session-learning

session-learning is a skill for Claude Code, Codex from JakubMikolajek/codex-skills-collection. It costs 84 tokens per session (2,851 once invoked), scanned A, original, MIT.

A procedure for recording what was learned after coding implementation or review work. It tracks which guidance was used, what worked, and where instructions or routing were unclear.

In plain words
What is it for?
Maintaining skill notes, recording real failure patterns, and improving how repository tasks are routed and completed.
Why use it?
It turns observed problems into improvements for future coding sessions instead of letting the same mistakes repeat.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions AGENTS.md; mentions Codex.

Good fit Maintaining skill notes, recording real failure patterns, and improving how repository tasks are routed and completed.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jakubmikolajek/codex-skills-collection/session-learning
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.

Any agent
npx skills add JakubMikolajek/codex-skills-collection --skill session-learning
Clone the repo
git clone --depth 1 https://github.com/JakubMikolajek/codex-skills-collection

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for session-learning

README.md
[![agentmods](https://agentmods.dev/badge/skills/jakubmikolajek/codex-skills-collection/session-learning/github.svg)](https://agentmods.dev/skills/jakubmikolajek/codex-skills-collection/session-learning)
Your own site
<a href="https://agentmods.dev/skills/jakubmikolajek/codex-skills-collection/session-learning"><img src="https://agentmods.dev/badge/skills/jakubmikolajek/codex-skills-collection/session-learning/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.

agentmods 80×15 button for session-learning

Your own site · 80×15
<a href="https://agentmods.dev/skills/jakubmikolajek/codex-skills-collection/session-learning"><img src="https://agentmods.dev/badge/skills/jakubmikolajek/codex-skills-collection/session-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,851 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00084 $0.02851
Opus 5 $0.00042 $0.01425
Sonnet 5 $0.00017 $0.00570
Haiku 4.5 $0.00008 $0.00285

Measured 10d ago against content hash 20162871076c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

session-learning 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 10d 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.

skills/session-learning/SKILL.md · 295 lines

How it starts

The opening of the file, as written. The whole thing — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Session Learning

This skill closes the feedback loop on the skill system itself. After every implementation or review session, it records what the agent observed about skill quality, routing accuracy, and failure patterns — so the system improves from real usage rather than speculation.

When to Use

  • Automatically at the end of every /implement or /review task
  • After /debug when a root cause reveals a missing or incorrect skill section
  • After /multi-repo when cross-repo routing decisions were made
  • When the agent notices mid-session that a loaded skill was wrong, incomplete, or ambiguous

When NOT to Use

  • Session consisted only of answering a question with no file modifications
  • Task was a single-response clarification with no skill loading involved
  • User explicitly says "skip learning" or "no learning record needed"

Core Principles

Observation Over Inference

Only record patterns directly observed this session. Do not infer failure patterns from code not read, or routing quality from paths not taken.

Repository-Aware Paths

Do not hardcode skill paths. Resolve a workspace skill root first:

  • Use .codex/skills when the repository is bootstrapped under .codex
  • Use skills when skills live at repository root

Use [SKILLS_ROOT] in all instructions and outputs below.

Path guardrail:

  • Never emit mixed paths in one entry (for example both skills/... and .codex/skills/...)
  • Resolve [SKILLS_ROOT] once per session-learning run, then reuse it everywhere (skill list, per-skill detail path, global rollup detail path)

Run Evidence (if available)

Some orchestrators (a hybrid Claude + Codex session, for example) persist a machine-readable run log for each delegation instead of relying purely on in-context memory. When such evidence exists, ground Step 1-2 in it rather than reconstructing everything from recollection:

  • Look for .ai/runs/YYYY-MM.jsonl (or the project's documented equivalent) in the current project root before starting Step 1.
  • If present, treat its start/completion events (route, preloads, model/effort actually used, stop_reason) as ground truth for what ran — do not silently override them with a differently-remembered version.
  • Full event schema and the known gaps in what it captures (no timing, no original prompt in the completion half) are documented in references/run-log-schema.md in this skill.
  • If no run log exists for this session (a standalone Codex-only run, or an orchestrator that doesn't produce one), proceed exactly as before — this is a best-effort grounding source, not a hard requirement to run this skill at all.
  • Run evidence and the append-only references/failure-patterns.md / routing/FAILURES.md history are different things: evidence tells you what ran; the failure-pattern files are your judgment about what that run revealed. Consuming the former does not change how the latter is written.

Read the full file on GitHub · 295 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 10d ago First seen · 295 lines · 84 tokens per session scan A 20162871076c

Subscribe to this mod's changes

session-learning is a skill published in the GitHub repository JakubMikolajek/codex-skills-collection (5 stars, last pushed 4d ago), licensed MIT. It adds 84 tokens to every session and 2,851 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

media-ingest

Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.

garrytan/gbrain · 52 tokens

mem0-oss-to-platform

Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…

mem0ai/mem0 · 273 tokens

Cortex

Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…

danielmiessler/LifeOS · 196 tokens

memory

Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.

opensquilla/opensquilla · 44 tokens

ha-data-stores

Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…

shiwenwen/hope-agent · 115 tokens

establishing-project-context

Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.

GanyuanRan/Aegis · 45 tokens