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/koldovsky/project-factory/project-factory-initgit clone --depth 1 https://github.com/koldovsky/project-factoryWrote 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/koldovsky/project-factory/project-factory-init)<a href="https://agentmods.dev/commands/koldovsky/project-factory/project-factory-init"><img src="https://agentmods.dev/badge/commands/koldovsky/project-factory/project-factory-init.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 5.1 | $0.00023 | $0.00290 |
| Opus 5 | $0.00012 | $0.00145 |
| Sonnet 5 | $0.00005 | $0.00058 |
| Haiku 4.5 | $0.00002 | $0.00029 |
Grade A, and why
project-factory-init 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 5d 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
Execute the Project Factory init procedure. Canonical steps: commands/init.md
(summarized in AGENTS.md, which Codex reads natively as the rules).
Install the per-project loop — the deterministic scripts/check-*, git hooks,
CI, OpenSpec, and filled templates — idempotently and non-destructively
(Gate G0). Then follow MASTER-PROMPT.md from Phase 1 under
checklists/quality-gates.md.
Codex subagents have no parallel Workflow fan-out, so run the review/eval/spec
passes sequentially with fresh context (maker ≠ checker) — but still produce
the real evidence artifacts; the gates check artifacts, not process. For Phase 6
recordings use LOCAL headless Playwright (npm i -D @playwright/test && npx playwright install chromium && node scripts/record-demos.mjs), never an in-app
browser connector, then check-recordings + check-a11y + vision-verify. The
scripts, gates, specs, and evidence are identical to the Claude Code path. See
docs/portability.md.
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.
- 5d ago First seen · 22 lines · 23 tokens per session scan A 41fbb7ecee94
project-factory-init is a command published in the GitHub repository koldovsky/project-factory (4 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 290 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 2026-08-31.
Other commands, from other repositories
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.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.