Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Hainrixz/aprende-skill/plugin install aprendeWrote 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/hainrixz/aprende-skill/aprende)<a href="https://agentmods.dev/skills/hainrixz/aprende-skill/aprende"><img src="https://agentmods.dev/badge/skills/hainrixz/aprende-skill/aprende.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 | $0.00195 | $0.07124 |
| Opus 5 | $0.00097 | $0.03562 |
| Sonnet 5 | $0.00039 | $0.01425 |
| Haiku 4.5 | $0.00019 | $0.00712 |
Grade C, and why
aprende scanned grade C with 2 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
| `/aprende-enable-hooks` | Install PostToolUse + Stop hooks into `~/.claude/settings.json`. | Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
2. For `skill` candidates, `ls ~/.claude/skills/` and `ls ./.claude/skills/` How it starts
The opening of the file, as written. The whole thing — 621 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aprende — Learn from this conversation / Aprende de esta conversación
EN — A skill that turns finished conversations into durable, structured learnings: memories, anti-patterns (Reflexion-style), skill stubs, and project-doc updates. Confirmation-first. Never auto-writes.
ES — Un skill que convierte conversaciones terminadas en aprendizajes durables y estructurados: memorias, anti-patrones (estilo Reflexion), stubs de skills, y actualizaciones a project-docs. Confirmación primero. Nunca escribe automáticamente.
1. Purpose / Propósito
EN. Coding agents repeat mistakes across sessions because the corrections a
user makes in one conversation evaporate when the session ends. aprende
fixes that. When invoked, it reviews the current conversation, identifies what
is worth preserving across four well-defined categories, and writes those
learnings to the right files — in the format the user's existing memory
system already reads — only after the user picks the items to keep.
Guiding principle: a false positive locked into memory is worse than repeating a correction three times. Be liberal at surfacing candidates, strict at confirming them, and conservative at writing them. Prefer false negatives.
ES. Los agentes de código repiten errores entre sesiones porque las
correcciones que el usuario hace en una conversación se evaporan cuando esa
sesión termina. aprende arregla eso. Al activarse, revisa la conversación
actual, identifica qué vale la pena preservar a través de cuatro categorías
bien definidas, y escribe esos aprendizajes en los archivos correctos — en el
formato que ya usa el sistema de memoria del usuario — solo después de que el
usuario elija qué guardar.
Principio rector: un falso positivo cristalizado en la memoria es peor que repetir una corrección tres veces. Sé liberal al proponer candidatos, estricto al confirmarlos, y conservador al escribirlos. Prefiere los falsos negativos.
2. When to invoke / Cuándo activarse
What ships with it
6 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.
- 6d ago First seen · 621 lines · 195 tokens per session scan C 5cfc70d30180
aprende is a skill published in the GitHub repository Hainrixz/aprende-skill (23 stars, last pushed 1mo ago), licensed MIT. It adds 195 tokens to every session and 7,124 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it C with 2 findings (reads agent configuration directories, enumerates other installed skills). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
retrospective
Run a blameless retrospective — gather a timeline, generate insights, decide a few owned dated actions, and feed lessons into CONTINUITY.md memory.
hindsight-local
Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user).
handoff
Resume the most recent agent session for the current working directory, leading with any unanswered question. Use when the user says "where were we", "resume", "handoff", "pick up where I left off", or starts a session with no fresh context.
agentmemory-hooks
The agentmemory plugin hooks that capture observations automatically across the agent session lifecycle. Use when explaining how memory gets captured without manual saves, when debugging missing observations, or when tuning what gets recorded.
agentmemory-agents
How agentmemory wires into host coding agents via the connect command. Use when installing agentmemory into a specific agent, when asked which agents are supported, or when a connect adapter writes the wrong config path.
last30Days
Resolve "last30Days" to a concrete ISO date range relative to your run time — a rolling 30-day window ending today. Returns inclusive civil dates plus exact UTC instants so you have temporal context without computing dates by hand. Read-only: no writes, no network. Use before a "last 30 days" / trailing-month task…