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/sk-lim19f/hypomnema/feedbackgit clone --depth 1 https://github.com/sk-lim19f/HypomnemaWrote 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/sk-lim19f/hypomnema/feedback)<a href="https://agentmods.dev/commands/sk-lim19f/hypomnema/feedback"><img src="https://agentmods.dev/badge/commands/sk-lim19f/hypomnema/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 | $0.00028 | $0.01528 |
| Opus 5 | $0.00014 | $0.00764 |
| Sonnet 5 | $0.00006 | $0.00306 |
| Haiku 4.5 | $0.00003 | $0.00153 |
Grade A, and why
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 yesterday.
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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are running /hypo:feedback. Capture a behavior correction or preference into pages/feedback/ — the single source of truth for learned behaviors.
What this does
- Creates or updates
pages/feedback/<topic>.mdwith a dated entry and full classification frontmatter - Appends a reference to
log.md - Automatically refreshes the projection into
MEMORY.mdand the user's CLAUDE.md<learned_behaviors>viafeedback-sync --write
⚠️ Do not hand-edit MEMORY.md or CLAUDE.md
<learned_behaviors>for feedback. Those are one-way projections derived from the wiki page. Edit the wiki page; the projection follows.
Step 1 — Gather feedback details
If the user did not provide them, ask. The classification fields are required so the page can project correctly:
- Topic (slug): "What topic does this feedback apply to? (e.g.
response-length,commit-style)" - Rule (entry): "State the rule or correction in one or two sentences."
- Reason: "What incident or reasoning prompted this?"
- Scope: "Does this apply globally (all projects) or to this project only?" →
global|project:<project-id>(project-id must exact-match the resolved id; see Step 3 note) - Tier: "Is this a hard rule (L1) or a softer preference (L2)?" →
L1|L2 - Targets: "Where should this project?" →
project-memory(MEMORY.md) and/orclaude-learned(global CLAUDE.md). Defaultproject-memory. - Priority (1–5, higher sorts first; default 3).
- Sensitivity:
public(default) orsanitized(redacted secrets/paths).privateis not allowed — the wiki is git-pushed. - Failure type (optional): if this correction came from a real failure incident, classify it —
hallucination|false-completion|process-stall|over-caution|overreach|incompleteness|instruction-miss|convention-violation. Omit it for a pure preference or a brand-new convention ("always do X"). When several fit, take the most specific (the list is in precedence order; see SCHEMA §3.1).
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.
- yesterday Changed 70ba899376d9
- 5d ago First seen · 88 lines · 28 tokens per session scan A 830d1a3370ff
feedback is a command published in the GitHub repository sk-lim19f/Hypomnema (10 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 1,528 once invoked, about $0.0001 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.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
session-end
I'll summarize this coding session and update the memory system with our accomplishments.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.