each one against an actual
source. The findings doc has some of this, but after a lot of back and forth, it may be worth a refresh.
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.
- 6d ago First seen · 54 lines · 34 tokens per session scan A a34636a9d243
improve-a-plan is a skill published in the GitHub repository kimgoetzke/coding-agent-configs (2 stars, last pushed 17d ago), licensed MIT. It adds 34 tokens to every session and 825 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 2026-08-31.
Other skills, from other repositories
codebase-memory
Use the codebase knowledge graph for structural code queries. Triggers on: explore the codebase, understand the architecture, what functions exist, show me the structure, who calls this function, what does X call, trace the call chain, find callers of, show dependencies, impact analysis, dead code, unused functions…
using-pi-subagents
Operate pi-subagents jobs safely, including direct-work decisions, least-privilege tool selection, thinking-level selection, delegation, bidirectional messaging, parallel starts, timeout selection, waiting, cancellation, result handling, verification, and writer isolation.
configuring-pi-starship
Configure @narumitw/pi-starship and answer questions about its pi-starship.toml settings. Use when the user asks to create, edit, repair, migrate, or understand pi-starship.toml, or asks which pi-starship module, variable, option, style, palette, preset, or runtime behavior to use. Do not use for generic TOML, shell…
skill-creator
Create or update Agent Skills (SKILL.md plus optional scripts, references, or assets). Use when someone asks to design a new Agent Skill, refine an existing one, or structure skills for Pi discovery, packaging, or other Agent Skills-compatible clients.
pi-ralph-wiggum
Long-running iterative development loops with pacing control and verifiable progress. Use when tasks require multiple iterations, many discrete steps, or periodic reflection with clear checkpoints; avoid for simple one-shot tasks or quick fixes.
surf
Control Chrome browser via CLI for testing, automation, and debugging. Use when the user needs browser automation, screenshots, form filling, page inspection, network/CPU emulation, DevTools streaming, or AI queries via ChatGPT/Gemini/Perplexity/Grok/AI Studio.