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 dineshdb/pie --skill verificationgit clone --depth 1 https://github.com/dineshdb/pieWrote 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/dineshdb/pie/verification)<a href="https://agentmods.dev/skills/dineshdb/pie/verification"><img src="https://agentmods.dev/badge/skills/dineshdb/pie/verification/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/dineshdb/pie/verification"><img src="https://agentmods.dev/badge/skills/dineshdb/pie/verification.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 | $0.00014 | $0.00232 |
| Opus 5.5 | $0.00006 | $0.00093 |
| Sonnet 5.5 | $0.00003 | $0.00046 |
| Haiku 4.5 | $0.00001 | $0.00023 |
Grade A, and why
verification 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
Verification Workflow
Evidence-based development. No completion claims without fresh proof of success.
1. Reproduction (The "Failing State")
Before fixing, prove the bug exists with a minimal test or script.
cat > repro.py << 'EOF'
# Minimal code to trigger the failure
EOF
python3 repro.py # Should FAIL
2. Validation (The "Passing State")
After implementation, run the same reproduction script to confirm the fix.
python3 repro.py # Should now PASS
3. Regression Check
Ensure no collateral damage by running existing test suites.
cargo test
uv run pytest
repo verify
Principles
- Repro First: Never fix a bug you haven't reproduced.
- Isolate: Keep reproduction scripts minimal and independent.
- Clean Up: Remove temporary repro scripts after verification is complete.
- Evidence: Prefer actual command output over "hand-wavy" assertions of success.
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 · 38 lines · 14 tokens per session scan A 45242201dc55
verification is a skill published in the GitHub repository dineshdb/pie (2 stars, last pushed 28d ago), licensed MIT. It adds 14 tokens to every session and 232 once invoked, about $0.0001 per session on Opus 5.5. 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.
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Analyze codebase structure before making changes — the "Surgeon's Scan" pattern.
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test-fix
Diagnose and minimally fix a failing test, then re-run.