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 agentmods add commands/sophymarine/openregistry/shellcheckgit clone --depth 1 https://github.com/sophymarine/openregistryWhat 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 | $0.00044 | $0.00553 |
| Opus 5 | $0.00022 | $0.00277 |
| Sonnet 5 | $0.00009 | $0.00111 |
| Haiku 4.5 | $0.00004 | $0.00055 |
Grade A, and why
shellcheck 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 2d 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
Score shell-probability for "$1" in $2. Every signal must be backed by a live government-registry lookup — cite the record for each.
Step 1 — Resolve
search_companies + get_company_profile → company_id.
Step 2 — Signal collection (live calls)
- SIG-1 single director:
get_officers, count active = 1? - SIG-2 zero accounts:
list_filings(category="accounts")→ count == 0 after required filing date? - SIG-3 micro-entity only:
profile.accounts type = "micro-entity"? - SIG-4 overseas registered office:
profile.address.country≠ incorporation country? - SIG-5 shared-address concentration (where jurisdiction supports reverse-lookup): 50+ companies at same office?
- SIG-6 nominee director: for each director in GB,
get_officer_appointments— 10+ appointments = flag - SIG-7 PSC = director: cross-reference
get_persons_with_significant_controlwithget_officers— same natural person? - SIG-8 zero employees:
fetch_documenton latest accounts → parse XBRLaverage-number-of-employees - SIG-9 young + dormant: inc date <18mo + filing count 0
- SIG-10 name patterns: "Nominees Ltd", number-only, typical offshore templates
Step 3 — Score (0-100) Strong (SIG-1/2/3): 20pts each · Multipliers (SIG-4/5/6/7): 10pts each · Weak (SIG-8/9/10): 5pts each. Cap at 100.
Step 4 — Report
Shell probability: X/100 with each contributing signal's evidence cited (e.g. "SIG-6 (nominee director pattern): director John Smith holds 47 active appointments — see get_officer_appointments response for officer_id YYY"). Missing data for a signal is explicitly marked "❌ Signal Z: insufficient data".
Step 5 — Caveats Shell signals ≠ proof of fraud. Score is a screening heuristic, one layer in AML pipeline. Many legitimate holding vehicles tick several boxes.
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.
- 2d ago First seen · 32 lines · 44 tokens per session scan A ffdabc5d81b8
shellcheck is a command published in the GitHub repository sophymarine/openregistry (17 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 44 tokens to every session and 553 once invoked, about $0.0002 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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