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/gobing-ai/superskillWrote 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/gobing-ai/superskill/magent-refine)<a href="https://agentmods.dev/commands/gobing-ai/superskill/magent-refine"><img src="https://agentmods.dev/badge/commands/gobing-ai/superskill/magent-refine/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/gobing-ai/superskill/magent-refine"><img src="https://agentmods.dev/badge/commands/gobing-ai/superskill/magent-refine.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 | $0.00006 | $0.00236 |
| Opus 5.5 | $0.00002 | $0.00094 |
| Sonnet 5.5 | $0.00001 | $0.00047 |
| Haiku 4.5 | $0.00001 | $0.00024 |
Grade A, and why
magent-refine 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 1mo 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
Magent Refine
Wraps cc:cc-magents skill.
Run the skill's refine operation. Pass $ARGUMENTS unchanged and retain the
request's scope and authorization. Preserve the distinction between
deterministic structural fixes and semantic edits: --dry-run writes nothing,
while --auto applies only the CLI's eligible fixes. Do not broaden a requested
edit or claim that --auto rewrote the body.
Skill(skill="cc:cc-magents", args="refine $ARGUMENTS")
If the native Skill mechanism is unavailable, read the installed
cc-magents skill and follow its workflow. Use the equivalent CLI for the
deterministic lane; CLI output does not replace semantic review:
superskill magent refine $ARGUMENTS
Return the delegated result and surface failures without weakening verification or safety boundaries.
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.
- 1mo ago Changed · -43 lines · -4 tokens per session 2f50a83c0b97
- 1mo ago Changed · +7 lines 8648c1284eb4
- 1mo ago First seen · 67 lines · 10 tokens per session scan A 4246f705c202
magent-refine is a command published in the GitHub repository gobing-ai/superskill (5 stars, last pushed 8d ago), licensed Apache-2.0. It adds 6 tokens to every session and 236 once invoked, about $0.0000 per session on Opus 5.5. 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.
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.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.