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 agentmods add skills/lykhoyda/rn-dev-agent/list-learned-actionsnpx skills add Lykhoyda/rn-dev-agent --skill list-learned-actionsgit clone --depth 1 https://github.com/Lykhoyda/rn-dev-agentWrote 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/lykhoyda/rn-dev-agent/list-learned-actions)<a href="https://agentmods.dev/skills/lykhoyda/rn-dev-agent/list-learned-actions"><img src="https://agentmods.dev/badge/skills/lykhoyda/rn-dev-agent/list-learned-actions.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 | $0.00029 | $0.00245 |
| Opus 5 | $0.00015 | $0.00122 |
| Sonnet 5 | $0.00006 | $0.00049 |
| Haiku 4.5 | $0.00003 | $0.00024 |
Grade A, and why
list-learned-actions 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 5d 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.
This is a copy
81% identical to build-and-test — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
list-learned-actions
Invoke this workflow explicitly as $rn-dev-agent:list-learned-actions [request text].
The exact text after the skill mention is the conceptual request. It is user-message data, not a shell variable or a Claude command-template substitution. Preserve it while applying the workflow's documented grammar; pass only separately parsed and validated values to MCP tools or package helpers. Never use eval or interpolate the raw request into a shell command.
Read the complete package-local workflow before acting. Resolve that file and every helper relative to this exact SKILL.md path; never scan Codex caches or rely on a plugin-root environment variable. If a required cdp MCP tool is absent from the active task, stop and use the read-only discovery diagnosis. Do not substitute raw Maestro for rn-dev-agent strict proof.
What ships with it
1 file 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.
- 5d ago First seen · 14 lines · 29 tokens per session scan A a92c34079ffd
list-learned-actions is a skill published in the GitHub repository Lykhoyda/rn-dev-agent (11 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 245 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 81% identical to build-and-test, differing in 10 lines, and is treated as a copy.
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