agentleFS
ZhekinmaksimZhekinmaksim/hermes-veil/SKILL.md

veil

Skill4 starsChanged 7 months agoMITView on GitHub

Encrypt secrets and PII before they reach LLM providers via MEMORY.md. Intercept save-to-memory requests containing credentials, API keys, or personal data; replace sensitive payload with opaque reference; store real value in local age-encrypted vault. Agent resolves references locally at tool-execution time via veil_resolve.

5 other projects use a version of this.

Adopted by

Most recently taken up by

  • Other versions5 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 5

    1. BankrBot/openclaw-skills★ 1.2ksame name
    2. BankrBot/skills★ 1.2ksame name
    3. Eskyee/agentbot-opensource★ 17same name
    4. basezh/agent-skills-on-base★ 15same name
    5. andrewrgarcia/veil★ 0same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

The file

We split this skill into the rules it gives an agent

7 rules. 0 are common, 2 are distinctive, and 5 are very specific to this project.

  • 0

    Common

    Most of the 5 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Seal: Call scripts/veil.py seal with the sensitive value and a short human-readable label (e.g., pg-prod, client-anna).

    line 65
  2. Very specific:

    Write placeholder to memory: Use the memory tool to save an entry containing the placeholder instead of the plaintext.

    line 69
  3. Very specific:

    Resolve at execution time: When the agent later needs the real value to run a command or make a request, call scripts/veil.py resolve …

    line 79
  4. Very specific:

    Audit (if enabled): If veil.onchain_audit is true, every seal and rotate operation automatically appends SHA256(vault_state || timestamp) …

    line 86
  5. Distinctive:

    Never log the resolved value: If the agent prints plaintext into a chat response, a log file, or a terminal command visible to another …

    line 106
  6. Very specific:

    Vault corruption is unrecoverable: If vault.age is deleted or the passphrase is forgotten, references become dangling.

    line 116
  7. Distinctive:

    After on-chain audit publish:

    line 145

“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

VEIL — Privacy Layer for Hermes Memory

When to Use

Trigger this skill before writing to MEMORY.md or USER.md when the user's message contains any of the following categories of data:

  • Credentials: passwords, API keys, private keys, tokens, connection strings
  • Identifiers: SSH keys, TLS certs, webhook secrets, database URLs with auth
  • Personally identifiable information of third parties: full names paired with emails/phones, monetary amounts tied to named clients, addresses
  • Anything the user explicitly marks as sensitive: "private", "secret", "don't tell anyone", "between us"

Common triggers in user language:

  • "remember my X password is ..."
  • "save my API key for ..."
  • "my client Y paid Z amount"
  • "don't forget the SSH key for staging is ..."

Do not trigger for: generic environment facts ("I use Ubuntu 22.04"), public project names, the user's own first name, or tool/library preferences. These belong in plain memory.

Procedure

  1. Detect: Parse the user's remember-request. Identify the sensitive span(s). If unsure whether something is sensitive, ask the user once — do not default to saving in plaintext.
  2. Seal: Call scripts/veil.py seal with the sensitive value and a short human-readable label (e.g., pg-prod, client-anna). The script returns an opaque reference like {{veil:pg-prod-2026a}}.
  3. Write placeholder to memory: Use the memory tool to save an entry containing the placeholder instead of the plaintext.

    Good: User's Postgres prod credentials stored in VEIL: {{veil:pg-prod-2026a}}. Host: db.example.com, port 5432.

    Bad: User's Postgres prod password is hunter2.

    The non-sensitive scaffolding (host, port, which system it's for) stays in plaintext — the agent needs it to decide when to resolve.

  4. Resolve at execution time: When the agent later needs the real value to run a command or make a request, call scripts/veil.py resolve <reference>. The plaintext is returned only to the tool execution context and only on the user's machine. Never write the resolved value back to MEMORY.md or echo it in a chat response.
  5. Audit (if enabled): If veil.onchain_audit is true, every seal and rotate operation automatically appends SHA256(vault_state || timestamp) to the Solana audit log. No user action needed.

Commands

veil init                     one-time setup; creates vault, prompts for passphrase
veil seal <label> <value>     returns {{veil:label-hash}}
veil resolve <reference>      returns plaintext (local only)
veil list                     lists all references with labels, no values
veil rotate                   re-encrypt vault with new passphrase
veil migrate                  scan existing MEMORY.md, propose redactions
veil audit verify             check local vault matches latest on-chain digest
veil audit publish            manually publish current digest

Pitfalls

  • Never log the resolved value. If the agent prints plaintext into a chat response, a log file, or a terminal command visible to another user, the leak is real. Prefer piping into stdin of the target command: veil resolve pg-prod-2026a | psql -h db.example.com ...
  • Placeholders are not semantically hidden. An LLM seeing User's Postgres prod credentials: {{veil:pg-prod-2026a}} can infer the category of the secret. This is acceptable — the value is still protected — but don't claim VEIL hides the existence of credentials.
  • Vault corruption is unrecoverable. If vault.age is deleted or the passphrase is forgotten, references become dangling. Back up vault.age to an external location; it is encrypted, so backup exposure is not a leak.
  • On-chain audit is opt-in for a reason. It costs ~0.000005 SOL per operation and publicly reveals that the user updates their vault, even though it hides what. Users who don't want any network footprint should keep it off.
  • Do not mix VEIL references across machines. Each vault is a closed world. If a user copies MEMORY.md to a new machine without the matching vault.age, references cannot be resolved.

Verification

After seal:

veil list | grep <label>            reference should appear
grep -r "<plaintext>" ~/.hermes/    should return nothing

After resolve:

veil resolve <reference>            should return exact plaintext

After on-chain audit publish:

veil audit verify                   should report "OK: local digest matches chain"

References

  • references/threat-model.md — what VEIL does and does not protect against
  • references/crypto-choices.md — why age, why Argon2id, why Solana for audit
  • references/usage-examples.md — end-to-end examples for common workflows

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.