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 felipesauer/mnema --skill diagnosing-recordinggit clone --depth 1 https://github.com/felipesauer/mnemaWrote 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/felipesauer/mnema/diagnosing-recording)<a href="https://agentmods.dev/skills/felipesauer/mnema/diagnosing-recording"><img src="https://agentmods.dev/badge/skills/felipesauer/mnema/diagnosing-recording/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/felipesauer/mnema/diagnosing-recording"><img src="https://agentmods.dev/badge/skills/felipesauer/mnema/diagnosing-recording.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 | $0.00035 | $0.00877 |
| Opus 5.5 | $0.00014 | $0.00351 |
| Sonnet 5.5 | $0.00007 | $0.00175 |
| Haiku 4.5 | $0.00003 | $0.00088 |
Grade A, and why
diagnosing-recording 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 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.
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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnosing recording
A session that settled a choice and recorded nothing is evidence for the next row of the table in
recording-decisions. This reads the host's transcripts of the session and says what happened,
citing the line it says it from. A finding with no line is dropped.
What the transcripts are, and where
The host writes one JSON object per line. Claude Code keeps them under ~/.claude/projects/, or
under $CLAUDE_CONFIG_DIR/projects/ where that is set, in a directory it names by flattening the
path it started in. That name is lossy — /, . and - all become -, so a sibling project can
share a prefix — so do not pick a session by directory name. A transcript belongs to a project when
the cwd recorded on its lines is the project's root or under it. packages/code/src/transcripts.ts
in the mnema repository is the product's own reader of this store and the reference for each fact
below; it reads token counts, never words, and this skill is the one place that reads words, on
purpose and for one question.
- A session is one main transcript and the transcripts of its subagents nested beside it. The
session's id is on the lines (
sessionId), not in the file name. - A line per content block: one assistant message is several lines, and a tool call is its own
line. Count a message once, by its
message.id. - A person's prompt is a
userline whose content is text, withoriginhumanwhere the host writes one. Tool results areuserlines too; so are injected text (isMeta) and notifications.
What to do
- Find the session: the project's transcripts, newest first, or the one the person names.
- Number the lines:
grep -nover the file gives every finding its line. Every claim below is a<transcript path>:<line>. - Lay out the timeline, with the first line of each:
- where a choice was settled: an assistant line that picks between approaches, a library, a trade-off, or a person's prompt that corrects or directs;
- where the project's record was read: tool calls whose name ends in
governing_rules,search,read_recordorbootstrap; - where it was written to: tool calls whose name ends in
record_decision,capture_memoryorrecord_observation. In Claude Code the name carries a prefix,mcp__plugin_mnema_mnema__ormcp__mnema__; match on the end.
- For each choice with no write after it, say which of these it was, with the lines: never weighed as a decision; weighed and not recorded; recorded somewhere else (the host's own memory, a file); recorded by a subagent, which hands decisions back instead of recording them.
- Where the agent said why it did not, quote the sentence and match it against the Red Flags table
of
recording-decisions. A sentence that matches no row is a candidate for a new one, with this line as its evidence. A row is not added or rewritten without a result from before and after.
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 First seen · 61 lines · 35 tokens per session scan A 139a75f8f1ea
diagnosing-recording is a skill published in the GitHub repository felipesauer/mnema (4 stars, last pushed yesterday), licensed Apache-2.0. It adds 35 tokens to every session and 877 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-03.
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