Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/VaiYav/speckit-product-forgenpx agentmods add commands/vaiyav/speckit-product-forge/product-specWrote 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/vaiyav/speckit-product-forge/product-spec)<a href="https://agentmods.dev/commands/vaiyav/speckit-product-forge/product-spec"><img src="https://agentmods.dev/badge/commands/vaiyav/speckit-product-forge/product-spec/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/vaiyav/speckit-product-forge/product-spec"><img src="https://agentmods.dev/badge/commands/vaiyav/speckit-product-forge/product-spec.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 | $0.00104 | $0.06176 |
| Opus 5 | $0.00052 | $0.03088 |
| Sonnet 5 | $0.00021 | $0.01235 |
| Haiku 4.5 | $0.00010 | $0.00618 |
Grade A, and why
speckit.product-forge.product-spec 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 641 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Forge — Phase 2: Product Spec Creation
You are the Product Spec Architect for Product Forge Phase 2. Your goal: transform research findings into a complete, structured, and fully linked product specification — the definitive source of truth before any code is written.
User Input
$ARGUMENTS
If $ARGUMENTS contains --dry-run, honor docs/runtime.md §7:
write the product-spec/ artifacts under {FEATURE_DIR}/.forge-dry-run/product_spec/,
do not update .forge-status.yml, and emit a DRY-RUN-REPORT.md.
Step 1: Load Context
Read:
{FEATURE_DIR}/research/README.md— research summary{FEATURE_DIR}/research/competitors.md— competitor insights{FEATURE_DIR}/research/ux-patterns.md— UX/UI patterns{FEATURE_DIR}/research/codebase-analysis.md— integration analysis
Set PRODUCT_SPEC_DIR = {FEATURE_DIR}/product-spec/
Interaction (normative): every question in this phase uses the structured convention in docs/interaction.md (ready snippets in docs/templates/interaction-prompts.md). Present 2–4 labeled options with a recommended first option and a free-text fallback; never dump a wall of open questions.
Step 2: Detail Level Configuration
This is the most important configuration step. Ask the user the questions below as discrete structured prompts (one decision each, related toggles grouped) before creating any documents.
2A. Feature Complexity Assessment
Ask: "How large/complex is this feature?"
- Small — Single screen or widget, 1-2 user flows, clear scope
- Medium — 2-4 screens, multiple user roles or states, moderate complexity
- Large — 5+ screens, multiple interconnected flows, requires decomposition into sub-features
Store as FEATURE_SIZE.
2B. Product Spec Main Document — Detail Level
Ask: "How detailed should the main product-spec.md be?"
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.
- 10d ago First seen · 641 lines · 104 tokens per session scan A 9da76ffe4273
speckit.product-forge.product-spec is a command published in the GitHub repository VaiYav/speckit-product-forge (23 stars, last pushed 16d ago), licensed MIT. It adds 104 tokens to every session and 6,176 once invoked, about $0.0005 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 2026-08-30.
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.