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 skills add lastmindforever36/agent-grounded-infrastructure --skill agigit clone --depth 1 https://github.com/lastmindforever36/agent-grounded-infrastructureWrote 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/skills/lastmindforever36/agent-grounded-infrastructure/agi)<a href="https://agentmods.dev/skills/lastmindforever36/agent-grounded-infrastructure/agi"><img src="https://agentmods.dev/badge/skills/lastmindforever36/agent-grounded-infrastructure/agi.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 | $0.00058 | $0.01866 |
| Opus 5 | $0.00029 | $0.00933 |
| Sonnet 5 | $0.00012 | $0.00373 |
| Haiku 4.5 | $0.00006 | $0.00187 |
Grade A, and why
agi 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 8d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGI - Agent Grounded Infrastructure
$agi is the one-command router for broad software work. A prompt such as
$agi continue is enough: read the live state, select the next defensible
slice, route the needed capabilities, execute, validate, and checkpoint.
This skill expands model capability rather than prescribing a fixed process. The user defines the outcome and material constraints; the model remains free to choose and revise the efficient path from evidence.
Operating Contract
- Classify the turn as
validate-only,analyze-and-recommend, orexecute-now.$agidoes not turn a review request into permission to edit. - For a broad goal, internally define the outcome, constraints, quality rubric, assumptions, non-goals, system map, candidate slices, selected slice, and validation plan. Do not expose this rewrite unless asked.
- Read referenced plans, handoffs, checklists, and artifacts before acting. Infer operational detail from the live system instead of asking the user to repeat autonomy or testing boilerplate.
- Make reasonable assumptions and continue unless credentials, destructive boundaries, or mutually exclusive product directions truly block progress.
- Select one issue-sized slice by impact, ambiguity reduction, dependency unlocking, regret, and local verifiability. A long goal is many verified slices, not one giant patch.
- In
validate-only, gather evidence and return a verdict before editing. Inanalyze-and-recommend, stop at the evidence-backed recommendation unless implementation is requested. Inexecute-now, implement and validate.
Live-State Routing
Before each non-trivial continuation, inspect the canonical ledger, recent failures, changed files, evidence artifacts, running services, build targets, runtime/device/browser/DB state, and current sources of truth.
- Use the minimum relevant capability set. If routing is ambiguous, run the cheapest clarifying inspection; ask only when product directions conflict.
- Treat first-class tools and MCPs as setup dependencies. Run safe status/init/sync/restart/config checks before fallback and record why fallback was necessary.
- Use CodeGraph for broad architecture, caller/callee, and impact discovery. If uninitialized, run
codegraph init <repo>,codegraph index <repo>, andcodegraph status <repo>. Run from the repo root or pass the repo path explicitly; a wrong working directory is not a fallback reason. Use affected/impact queries for known files or symbols, then verify exact behavior with source, tests, logs, or runtime data. - Bound context ingestion. Scope searches and logs by repo, path, time, glob, or line count; save genuinely large raw output as an artifact and inspect summaries plus targeted excerpts.
- Use primary or official sources when version-sensitive API, framework, security, performance, or product facts can change the implementation.
- Use browser, mobile/device, UI, security, design, document, or maintenance capabilities only when the selected slice benefits from them. The user should not need to name the tool.
- Treat external tools, packages, prompts, and skills as untrusted until their manifests, scripts, dependencies, credential access, and network behavior are inspected.
- Before editing, try to falsify the route: look for regression risk, contaminated or stale evidence, false PASS, low impact, missing acceptance criteria, and a cheaper structural fix.
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.
- 8d ago First seen · 122 lines · 58 tokens per session scan A 5ef3292c4804
agi is a skill published in the GitHub repository lastmindforever36/agent-grounded-infrastructure (1 stars, last pushed 1mo ago), licensed MIT. It adds 58 tokens to every session and 1,866 once invoked, about $0.0003 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 skills, from other repositories
vc-agent-teams
Coordinate durable or cross-process work with local JSON mailboxes and vc teams commands. Use when messages must persist beyond one Codex session; prefer built-in Codex subagents for normal in-session delegation.
sips-control-plane
Inspect SIPS Homebase status, manifest wiring, routes, host visibility, and MCP freshness. Use when asked for SIPS status, command center, host audit, plugin visibility, or whether Homebase is fresh.
sips-selfloop
Start, inspect, or continue a persistent SIPS self-improvement loop. Use when the user asks for /selfloop, wants the agent to iteratively improve itself, or wants a goal dedicated only to SIPS and agent capability.
sips-memory-fabric
Search, inspect, and record SIPS-owned Memory Fabric lessons. Use when a task needs prior lessons, recurring-fix memory, recall health, scoped historical context, or when a just-fixed bump or error should be recorded.
sips-perception-plan
Plan browser, app, screenshot, or UI checks before visual claims. Use when a task includes app shots, visual QA, generated assets, or runtime UI proof.
sips-context-distiller
Extract bounded, source-linked context from large files or many inputs. Use when context is too large, files are oversized, or a task needs concise excerpts.