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/letta-ai/letta-code/submitting-feedbacknpx skills add letta-ai/letta-code --skill submitting-feedbackgit clone --depth 1 https://github.com/letta-ai/letta-codeWrote 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/letta-ai/letta-code/submitting-feedback)<a href="https://agentmods.dev/skills/letta-ai/letta-code/submitting-feedback"><img src="https://agentmods.dev/badge/skills/letta-ai/letta-code/submitting-feedback.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 | $0.00087 | $0.00551 |
| Opus 5 | $0.00044 | $0.00275 |
| Sonnet 5 | $0.00017 | $0.00110 |
| Haiku 4.5 | $0.00009 | $0.00055 |
Grade A, and why
submitting-feedback 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 today.
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.
What it actually says
Submitting Feedback
Use this skill for product and developer feedback about Letta Code: reproducible bugs, broken features, confusing product behavior, and requested changes to the software or its developer-facing behavior.
Do not use this skill when the user corrects how the current agent should behave, communicate, remember, or work with them. Treat that as learning: make the appropriate memory edit so the correction changes the agent's future behavior. A user's frustration with the agent is not by itself product feedback and is not a reason to offer feedback submission.
If the user says yes, or directly asks you to submit feedback:
-
Gather the relevant context already available in the conversation and environment. If a detail essential to understanding or reproducing the problem is missing, ask the user one focused question before submitting. Do not invent missing details.
-
Write a concise, factual report in your own voice as the agent. Do not impersonate the user or make the report sound user-authored. The first line must disclose:
Agent-submitted feedback on behalf of the user.Include:
- Your agent name.
- Who you are working with (the user's name or role, if known; otherwise say
user not identified). - The task or goal underway when the problem occurred and enough surrounding context to understand why it mattered.
- What actually happened, what the user expected, and the impact on the task.
- Concrete evidence already available, such as exact error text, the failed command or action, relevant paths or links, and reliable reproduction steps. Distinguish what the user reported from what you observed or inferred.
Prefer specific nouns and observable behavior over generic judgments. Do not submit context-free summaries such as “the feature is broken,” “the UX should be improved,” or polished product-language filler. Keep unknowns explicit rather than guessing.
-
Submit it with:
letta feedback --message '<feedback>'
- Tell the user whether submission succeeded. If it failed, report the safe CLI error and do not claim the team received it.
Do not include secrets, credentials, unrelated conversation content, or private file contents. The command adds the current agent and conversation identifiers so the team can find the relevant run.
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.
- today Changed · +2 lines · +22 tokens per session c322f02403e8
- 2d ago Changed · +10 lines d6144d4d89fb
- 4d ago First seen · 22 lines · 65 tokens per session scan A 37a83a4d6fec
submitting-feedback is a skill published in the GitHub repository letta-ai/letta-code (3,193 stars, last pushed today), licensed Apache-2.0. It adds 87 tokens to every session and 551 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 2026-08-30.
Other skills, from other repositories
memanto-companion
Inspect and manage the cross-session engineering memory that Memanto maintains for your Claude Code skills. Use when the user asks what Memanto remembers, wants to see their engineering profile, manually recall context for a skill, or store a decision. The automatic lifecycle hooks handle capture/injection on their…
memory-recall
Search and recall relevant memories from past sessions via memsearch. Use when the user's question could benefit from historical context, past decisions, debugging notes, previous conversations, or project knowledge -- especially questions like 'what did I decide about X', 'why did we do Y', or 'have I seen this…
ov-experience-memory
Retrieve and apply OpenViking Experience memories through the Agent runtime's generic OpenViking search and read tools. Use before or during executable, multi-step, or tool-based work such as coding, file or data changes, configuration, deployment, workflow execution, and failure recovery when prior operational…
mnemon
Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.
prolong
Recover and use durable coding-session history from PRO-LONG's local append-only log. Use on long-running coding tasks, after context compaction or session resume, when reconstructing prior decisions or tool results, or before repeating work that may already have been attempted.
prep
Session wrap-up. Update memories, check plans, review git state, check inbox, flag loose ends. Use before closing a session or compacting context.