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/sairam0424/mindforge/auditgit clone --depth 1 https://github.com/sairam0424/MindForgeWrote 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/commands/sairam0424/mindforge/audit)<a href="https://agentmods.dev/commands/sairam0424/mindforge/audit"><img src="https://agentmods.dev/badge/commands/sairam0424/mindforge/audit.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 | $0.00020 | $0.00265 |
| Opus 5 | $0.00010 | $0.00133 |
| Sonnet 5 | $0.00004 | $0.00053 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
audit 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 today.
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
Query .planning/AUDIT.jsonl by phase, event, date, severity, integration, or
agent. Usage: /mindforge:audit [filters]
Supported options
--phase N--event NAME--agent NAME--severity LEVEL--date YYYY-MM-DD--summary--verify--export PATH
Summary mode
Summarise counts by event, severity, integration, and phase.
Entries with "phase": null are reported as project-level, not dropped.
Verify mode
Validate JSONL shape and chronological ordering.
Timestamp comparison may use string comparison because ISO-8601 UTC timestamps
with a Z suffix sort lexicographically.
Archive rotation
If the active audit log exceeds 10,000 lines, rotate it into
.planning/audit-archive/ before continuing heavy writes. Rotation must never
delete history; it archives then resets the active file.
Export safety
Validate export paths stay inside the project directory. If a path traversal or
unsafe destination is requested, export into .planning/ instead.
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.
- today First seen · 35 lines · 20 tokens per session scan A b9b40ba59eb7
audit is a command published in the GitHub repository sairam0424/MindForge (0 stars, last pushed today), licensed MIT. It adds 20 tokens to every session and 265 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 2026-09-03.
Other commands, from other repositories
factory-report
Linear status report across configured repos — queue depth, triage backlog, ready-to-dispatch, blocked/held.
factory-sweep
Find Linear tickets that have gone obsolete (duplicate, already shipped, overtaken by events) and retire them with evidence.
vichu
Start or continue a verified VichuFlow run for a task.
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.