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/alinotfoundbtw/sounding/corpusgit clone --depth 1 https://github.com/alinotfoundbtw/soundingWhat 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 | $0.00009 | $0.00226 |
| Opus 5 | $0.00005 | $0.00113 |
| Sonnet 5 | $0.00002 | $0.00045 |
| Haiku 4.5 | $0.00001 | $0.00023 |
Grade A, and why
corpus 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
Audit every skill under $ARGUMENTS — work we did not write — and report.
For each skill: score and the rule codes that fired. Then aggregate:
- mean and median score
- how many scored 100
- a count per rule code, worst first
- for any rule firing on more than ~15% of the corpus, quote two real examples
Then judge each firing rule honestly: is it a real defect in their work, or a defect in our rule? Assume ours until proven otherwise. The first corpus run found a 46% false-positive rate and four defects, including a rule that flagged security guidance because it quoted an attack string.
Do not change any rule yet. Report first, with a recommendation per rule.
If a rule needs loosening, also state how you will verify it still fires on genuinely bad input — tuning until the corpus is green is the same failure wearing a different mask.
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 · 25 lines · 9 tokens per session scan A 2d85be2f00e9
corpus is a command published in the GitHub repository alinotfoundbtw/sounding (4 stars, last pushed 7d ago), licensed MIT. It adds 9 tokens to every session and 226 once invoked, about $0.0000 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 2026-08-31.
Other commands, from other repositories
validate-dependencies
Validate all task dependencies for issues.
flow-nexus-neural
Train and deploy neural networks in distributed sandboxes.
flow-nexus-auth
Flow Nexus authentication and user management.
validate-prd
Validate an existing PRD against BMAD standards - comprehensive review for completeness, clarity, and quality.
editorial-review-structure
Structural editor that proposes cuts, reorganization, and simplification while preserving comprehension.
add-tool
Scaffold a new FreeAgent MCP tool (handler + registration + test) following the repo pattern.