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 commands/lykhoyda/rn-dev-agent/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/commands/lykhoyda/rn-dev-agent/list-learned-actions)<a href="https://agentmods.dev/commands/lykhoyda/rn-dev-agent/list-learned-actions"><img src="https://agentmods.dev/badge/commands/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 | $0.00051 | $0.01290 |
| Opus 5 | $0.00026 | $0.00645 |
| Sonnet 5 | $0.00010 | $0.00258 |
| Haiku 4.5 | $0.00005 | $0.00129 |
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 4d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
List learned actions matching: $ARGUMENTS (optional keyword filter; omit to list all)
Why this command exists
Past sessions accumulate two kinds of reusable knowledge that future sessions should look at BEFORE re-deriving anything from scratch:
- Feedback memories at
~/.claude/projects/<encoded-cwd>/memory/feedback_*.md - Executable artifacts:
.rn-agent/actions/*.yamland*.yml,.rn-agent/skeleton.yaml
This command surfaces both lists in one place — and the underlying
packages/rn-dev-agent-core/src/learned-actions.ts (compiled to packages/rn-dev-agent-core/dist/learned-actions.js) is the same script invoked programmatically
by /rn-dev-agent:test-feature Step 0 and by the rn-tester / rn-debugger
agents' artifact-scan steps. Keeping the discovery logic in one script means
every consumer sees the same inventory.
Run
node "${CLAUDE_PLUGIN_ROOT}/rn-dev-agent-core/dist/learned-actions.js" \
--workspace-root "$PWD" \
--memory-cwd "$PWD" \
${ARGUMENTS:+--filter "$ARGUMENTS"}
The script auto-discovers:
- The user's per-project auto-memory directory (Section A)
.rn-agent/actions/*.yamland*.ymlin the cwd, in<cwd>/test-app/, and in any<sibling>/test-app/adjacent to the cwd (Section B).rn-agent/skeleton.yamlin the same locations (Section C)- Plugin commands (Section D — only populated when running inside the plugin repo)
It exits 0 when results found, 3 when nothing matches, 2 on bad flags.
Programmatic invocation
Other commands and agents can call the script directly to get JSON for
decision-making. Always use --json from a programmatic caller — the
human table format is not a stable contract.
RESULT=$(node "${CLAUDE_PLUGIN_ROOT}/rn-dev-agent-core/dist/learned-actions.js" \
--json --section b --filter "task creation" --workspace-root "$PWD" --memory-cwd "$PWD")
echo "$RESULT" | jq '.sections.flows.items[0].path'
JSON shape (abridged):
{
"cwd": "/Users/.../my-rn-app",
"filter": "cart",
"sections": {
"memories": { "count": 0, "dir": "...", "items": [] },
"flows": {
"count": 1,
"roots": ["..."],
"items": [
{
"flow": "cart-add-item",
"path": "/.../cart-add-item.yaml",
"appId": "com.example.app",
"purpose": "Add an item to the cart and verify the badge increments",
"params": ["ITEM_ID", "QTY"],
"replay": "display-only legacy command; execute through cdp_run_action"
}
]
},
"skeletons": { "count": 1, "items": [...] },
"commands": { "count": 16, "items": [...] }
},
"total": 18
}
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.
- 4d ago First seen · 124 lines · 51 tokens per session scan A 5db40b6a6d2f
list-learned-actions is a command published in the GitHub repository Lykhoyda/rn-dev-agent (11 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 1,290 once invoked, about $0.0003 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 2026-08-30.
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