Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jordanhindo/lorenpx agentmods add skills/jordanhindo/lore/lore-recallWrote 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/jordanhindo/lore/lore-recall)<a href="https://agentmods.dev/skills/jordanhindo/lore/lore-recall"><img src="https://agentmods.dev/badge/skills/jordanhindo/lore/lore-recall/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/jordanhindo/lore/lore-recall"><img src="https://agentmods.dev/badge/skills/jordanhindo/lore/lore-recall.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 | $0.00068 | $0.00754 |
| Opus 5 | $0.00034 | $0.00377 |
| Sonnet 5 | $0.00014 | $0.00151 |
| Haiku 4.5 | $0.00007 | $0.00075 |
Grade A, and why
lore-recall 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 8d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lore Recall
Use this workflow when past session memory matters. It sits above the low-level skills/lore CLI skill: use skills/lore for command mechanics and setup/indexing, use this skill for evidence planning, trust, freshness, and final packet shape.
Quick Start
- Run
lore status --jsonwith the narrowest useful scope (--project,--source,--since,--until) before retrieval. - Build a recall plan: 2-4 query variants, likely filters, and why each query exists.
- Search narrowly first:
lore search "<terms>" --json, then--relevantwhen recency matters. - Drill down only from real ids returned by Lore:
lore get <id> --full,lore context <id> --json, orlore session <session> --around <id> --json. - Emit a bounded evidence packet using
references/evidence-packet.md. - If retrieval fails, report the gap and next queries tried. Do not invent memory.
Trust Rules
- Lore transcripts are testimony, not truth. Current files, tests, runtime artifacts, and live system state beat stale transcript claims.
- Never dump a whole session. Use search, one full message, or a bounded context/window.
- Never invent ids. Every message/session id must come from Lore output.
- Preserve provenance: message id, session id, source, project, branch, timestamp, role/model when known.
- Unknown source data stays
nullor "unknown". Do not infer branch, project, model, or freshness. - Real transcript excerpts are private. In committed examples/evals use synthetic ids and text only.
Freshness Labels
Use labels from references/freshness.md:
current: corroborated by current files/tests/runtime.recent: transcript timestamp is plausibly relevant and no contradiction found.stale: older memory may have drifted or current artifacts disagree.unknown: missing timestamp or insufficient provenance.
Failure Recovery
If status says missing_store, empty_store, source_absent, possibly_unsynced, stale_schema, or unreadable_store, stop and report the status plus recovery. If schemaVersion is greater than supportedSchemaVersion, say explicitly that read-only retrieval may work but write recovery such as lore sync can be refused until Lore is updated. If search returns zero hits, broaden once, try one synonym query, and report gaps instead of pretending.
What ships with it
9 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.
- evals/evals.json 2.1 KB
- evals/test-report.md 4.4 KB
- examples/bad-anti-pattern.md 324 B
- examples/failed-retrieval-packet.json 497 B
- examples/good-evidence-packet.json 1.1 KB
- references/evidence-packet.md 1.5 KB
- references/freshness.md 496 B
- references/query-planning.md 667 B
- scripts/validate-evidence-packet.mjs 2.7 KB runs code
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.
- 8d ago First seen · 60 lines · 68 tokens per session scan A fc9f528c8783
lore-recall is a skill published in the GitHub repository jordanhindo/lore (13 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 754 once invoked, about $0.0003 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 2026-08-30.
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