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 janbjorge/rekal --skill rekal-savegit clone --depth 1 https://github.com/janbjorge/rekalWrote 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/janbjorge/rekal/rekal-save)<a href="https://agentmods.dev/skills/janbjorge/rekal/rekal-save"><img src="https://agentmods.dev/badge/skills/janbjorge/rekal/rekal-save/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/janbjorge/rekal/rekal-save"><img src="https://agentmods.dev/badge/skills/janbjorge/rekal/rekal-save.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00079 | $0.01183 |
| Opus 5.5 | $0.00032 | $0.00473 |
| Sonnet 5.5 | $0.00016 | $0.00237 |
| Haiku 4.5 | $0.00008 | $0.00118 |
Grade A, and why
rekal-save 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 2mo 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Save durable knowledge from this session into rekal. Goal: user never repeats themselves across sessions.
Step 1: Extract candidates
Review the conversation. Per item, apply this filter:
Would a fresh agent in a new session benefit from knowing this?
├── YES → candidate
└── NO → skip
Candidate types:
| What | Example |
|---|---|
| Preference with reasoning | "User prefers dataclasses over hand-written __init__ for less boilerplate" |
| Architecture/convention | "Auth service uses JWT, lives in services/auth, 15-min token expiry" |
| Decision + why | "Chose PostgreSQL over MySQL for JSONB support and better partial indexes" |
| Procedure | "Deploy: 1) git tag vX.Y.Z 2) git push --tags 3) wait CI 4) merge to main" |
| Bug with non-obvious cause | "OOM from unbounded LRU cache in parser, fixed with maxsize=1000" |
| Behavior correction | "Never use grep/find. Use rg/fd. Strict, no exceptions." |
Skip (do not store):
- Transient state: "currently editing main.py", "tests passing now"
- Trivially re-discoverable: "function foo is on line 42", "file has 200 lines"
- Too vague: "user likes clean code", "project uses Python"
- Session mechanics: "user asked me to fix a bug", "we discussed testing"
- Secrets, API keys, passwords, tokens: never
If zero candidates survive, stop here. Do not force-store.
Step 2: Deduplicate each candidate
For EVERY candidate, before storing:
memory_build_context(query="<candidate topic in natural language>")
Read results. Apply:
Recall returned results?
├── NO match at all
│ └── Proceed to step 3 (store new)
│
├── Same topic, same info (duplicate)
│ └── SKIP. Do not store.
│
├── Same topic, new/updated info
│ └── memory_store(content="<updated content>", replaces="<matched memory id>")
│
└── Same topic, contradictory info
└── memory_store(content="<corrected content>", replaces="<matched memory id>")
Include what changed and why in the content.
Critical rule: Two memories about the same topic must never coexist.
replaces supersedes the older memory so it stops surfacing. "User's
preferred formatter" appears exactly once in the database.
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.
- 2mo ago First seen · 140 lines · 79 tokens per session scan A 7f3f6ea97745
rekal-save is a skill published in the GitHub repository janbjorge/rekal (53 stars, last pushed 2mo ago), licensed MIT. It adds 79 tokens to every session and 1,183 once invoked, about $0.0003 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 2026-08-30.
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