code-planning

A skill describing how to turn a software request into a precise implementation plan. It requires the plan to explain the reason for the work, project rules, and enough detail for different developers to reach nearly the same result.

In plain words
What is it for?
Writing repository plans for feature work, redesigns, fixes, or other changes that need clear scope, constraints, and implementation guidance.
Why use it?
It reduces guesswork before coding and makes proposed changes easier to review and carry out consistently.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/ai-swiss/base/code-planning
Any agent
npx skills add ai-swiss/base --skill code-planning
Clone the repo
git clone --depth 1 https://github.com/ai-swiss/base

Made for: Claude Code, Codex.

Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,944 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00027 $0.01944
Opus 5 $0.00014 $0.00972
Sonnet 5 $0.00005 $0.00389
Haiku 4.5 $0.00003 $0.00194

Measured 2d ago against content hash dc54d7513f68, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

code-planning 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 2d 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.

.ai/agents/base-contributor/skills/competences/code-planning/SKILL.md · 131 lines

How it starts

The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Planning code: from a ticket of intent to an execution plan

This skill applies whenever a request ("redo the evaluation screen", "add a bootstrap") becomes an execution plan that a developer, even a junior, even an AI agent with no context, can follow without inventing. The bar: the plan is precise enough that two different implementers would produce nearly the same code, and motivated enough that a demanding reviewer recognises a doctrine in it, not a task list. It complements ../code-craft/SKILL.md, which covers the act of coding.

A plan lives in .plans/YYYY-MM-DD_subject.md of the repo it concerns. It is disposable after merge: the code, the tests and the CHANGELOG must stand on their own, which is what rule 1 below enforces.


1. The eight sections of a complete plan, in this order

  1. Origin: 3 to 5 lines on which concrete feedback or problems triggered this work (cite the observed symptoms, not generalities). This is what lets you arbitrate later: "does this slice serve one of the origin problems?"
  2. Non-negotiable rules: the repo's invariants (zero dependency, the write gate, tests by role, max component size, no old/new code cohabitation…) AND the exact verification commands to run at the end of each slice.
  3. Engineering doctrine: see §3. This is what tells a plan apart from a to-do list.
  4. The slice table: number, content, estimated size, dependencies between slices.
  5. The detailed slices: see §4.
  6. Known traps: each mistake already made on this repo or foreseeable on this ticket, phrased as an instruction ("apply bottom-up, or the line numbers shift"), to read BEFORE coding the relevant slice.
  7. The self-review grid: 10 to 12 binary questions the implementer asks of THEIR own diff before showing it (function over 40 lines? derivable state stored? one rule known in two places? a silent cap?). One "yes" = fix it first.
  8. The definition of done: checkboxes, mechanically verifiable where possible.

Read the full file on GitHub · 131 lines

Changes

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.

  1. 2d ago First seen · 131 lines · 27 tokens per session scan A dc54d7513f68

Subscribe to this mod's changes

code-planning is a skill published in the GitHub repository ai-swiss/base (42 stars, last pushed 19d ago), licensed Apache-2.0. It adds 27 tokens to every session and 1,944 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.

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