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.
git 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/hire)<a href="https://agentmods.dev/commands/sairam0424/mindforge/hire"><img src="https://agentmods.dev/badge/commands/sairam0424/mindforge/hire/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/commands/sairam0424/mindforge/hire"><img src="https://agentmods.dev/badge/commands/sairam0424/mindforge/hire.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 | $0.00043 | $0.00730 |
| Opus 5.5 | $0.00017 | $0.00292 |
| Sonnet 5.5 | $0.00009 | $0.00146 |
| Haiku 4.5 | $0.00004 | $0.00073 |
Grade A, and why
mindforge:hire 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
<execution_context> @.mindforge/skills/hiring-engineering/SKILL.md </execution_context>
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Seniority Calibration: Junior → executes tasks, learns patterns, needs guidance. Mid → owns features, solves ambiguous problems, mentors juniors. Senior → designs systems, defines standards, unblocks team. Staff → sets technical direction, influences org-wide decisions, scales team effectiveness. Adjust complexity and autonomy expectations per level.
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Interview Stage Design: Stage 1 (Screening) → 30min technical phone screen with 2 focused questions. Stage 2 (Coding) → 60-90min live coding (algorithm + system implementation). Stage 3 (System Design) → 60min architecture session (scale/reliability/trade-offs). Stage 4 (Behavioral) → 45min STAR method competency questions. Stage 5 (Team Fit) → 30min cultural values alignment.
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Question Bank Generation: For system-design → design URL shortener (junior), design rate limiter (mid), design distributed cache (senior), design multi-region data platform (staff). For coding → two-sum (junior), LRU cache (mid), concurrent task scheduler (senior), distributed transaction coordinator (staff). Include rubric with 1-4 scoring for each question dimension.
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Rubric Framework: Define 4 levels per competency. Level 1 (Below bar) → cannot complete basic task with hints. Level 2 (Mixed signals) → completes with significant guidance. Level 3 (Hire bar) → completes independently with good trade-off reasoning. Level 4 (Strong hire) → exceptional depth, proactive edge case handling, teaches interviewer something new.
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Calibration Protocol: Run shadow interviews for first 3 sessions. Compare scores across interviewers weekly. Flag score variance > 1 point for debrief. Maintain question difficulty metrics (pass rate, average score). Rotate questions quarterly to prevent memorization. Document common failure modes and how to probe deeper.
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Bias Mitigation: Use structured rubrics (not gut feel). Ask same core questions to all candidates. Train interviewers on inclusive language. Anonymize resumes during screening. Track demographic offer rates and adjust process if disparities emerge. Provide clear feedback to rejected candidates.
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 · 43 tokens per session scan A 1a7010549503
mindforge:hire is a command published in the GitHub repository sairam0424/MindForge (0 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 730 once invoked, about $0.0002 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 2026-09-03.
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