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/pcamarajr/personal-knowledge/eval-memorynpx skills add pcamarajr/personal-knowledge --skill eval-memorygit clone --depth 1 https://github.com/pcamarajr/personal-knowledgeWhat 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 | $0.00104 | $0.02373 |
| Opus 5 | $0.00052 | $0.01187 |
| Sonnet 5 | $0.00021 | $0.00475 |
| Haiku 4.5 | $0.00010 | $0.00237 |
Grade A, and why
eval-memory 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 yesterday.
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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
eval-memory — memory system health & efficacy runner
You are the runner. This skill is a procedure you execute live, mostly quiet, pausing to converse only where the user's judgment matters (the verdict and the dashboard gap). Default runs are read-only and safe to run unattended.
The one idea that makes this non-trivial
Every autolearn signal is self-referential: co-access feeds re-rank, which shapes what co-surfaces, which feeds co-access. So "the numbers are growing" proves activity, not improvement — it can be a self-confirming loop. The only way to claim "it's working" is a ground-truth judge external to the signal loop. That judge is you, ruling blind on whether re-ranked retrieval beats raw retrieval. Protect that blindness or the whole eval is theater.
Config (read, don't hardcode elsewhere)
- Server base URL:
http://127.0.0.1:7345(fromMEMORY_HTTP_HOST/MEMORY_HTTP_PORTin.env). - Repo data dir:
data/(gitignored). Key files:search-history.json,auto-signals.json,.autolearn-state.json,autolearn.config.json. - Frozen probe set:
eval/probe-set.json(local, gitignored — copy fromeval/probe-set.example.jsonand author your own; the controlled ruler — see Q4 below). - Outputs:
eval/ledger.jsonl(trend, append-only) andeval/reports/<YYYY-MM-DD>.md(narrative).
Procedure
Phase 0 — Setup (quiet)
- Read
eval/probe-set.json. If missing, stop and tell the user to seed it (see "Seeding" below). GET /health/detail. Recordautolearn_level,totals,last_flip_at,last_backup_*, and whetherlearning_statsis present.- Read the last few rows of
eval/ledger.jsonl(if any) for trend comparison.
Phase 1 — Health (quiet batch, Q9/B)
Collect, don't narrate each item. Three layers:
- Plumbing:
/healthreachable;/health/detail.autolearn_level== expected (activationper current.env);totalssane vs ledger;learning_statspresent iff level==activation. - Freshness (high-value — catches a silently-dead 60s flush timer): is
auto-signals.jsonandsearch-history.jsonmtime / newest-entrytsrecent relative to known activity? A stale file atactivationlevel is a red flag — learning has quietly stopped while everything looks fine. - Corpus hygiene: run
analyze_subjecton the top live subjects (your most active projects and the memory system) and/or scan totals for: orphans, untagged,statepastexpected_until, decisions-without-outcomes, contradictions, and obvious noise (test/tombstone/probe records). Produce a compact health summary with PASS/WARN/FAIL per check.
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.
- yesterday First seen · 146 lines · 104 tokens per session scan A 8bc362150fbd
eval-memory is a skill published in the GitHub repository pcamarajr/personal-knowledge (0 stars, last pushed 2mo ago), licensed MIT. It adds 104 tokens to every session and 2,373 once invoked, about $0.0005 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 2026-08-31.
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