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/pinperepette/context-kernel/kernel-verifynpx skills add Pinperepette/context-kernel --skill kernel-verifygit clone --depth 1 https://github.com/Pinperepette/context-kernelWhat 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 | $0.00062 | $0.00665 |
| Opus 5 | $0.00031 | $0.00332 |
| Sonnet 5 | $0.00012 | $0.00133 |
| Haiku 4.5 | $0.00006 | $0.00067 |
Grade A, and why
kernel-verify 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 2d 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.
What it actually says
kernel-verify — answer-invariance in-sessione (zero chiavi)
Serve a rispondere a una sola domanda: la versione compressa/proiettata del
contesto porta alla stessa risposta di quella completa? Cioe' A(x) = A(pi(x)).
Questo controllo non usa API ne' chiavi: lo esegui tu, Claude, dentro la sessione — quindi pesa solo sull'abbonamento gia' attivo.
Input
L'utente ti fornisce (o indica dove trovarli):
- un task/domanda
Q; - il contesto completo
x; - il contesto ridotto
pi(x)(output dicompress.py, dello slicer, o di un proiettorespan(Q)).
Procedura (falla in silenzio, poi riporta solo il verdetto)
- Rispondi a
Qusando solox. Chiama questaA(x). - Rispondi a
Qusando solopi(x). Chiama questaA(pi(x)). Trattale come due contesti separati: non lasciare chex"riempia i buchi" dipi(x). - Confronta
A(x)eA(pi(x))nel merito (stessa conclusione, stessi fatti rilevanti). Ignora differenze di forma, lunghezza o parole.
Output
Riporta in modo compatto:
- INVARIANTE: si/no
- se no: quale informazione presente in
xe' andata persa inpi(x)e ha cambiato la risposta (indica l'unita'/riga mancante). - una riga di verdetto (es. "stessa risposta: 30s di timeout" oppure "persa: il limite upload del Pro, ora la risposta e' incompleta").
A cosa serve
E' il gate che trasforma la compressione euristica (conservativa, non provabile) in una misura verificata per quel task. Usalo per:
- tarare quanto puoi comprimere prima che l'invarianza si rompa (il "ginocchio"
della curva rate-distortion di
span_rd.py); - validare a campione l'output del compressore su casi delicati.
Nota: lo slicer del codice (kernel-slice) e' gia' answer-preserving per
costruzione — su quello questo controllo e' ridondante. Serve soprattutto sul
testo/documenti, dove la proiezione e' empirica.
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.
- 2d ago First seen · 53 lines · 62 tokens per session scan A 3757017fe1ed
kernel-verify is a skill published in the GitHub repository Pinperepette/context-kernel (24 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 665 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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