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 skills add Vortx-AI/emem --skill emem-transparency-loggit clone --depth 1 https://github.com/Vortx-AI/ememWrote 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/skills/vortx-ai/emem/emem-transparency-log)<a href="https://agentmods.dev/skills/vortx-ai/emem/emem-transparency-log"><img src="https://agentmods.dev/badge/skills/vortx-ai/emem/emem-transparency-log/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vortx-ai/emem/emem-transparency-log"><img src="https://agentmods.dev/badge/skills/vortx-ai/emem/emem-transparency-log.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00113 | $0.01555 |
| Opus 5.5 | $0.00045 | $0.00622 |
| Sonnet 5.5 | $0.00023 | $0.00311 |
| Haiku 4.5 | $0.00011 | $0.00155 |
Grade A, and why
emem-transparency-log scanned grade A with 1 finding 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 13d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sf https://emem.dev/v1/log/sth > old_sth.json How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
emem-transparency-log
Network use. The commands in this skill send and receive JSON (and, where a step says so, an image or a raster file) to
https://emem.devonly, the service the plugin's MCP server connects to. Nothing they download is executed. The files underscripts/read local files and make no network calls.
A receipt proves the responder signed some fact cids. It cannot prove the responder showed everyone the same history. The transparency log does: every attestation and every memory write is a leaf in one RFC 6962 Merkle tree, and the responder signs its head. Pin a head today, and tomorrow you can prove the log only grew.
| Call | MCP | Gives |
|---|---|---|
GET /v1/log/sth |
emem_log_sth |
the signed tree head: tree_size, root_b32, signed_at, signature |
GET /v1/log/inclusion?leaf_index=<i> or ?entry_hash=<b32> |
emem_log_inclusion |
the audit path from one leaf to the current root |
GET /v1/log/consistency?first=<n>&second=<m> |
emem_log_consistency |
the proof that the size-n tree is a prefix of the size-m tree |
GET /v1/log/witnesses |
emem_log_witnesses |
independent co-signatures over (tree_size, root) |
GET /v1/log/entries?start=<i>&end=<j> |
the raw entries, so a third party can read what else is in the tree |
All reads, no key. The tree hashes with BLAKE3: leaf =
blake3(0x00 || entry_hash), node = blake3(0x01 || left || right),
lone nodes promoted rather than duplicated.
Verify a head, an inclusion, and growth
scripts/verify_log.py ships with this skill (${CLAUDE_SKILL_DIR} is filled
in by Claude Code). It makes no network calls and imports nothing outside
the standard library except scripts/emem_crypto.py, shipped with this skill, a
plain-Python BLAKE3 and Ed25519 verifier; --self-test checks that
module against the official test vectors. Save each response to a file
and pass the file:
V="${CLAUDE_SKILL_DIR}/scripts/verify_log.py"
curl -sf https://emem.dev/v1/log/sth > old_sth.json
python3 "$V" sth old_sth.json
curl -sf "https://emem.dev/v1/log/inclusion?leaf_index=12345" > inc.json
python3 "$V" inclusion inc.json
# later: a new head, and the proof that links the two
curl -sf https://emem.dev/v1/log/sth > new_sth.json
OLD=$(jq .sth.tree_size old_sth.json); NEW=$(jq .sth.tree_size new_sth.json)
curl -sf "https://emem.dev/v1/log/consistency?first=$OLD&second=$NEW" > cons.json
python3 "$V" consistency old_sth.json new_sth.json cons.json
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago Changed a1cec2bac4f2
- 1mo ago Changed · +2 lines 657f42b7973f
- 1mo ago First seen · 109 lines · 113 tokens per session scan A 1b988fa33e3d
emem-transparency-log is a skill published in the GitHub repository Vortx-AI/emem (76 stars, last pushed today), licensed Apache-2.0. It adds 113 tokens to every session and 1,555 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-29.
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