Borrowing it
Nothing to install: this file belongs to hackafterdark/context-sherpa. 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/hackafterdark/context-sherpa/main/.agents/skills/architectural-grounding/SKILL.mdgit clone --depth 1 https://github.com/hackafterdark/context-sherpaWrote 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/hackafterdark/context-sherpa/architectural-grounding)<a href="https://agentmods.dev/skills/hackafterdark/context-sherpa/architectural-grounding"><img src="https://agentmods.dev/badge/skills/hackafterdark/context-sherpa/architectural-grounding.svg" alt="Measured on agentmods" 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 | $0.00029 | $0.00292 |
| Opus 5 | $0.00015 | $0.00146 |
| Sonnet 5 | $0.00006 | $0.00058 |
| Haiku 4.5 | $0.00003 | $0.00029 |
Grade A, and why
Architectural Grounding (Large File Discovery) 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 7d 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
Skill: Architectural Grounding
Goal: Understand the responsibility of a complex file without reading the raw source.
Execution Pattern
- Metadata Triage: If a file is known to be large (e.g.,
app.go,main.go,server.go), calllist_symbols_in_filewithdistill: trueimmediately. - Component Isolation: Identify the 2-3 specific methods or structs in the distilled summary that are relevant to the query.
- Symbolic Zoom: Call
search_definitionsorget_symbol_mapon those specific components to find their definitions. - Surgical Read: ONLY call
read_fileorview_filefor the specific line ranges identified in step 3. NEVER read more than 100 lines at once.
Success Metric
The agent should be able to explain the "Flow" of a feature (like Database Init) while having read fewer than 15% of the total lines in the file.
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.
- 7d ago First seen · 21 lines · 29 tokens per session scan A 540b38048375
Architectural Grounding (Large File Discovery) is a skill published in the GitHub repository hackafterdark/context-sherpa (27 stars, last pushed 5mo ago), licensed MIT. It adds 29 tokens to every session and 292 once invoked, about $0.0001 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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