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/benchmarkgit 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/benchmark)<a href="https://agentmods.dev/commands/sairam0424/mindforge/benchmark"><img src="https://agentmods.dev/badge/commands/sairam0424/mindforge/benchmark.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 | $0.00006 | $0.00289 |
| Opus 5 | $0.00003 | $0.00144 |
| Sonnet 5 | $0.00001 | $0.00058 |
| Haiku 4.5 | $0.00001 | $0.00029 |
Grade A, and why
benchmark 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
MindForge — Benchmark Command
Usage: /mindforge:benchmark [--skill skill-name] [--compare skill-a skill-b]
Measure skill effectiveness over time.
Single skill benchmark
For a named skill, analyse AUDIT.jsonl and skill-usage.jsonl:
- How many times was the skill loaded this month?
- What is the verify pass rate for tasks where this skill was loaded?
- Are there anti-patterns less common after this skill is loaded?
- What is the average session quality score when this skill is active?
Report:
Skill Benchmark: security-review v1.0.0
────────────────────────────────────────
Usage (last 30 days): 47 task loads
Trigger distribution: text match 68%, file-path 22%, file-name 10%
Verify pass rate: 91% (vs. 84% baseline without this skill)
Security findings: 8 HIGH caught (0 CRITICAL missed in tasks using this skill)
Session quality lift: +6.2 points average when loaded
Assessment: HIGH VALUE — clear quality improvement signal
Skill comparison
Compare two skills head-to-head:
- Load frequency
- Verify pass rate improvement
- Anti-pattern detection rate
- Context budget cost (token estimate)
Helps decide: should you keep both skills, or deprecate the lower-performer?
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 · 38 lines · 6 tokens per session scan A de66353ef846
benchmark is a command published in the GitHub repository sairam0424/MindForge (0 stars, last pushed today), licensed MIT. It adds 6 tokens to every session and 289 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 2026-09-03.
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factory-sweep
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vichu
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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.