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 LucasSantana-Dev/sharekit --skill knowledge-loopgit clone --depth 1 https://github.com/LucasSantana-Dev/sharekitWrote 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/lucassantana-dev/sharekit/knowledge-loop)<a href="https://agentmods.dev/skills/lucassantana-dev/sharekit/knowledge-loop"><img src="https://agentmods.dev/badge/skills/lucassantana-dev/sharekit/knowledge-loop/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/lucassantana-dev/sharekit/knowledge-loop"><img src="https://agentmods.dev/badge/skills/lucassantana-dev/sharekit/knowledge-loop.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 | $0.00084 | $0.01550 |
| Opus 5.5 | $0.00034 | $0.00620 |
| Sonnet 5.5 | $0.00017 | $0.00310 |
| Haiku 4.5 | $0.00008 | $0.00155 |
Grade A, and why
knowledge-loop 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 1mo 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Loop
Unifies the three knowledge systems (RAG index, claude-mem, handoffs) into one workflow so capture and retrieval stop being separate manual acts.
Auto-invocation triggers
- User asks "what did we decide about X" / "where did we leave Y" / "is there a memory note for Z"
- End of a meaningful task (commit landed, PR merged, decision reached)
- User explicitly says "remember", "save this", "checkpoint", "handoff"
- Session-budget guard signals approaching context limit
Workflow
Mount guard (required before any RAG/brain op — standards/skill-patterns.md §mount-guard):
run references/mount-guard.sh — if External HD is unmounted,
surface BLOCKED: External HD unmounted — RAG/vault unreachable and halt; do not return
empty recall as if the index were searched.
Phase 1 — Query (always)
Invoke /recall (MCP: rag_query(query="<topic>", top=5)) with the user's question
or the active task topic. For which knowledge source to route to, recall is canonical —
see references/recall-routing.md. If the user is asking a
recall question, return the answer immediately and skip Phase 2/3 unless they also asked to
capture something.
Done when: recall returns {hit_count: N, top_cosine: X, source: [memory|handoff|commit|code]} — confirm top result answers your question (cosine ≥0.50) or increase top up to 8.
Phase 2 — Capture (if new knowledge produced)
Invoke /sync-memories with what was learned, decided, or built this session. Skip if
the session was pure read/recall with no durable output. To classify what artifact to
write (memory vs committed doc) and which tags apply, follow the decision tree in
references/graduation-gate.md.
Done when: all memory files written to ~/.claude/memory/ (or project-local memory/) and registered in memory index — confirm via file listing + MEMORY.md pointer.
Phase 3 — Improve (conditional)
If recall returned weak hits (cosine <0.40) for a query that should have hit something,
invoke /rag-curate to add the missing doc or rewrite the weak chunk. Skip if recall
was strong.
What ships with it
5 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.
- 1mo ago First seen · 125 lines · 84 tokens per session scan A cdf0bcf17db4
knowledge-loop is a skill published in the GitHub repository LucasSantana-Dev/sharekit (1 stars, last pushed 3d ago), licensed MIT. It adds 84 tokens to every session and 1,550 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 2026-09-03.
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