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Skill Sylad/claude-code-codex/.claude/skills/save

Codex non-officiel sur Claude Code — theorie, ecosysteme, case studies, ressources. Code en pair-programming avec Claude Code.

Install
npx -y skills add Sylad/claude-code-codex --skill save

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when the user wants to capture the learnings from the current session into persistent artifacts — auto-memory notes, sub-agent definitions, custom skills, hooks, or project-level CLAUDE.md additions. Triggers on phrases like "/save", "save what we learned", "memorize this session", "capture the learnings", or after a substantial back-and-forth that produced reusable patterns.

SKILL.md

6.8 KB, ~1.5k tokens by cl100k_base, as published. Nobody here has run it

Save

Reviews the current session and proposes which learnings to persist as which artifact type. Companion to /skillify (which transforms external concepts into skills) — this one captures the internal learnings of a session into the right durable form.

When to use this skill

Use save when:

  • The user explicitly says /save, "memorize this", "capture what we learned"
  • A long session has produced multiple reusable patterns and the user wants to harvest them before context is lost
  • After a productive back-and-forth that surfaced a new convention, anti-pattern, or tool usage worth keeping

Don't use save when:

  • The session was a single one-off task with no transferable learning
  • Auto memory will already capture it implicitly (the system writes some memories automatically — don't duplicate)
  • The user is asking to save a specific file (that's just Write, not this skill)

The 5 categories of artifact

Each captured learning belongs in exactly one home. Pick the right home — wrong home = lost or duplicated.

ArtifactPathUse for
Memory note~/.claude/projects/<project>/memory/<name>.mdUser profile, feedback (do/don't), project state, references to external systems. Cross-session.
Sub-agent.claude/agents/<name>.md (project) or ~/.claude/agents/<name>.md (user)A reusable role with its own context window — code-reviewer, security-auditor, lore-curator.
Custom skill.claude/skills/<name>/SKILL.md (project) or ~/.claude/skills/<name>/SKILL.md (user)A reusable workflow or methodology, slash-commandable. Use /skillify if extracting from an external tool.
Hook.claude/settings.json hooks: block + script in .claude/hooks/<name>.shAn automatic enforcement at a lifecycle event (PreToolUse, SessionStart, etc.). Not user-callable.
CLAUDE.md addition<project>/CLAUDE.md or ~/.claude/CLAUDE.mdA durable project-level rule that should always be in context. Not a one-shot hint.

If the learning fits none of the above — drop it. Not everything is worth persisting.


The procedure

Track each step with TodoWrite from the start.

Step 1 — Review the session

Re-read the conversation context (the user's recent messages + your own actions). Identify the non-obvious things — patterns the user explicitly validated, mistakes you made + corrections, recurring conventions, tools/URLs the user shared, decisions with rationale.

Skip everything that:

  • Is already in MEMORY.md (run Read ~/.claude/projects/<project>/memory/MEMORY.md first to know what's already there)
  • Is in CLAUDE.md (Read CLAUDE.md)
  • Is trivial / one-off / "what to do next" task state

Step 2 — Categorize each candidate

For each learning, propose its home with the table above. Use this template :

1. <one-sentence description of the learning>
   → <Memory | Agent | Skill | Hook | CLAUDE.md>
   → Path: <proposed path>
   → Why this home: <one sentence>

If you're hesitating between two homes, default to Memory (it's the most reversible).

If two learnings are about the same topic, merge them. Don't fragment.

Step 3 — Confirm with user

Present the categorized list via AskUserQuestion. Let the user :

  • ✅ Approve all
  • ✏️ Edit specific items (re-categorize, rename, drop)
  • ❌ Drop some

Don't write anything yet. Wait for explicit OK.

Step 4 — Write artifacts

For each approved learning, create the file with proper structure :

Memory note : YAML frontmatter (name, description, type ∈ user/feedback/project/reference) + body. For feedback/project types, include Why: and How to apply: lines.

Sub-agent : YAML frontmatter (name, description ≤ 200 chars, tools, optional model/permissionMode) + body system prompt.

Skill : YAML frontmatter (name, description includes trigger phrases, argument-hint, allowed-tools) + body with When-to-use / Procedure / Examples / Anti-patterns. Cap at ~250 lines. If extracting from external source, prefer /skillify instead.

Hook : add to .claude/settings.json hooks: array with proper matcher + handler. Script in .claude/hooks/<name>.sh made executable.

CLAUDE.md addition : append a clearly-scoped section. Don't dump huge blocks. Project-specific only — don't duplicate the global file.

Step 5 — Update indices & finalize

  • For memories: append a line to MEMORY.md index in the format - [Title](file.md) — one-line hook
  • Verify nothing leaked into the wrong scope (user-global vs project-local).
  • Propose a single commit if applicable: chore: capture session learnings (memories + skills + ...).

Examples

Mid-session save (recommended cadence): after a productive 1h+ session that introduced a new convention. Run /save to harvest before context closes.

Post-test save: after testing a third-party tool (like Ruflo), /save is the natural follow-up to /skillify/skillify extracts the idea, /save captures the meta-learnings (what we learned about the testing process itself, the verdict pattern, the watchlist).

End-of-day save: at the end of a long working day, /save to consolidate before tomorrow.


Anti-patterns

  • ❌ Saving everything. Apply quality bar — if a future session won't gain from this, drop it.
  • ❌ Putting a workflow into Memory instead of a Skill. Workflows belong to Skills (or Agents).
  • ❌ Putting a one-off project decision into the user-global ~/.claude/CLAUDE.md. Project-specific stays project-local.
  • ❌ Skipping confirmation. Always present the categorized list before writing.
  • ❌ Re-saving things already covered. Read MEMORY.md and existing skills/CLAUDE.md first to avoid duplication.
  • ❌ Modifying the user-global ~/.claude/CLAUDE.md without explicit text confirmation. Some hooks block self-modification — fall back to printing the suggested change.

Companion skills

  • /skillify — when the learning is a concept observed in an external tool, transform it into a skill (Step 4 then becomes "write a skill file via the skillify procedure").
  • (future) — /load could be its mirror : load relevant memory/skills at session start based on the current task.

Gives 0 of the 12 instructions most memory context skills give in ~1.5k tokens

Counted across 674 of the 847 authors here whose files we hold, read 2026-08-06

  • inform the user when setup is completein 21 of 674, across 6 files
  • confirm the draft with the user before writingin 21 of 674, across 6 files
  • update the agent skills block in place if it existsin 21 of 674, across 6 files
  • present findings to the userin 20 of 674, across 5 files
  • write the three docs files from seed templatesin 20 of 674, across 5 files
  • ask the user about each decision one at a timein 19 of 674, across 4 files
  • edit CLAUDE.md if it existsin 18 of 674, across 3 files
  • explore current repo statein 18 of 674, across 3 files
  • do not overwrite user edits to surrounding sectionsin 18 of 674, across 3 files
  • back up the original file before overwritingin 16 of 674, across 8 files
  • keep the memory index under 200 linesin 15 of 674
  • Provide actionable steps and verificationin 13 of 674, across 2 files

Said here and by no other author read

  • track steps with todowrite
  • review the session for non-obvious patterns
  • categorize each learning into one artifact type
  • merge learnings about the same topic
  • present the list to the user for confirmation
  • create the file with the proper structure

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.