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 skills add JakubMikolajek/codex-skills-collection --skill session-learninggit clone --depth 1 https://github.com/JakubMikolajek/codex-skills-collectionWrote 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/jakubmikolajek/codex-skills-collection/session-learning)<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.
<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>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 | $0.00084 | $0.02851 |
| Opus 5 | $0.00042 | $0.01425 |
| Sonnet 5 | $0.00017 | $0.00570 |
| Haiku 4.5 | $0.00008 | $0.00285 |
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.
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
/implementor/reviewtask - After
/debugwhen a root cause reveals a missing or incorrect skill section - After
/multi-repowhen 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/skillswhen the repository is bootstrapped under.codex - Use
skillswhen 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/completionevents (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.mdin 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.mdhistory 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.
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.
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.
- 10d ago First seen · 295 lines · 84 tokens per session scan A 20162871076c
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.
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