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 skills/boshu2/agentops/using-gcnpx skills add boshu2/agentops --skill using-gcgit clone --depth 1 https://github.com/boshu2/agentopsWrote 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/boshu2/agentops/using-gc)<a href="https://agentmods.dev/skills/boshu2/agentops/using-gc"><img src="https://agentmods.dev/badge/skills/boshu2/agentops/using-gc.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 | $0.00056 | $0.04256 |
| Opus 5 | $0.00028 | $0.02128 |
| Sonnet 5 | $0.00011 | $0.00851 |
| Haiku 4.5 | $0.00006 | $0.00426 |
Grade A, and why
using-gc scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "$API/runs/<run-id>" # {run_id, title, status, target, scope, started_at, updated_at} How it starts
The opening of the file, as written. The whole thing — 360 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using GC
Use Gas City only when the caller explicitly selects it. Treat it as a replaceable execution adapter, not a correctness or completion boundary. The adapter cannot select AgentOps semantics, issue a binding verdict, or turn factory completion into delivery or validation proof.
Choose the factory first
AgentOps supports both Gas City and the Agentic Coding Flywheel as external software-factory runtimes. Use this skill only for Gas City. If the caller selects the Flywheel, switch to using-flywheel and its native workflow instead of wrapping it in Gas City.
AgentOps supplies skills and evidence contracts to either factory. It does not
need its own Gas City formula or role pack. Install or link AgentOps skills into
the provider runtime before starting workers; the upstream Mayor, coordinator,
and workers can then discover and select plan, implement, test,
validate, and other AgentOps skills normally.
Gas City 1.4 operating model
Gas City 1.4 is run-centered. The supervisor serves the dashboard and typed,
paginated session/run APIs. Every graph-owning city or rig scope needs its own
core.control-dispatcher; that deterministic worker advances formula control
beads. Agent workers claim routed work. The upstream gc.mayor skill is the
guided coordinator; gc.run-operator launches and supervises formulas.
The normal AgentOps path is:
- Install and pin the upstream
gascityworkflow and rig-role imports. - Add the project as a rig, prepare its stock maintainer runtime, and make AgentOps skills visible to its provider sessions.
- Create a caller-owned source intent bead and hand its id to the Mayor,
which authors the workflow beads and dispatches the upstream
build-basic, continuation, review, or implementation formula that matches the available artifacts. - Read run, session, bead, artifact, and verdict state. Completion is never inferred from chat or pane prose.
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.
- today First seen · 360 lines · 56 tokens per session scan A e3686cd6bf0d
using-gc is a skill published in the GitHub repository boshu2/agentops (433 stars, last pushed today), licensed Apache-2.0. It adds 56 tokens to every session and 4,256 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
drift-detect
Use when the user says "plan drift", asks whether the roadmap, plans, or docs still match the code, or is deciding what to rebuild when restarting a stalled project. For doc-vs-code drift inside a specific diff, use sync-docs.
atomic-issues-prs
Publish a change-set as atomic GitHub issues or PRs. Use when the user says "atomic PRs" or wants one issue/PR per logical change.
graph-backbone
Use when defining, revising, or gate-replanning the project structural backbone in project-root graph.yaml; produces a validated approved revision preserving immutable topology and additive metadata. Not for remote, credential, publish, deploy, or irreversible changes.
diamond-task
Use when one issue or PR is too large for a worker and partitions into disjoint write sets. Builds a task DAG, composes a reviewed result, or names the falsified node or split. Not for independent parallel tasks — use parallel-launch. No remote or irreversible changes.
enforce-workflow-constraints
Use when any bounded workflow starts or reaches an action, path, proposal, or merge boundary. Loads constraints before the first action, re-evaluates them at every boundary, and refuses rather than default-allow on an unreadable constraint set.
retro
Facilitate a structured sprint retrospective — what went well, what didn't, and prioritized action items with owners and deadlines. Use when running a retrospective, reflecting on a sprint, creating action items from team feedback, or learning how to run effective retros.