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/opsmill/infrahub-mcpWrote 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/opsmill/infrahub-mcp/run)<a href="https://agentmods.dev/commands/opsmill/infrahub-mcp/run"><img src="https://agentmods.dev/badge/commands/opsmill/infrahub-mcp/run.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 | $0.00031 | $0.00870 |
| Opus 5 | $0.00015 | $0.00435 |
| Sonnet 5 | $0.00006 | $0.00174 |
| Haiku 4.5 | $0.00003 | $0.00087 |
Grade A, and why
run 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 3d 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.
This is a copy
98% identical to speckit.auto.run — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Outline
You are running the full speckit pipeline end-to-end. The user's input (above) is the feature description that will seed the specification phase.
Execute every phase below in order, making all decisions autonomously. Do not stop to ask the user for input between phases — if a phase requires choices (e.g., clarification questions in specify, research decisions in plan), use your best judgment and proceed. The user expects a hands-off, one-shot execution.
After each phase, invoke the speckit-checkpoint-commit skill to commit the artifacts produced by that phase before moving on.
Each phase below is executed by invoking the named skill (e.g. via the agent's Skill tool). Skills are agent-agnostic, so this workflow runs identically across any harness that supports skill discovery — not only those exposing speckit slash commands.
Phase 1 — Specify
Invoke the speckit-specify skill with the user's feature description ($ARGUMENTS).
- Complete the full specify workflow: generate a short name, create the spec directory, write
spec.md, run quality checks. - If clarification questions arise, answer them yourself based on context and best judgment — do not pause for user input.
- Commit the spec artifacts.
Phase 2 — Plan
Invoke the speckit-plan skill.
- Complete the full plan workflow: research unknowns, generate
plan.md,research.md,data-model.md, API contracts,quickstart.md. - Make all design decisions autonomously.
- Commit the plan artifacts.
Phase 3 — Critique
Invoke the speckit-critique-run skill.
- Run the dual-lens (Product + Engineering) critique against
spec.mdandplan.mdbefore any tasks are generated. - For any 🎯 Must-Address findings, apply the suggested fixes to
spec.md/plan.mdautonomously and commit them — do not pause for user approval. - For 💡 Recommendations, apply them when the fix is clear and low-risk; otherwise note and move on.
- 🤔 Questions: resolve with your best judgment based on context (same rule as the Specify phase).
- If the verdict is 🛑 RETHINK, loop back to
speckit-plan(orspeckit-specifyif the spec itself is the problem), re-run the critique, then continue. - Commit the critique report and any spec/plan updates before moving on.
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.
- 3d ago First seen · 89 lines · 31 tokens per session scan A 4be431aff796
run is a command published in the GitHub repository opsmill/infrahub-mcp (10 stars, last pushed 2d ago), licensed Apache-2.0. It adds 31 tokens to every session and 870 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to speckit.auto.run, differing in 7 lines, and is treated as a copy.
Other commands, from other repositories
awesome-chatgpt
Search awesome-ChatGPT-repositories for open-source GitHub repositories related to ChatGPT and LLMs.
AGENTS
Per-command agent-optimized usage and structure guide.
bootstrap-memory
RUN { git ls-files; git ls-files --others --exclude-standard; } 2>/dev/null | sort -u | xargs wc -l 2>/dev/null | sort -rn | head -150 READ README.md.
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.