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 mahdisanagostar/chef --skill graph-first-retrievalgit clone --depth 1 https://github.com/mahdisanagostar/chefWrote 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/mahdisanagostar/chef/graph-first-retrieval)<a href="https://agentmods.dev/skills/mahdisanagostar/chef/graph-first-retrieval"><img src="https://agentmods.dev/badge/skills/mahdisanagostar/chef/graph-first-retrieval/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/mahdisanagostar/chef/graph-first-retrieval"><img src="https://agentmods.dev/badge/skills/mahdisanagostar/chef/graph-first-retrieval.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 | $0.00000 | $0.00049 |
| Opus 5.5 | $0.00000 | $0.00020 |
| Sonnet 5.5 | $0.00000 | $0.00010 |
| Haiku 4.5 | $0.00000 | $0.00005 |
Grade A, and why
graph-first-retrieval 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.
What it actually says
Graph First Retrieval
Always retrieve from graph before source.
Primary index:
knowledge-vault/Graphify/graphify-out/wiki/index.md
Secondary report:
knowledge-vault/Graphify/graphify-out/GRAPH_REPORT.md
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 · 12 lines · 0 tokens per session scan A 4923510409fc
graph-first-retrieval is a skill published in the GitHub repository mahdisanagostar/chef (2 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 49 tokens. 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.
Other skills, from other repositories
llm-wiki
Explain and apply the foundational LLM Wiki architecture and knowledge-management strategy. Use for setup, architecture, schema, or organization decisions; operational ingest/query/lint work uses dedicated skills.
llmwiki
Build and use a persistent, citation-traceable knowledge wiki from documents, research papers, notes, or project documentation. Use for llmwiki projects, retrieving reusable evidence for an agent task, or compiling a corpus for repeated questions. A one-off file read or live web search does not need a wiki.
brain
Health-check the second brain so failures are not silent: is the local search server alive, is the RAG index fresh, is the session-memory ledger filling, did the nightly distillation run. Read-only, with a green/red verdict per component. Triggers: "/brain", "/memory", "is the reindex alive".
query
Use when retrieving from a project knowledge-base/ — multi-channel RAG recall (vector plus lexical plus metadata, RRF-fused) returning locate-pointer cards; multi-round and multi-angle querying allowed. Trigger /ragkit:query, or called by name by workflows like specode. Building or refreshing the index instead uses…
embed
Use when building or refreshing the RagKit index for a project knowledge-base/ (e.g. after distill added or updated knowledge points). Trigger /ragkit:embed. Querying instead uses ragkit:query; index health uses ragkit:status.
eval
Use when measuring or tuning RagKit retrieval accuracy — runs the golden evalset, reports recall@k and MRR per bucket. Trigger /ragkit:eval. Run before or after any retrieval-param change to keep a baseline.