Borrowing it
Nothing to install: this file belongs to EastStarAI/sanad-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/EastStarAI/sanad-agent/main/.agents/skills/sanad-reference-grounding/SKILL.mdgit clone --depth 1 https://github.com/EastStarAI/sanad-agentWrote 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/eaststarai/sanad-agent/sanad-reference-grounding)<a href="https://agentmods.dev/skills/eaststarai/sanad-agent/sanad-reference-grounding"><img src="https://agentmods.dev/badge/skills/eaststarai/sanad-agent/sanad-reference-grounding/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/eaststarai/sanad-agent/sanad-reference-grounding"><img src="https://agentmods.dev/badge/skills/eaststarai/sanad-agent/sanad-reference-grounding.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 | $0.00090 | $0.00826 |
| Opus 5 | $0.00045 | $0.00413 |
| Sonnet 5 | $0.00018 | $0.00165 |
| Haiku 4.5 | $0.00009 | $0.00083 |
Grade A, and why
sanad-reference-grounding 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 12d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ground Sanad Work in External Evidence
Use one completion-oriented workflow for both evidence authoring and implementation. Publicly acknowledge that Sanad learns from external reference projects while keeping source-specific navigation details in the ignored local evidence store.
Choose the mode
- Author or refresh: Use when planning a new reference-informed task, when no packet exists, or when a source revision changed.
- Consume: Use when implementing or reviewing a task with a current packet.
- Audit: Use after implementation to compare the result with adopted and adapted obligations.
Read references/evidence-packet-authoring.md completely before authoring, refreshing, discovering, or downloading sources.
Resolve before implementation
- Run
scripts/resolve_packet.sh <task-id>from the primary checkout or a linked worktree. - Follow the returned status:
ready: read the packet completely and continue in Consume mode.authoring_required: enter Author mode and create the missing store data or packet.refresh_required: enter Refresh mode and reconcile the changed source.source_unavailable: exhaust catalog, public discovery, and authorized access options before blocking.
- Read the governing
AGENTS.mdand license for each selected source tree. - Read any
navigation_aidpaths returned by the resolver when they accelerate discovery. Treat them as optional maps, not evidence authority. - Inspect every mandatory source file, symbol, and test in the packet. Reports are navigation aids only; source and tests are authoritative.
- If a source working tree is dirty, determine whether the changed files overlap mandatory evidence. Do not treat unrelated dirt as a blocker and do not overwrite user changes.
Do not stop merely because a packet, catalog entry, or local clone is absent. Build or repair the recoverable evidence first, rerun the resolver, then continue implementation.
What ships with it
4 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.
- 12d ago First seen · 74 lines · 90 tokens per session scan A 11feb410f5c8
sanad-reference-grounding is a skill published in the GitHub repository EastStarAI/sanad-agent (44 stars, last pushed 2d ago), licensed MIT. It adds 90 tokens to every session and 826 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 2026-08-30.
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