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/nel-neru/libraium/source-command-receptionnpx skills add nel-neru/LibrAIum --skill source-command-receptiongit clone --depth 1 https://github.com/nel-neru/LibrAIumWhat 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 | $0.00015 | $0.00821 |
| Opus 5 | $0.00008 | $0.00411 |
| Sonnet 5 | $0.00003 | $0.00164 |
| Haiku 4.5 | $0.00002 | $0.00082 |
Grade A, and why
source-command-reception 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
source-command-reception
Use this skill when the user asks to run the migrated source command reception.
Command Template
Draft each entry's ## Reception — synthesized third-party signal (what the community reports: complaints, adopters, limitations, migration, maturity), not the owner's firsthand experience. This replaces the retired /confirm-notes: the owner is a curator, not a hands-on user of most entries, so there is no firsthand experience to confirm — honesty comes from sourcing every claim. One run covers ~3 entries.
Converse in Japanese (per AGENTS.md); keep the entry files themselves in English.
1. Pick the next batch
Read .Codex/reception-review.md. Take the first 3 unchecked (- [ ]) entries in file order. If none remain, tell the owner every entry has Reception and stop.
Show current coverage — node scripts/curation-report.mjs prints it in the reception line, or count the boxes directly. Batch is 3, not 5: each entry costs several gh calls and gh search has a ~30/min budget.
2. Gather evidence (this replaces the interview)
First export GITHUB_TOKEN=$(gh auth token) (clears the anonymous rate limit). Then run the read-only dossier for the batch:
node scripts/reception-scan.mjs --json --only <entry-id> # once per entry
It gathers — GitHub-only and writing nothing — the most-reacted issues (complaints/limitations), release cadence + open-issue count (maturity), and README adopter links. Read each dossier as your source material.
General web is an explicit opt-in. If a dossier is thin (few issues, no adopters), you MAY WebSearch for migration/positioning signal ("migrated from X to Y", "X vs Y", " in production") — but announce it first, since it reaches beyond GitHub. Never invent signal; where evidence is thin, write "limited public signal" rather than fabricate.
3. Draft in house style
Follow the entry-authoring skill's "Reception voice". For each entry, write the ## Reception section:
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 First seen · 55 lines · 15 tokens per session scan A 1c276c62772d
source-command-reception is a skill published in the GitHub repository nel-neru/LibrAIum (0 stars, last pushed 5d ago), licensed MIT. It adds 15 tokens to every session and 821 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 skills, from other repositories
shodh-memory
Persistent memory system for AI agents. Use this skill to remember context across conversations, recall relevant information, and build long-term knowledge. Activate when you need to store decisions, learnings, errors, or context that should persist beyond the current session.
okf-harness-bootstrap
This unified entrypoint routes setup and workspace maintenance without making the user choose a mode.
khaos-brain-open-ui
Open the local Khaos Brain desktop card browser for human review. Use when the user asks to open, show, view, inspect, or create a human-facing entry for the Khaos Brain UI, desktop app, card browser, or Windows shortcut. Do not use this for AI KB retrieval or feedback; use the predictive KB retrieval workflow for…
local-kb-retrieve
Retrieve relevant entries from the local predictive knowledge base as a lightweight preflight for repository work. Use route-first retrieval: infer the task direction, then search by domain path and cross-index before relying on flat keyword matching. In Codex, prefer a scout sidecar before non-trivial work and a…
kb-organization-maintenance
Run the repository-managed Khaos Brain organization maintenance cycle. Use only when a user or automation explicitly asks to inspect, review, or maintain a validated organization KB repository and this machine has opted into organization maintenance; this is the organization exchange cycle, not ordinary local KB Sleep.
kb-sleep-maintenance
Run the repository-managed automatic local maintenance cycle. Use only for explicit local maintenance or the scheduled KB Sleep automation, not ordinary retrieval or active-task write-back.