Actualis

Reference

What it detects

Three detectors run over your transcripts. All are deterministic — no model is in the loop, and a command either matches a rule or it does not. The same input gives the same output on every run.

Credentials

Credential material appearing in recorded shell commands, grouped by fingerprint. The value is never stored or printed; id is sha256[:8] of it, so the same secret reused two hundred times counts once.

TypePriorityShape
Stripe keycriticalsk_live_…, rk_live_…
AWS access keycriticalAKIA…, ASIA…
Anthropic keycriticalsk-ant-…
OpenAI keycriticalsk-…
JWT / service keycriticaleyJ….…
GitHub PAThighghp_, gho_, ghu_, ghs_, ghr_, github_pat_
Google API keyhighAIza…
Slack tokenhighxoxb-, xoxp-, xoxa-, …
GitLab PAThighglpat-…
Vercel tokenhighvcp_…
DigitalOceanhighdop_v1_…
HuggingFacehighhf_…

Detection is pattern-based, so it will miss things. It recognises the shapes of well-known credentials. A bespoke internal token with no distinguishing shape can pass unnoticed. Treat a clean report as “nothing matched”, not as “nothing is there”.

Shell audit

Nine deterministic categories over every recorded command, tuned to roughly a 4% flag rate by deleting rules as much as adding them. Categories include destructive filesystem operations, privilege escalation, remote code execution, credential handling and network reach.

Each flagged command is shown with the permission mode in force at the time, and every denial is recorded separately.

Coach findings

Eleven findings, each with evidence, an action, and an impact estimate where one is computable. Benchmarked against your own history, so no telemetry is needed and they work with a single user.

IDFinding
AF001Spend is concentrated in one project
AF002Cache efficiency below your own median
AF003Most agent activity is unsupervised
AF004Critical credentials sit in plaintext history
AF005A credential has been exposed for a long time
AF006The agent is being corrected more in one project
AF007Spend moved sharply week over week
AF008Everything runs at premium reasoning effort
AF009One ticket cost far more than your typical ticket
AF010A large share of spend is not attributable to a ticket
AF011Shell activity is partly invisible to the audit

Ask why any of them fired against your data:

$ actualis --why AF004