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/configflux/weld/agent-system-maintainernpx skills add configflux/weld --skill agent-system-maintainergit clone --depth 1 https://github.com/configflux/weldWrote 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/configflux/weld/agent-system-maintainer)<a href="https://agentmods.dev/skills/configflux/weld/agent-system-maintainer"><img src="https://agentmods.dev/badge/skills/configflux/weld/agent-system-maintainer.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 | $0.00033 | $0.00502 |
| Opus 5 | $0.00016 | $0.00251 |
| Sonnet 5 | $0.00007 | $0.00100 |
| Haiku 4.5 | $0.00003 | $0.00050 |
Grade A, and why
agent-system-maintainer 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 5d 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
Agent System Maintainer
You maintain the repository's AI customization system.
This includes:
- custom agents
- subagents
- agent skills
- prompt files
- custom instructions
- AGENTS.md
- CLAUDE.md
- GEMINI.md
- hooks
- MCP configuration
- OpenCode commands
- Cursor rules
- GitHub Copilot / VS Code customizations
Mandatory Workflow
Before modifying any AI customization file:
-
Run:
wd agents discover wd agents audit -
Identify the asset being changed:
wd agents explain <name-or-path> -
Check impact:
wd agents impact <name-or-path> -
If the requested change affects behavior, create a change plan:
wd agents plan-change "<user request>" -
Modify the smallest authoritative set of files.
-
Re-run:
wd agents audit -
Report the files changed, related assets checked, conflicts fixed, and remaining risks.
Rules
- Do not update only one platform-specific copy when an authoritative source exists.
- Do not create overlapping agents without explaining the boundary.
- Do not duplicate long instructions across platforms unless Weld marks them as generated copies.
- Prefer shared skills for reusable workflows.
- Prefer custom agents for persistent personas with specific tool permissions.
- Prefer prompt files or commands for one-off reusable tasks.
- Prefer hooks only when behavior must be deterministic.
- If a hook executes shell commands, document the trigger, risk, and rollback behavior.
- Keep descriptions precise because many agents use descriptions for implicit activation.
Output Format
Agent customization change summary
Request:
...
Files changed:
...
Graph impact checked:
...
Conflicts found:
...
Conflicts resolved:
...
Remaining risks:
...
Recommended follow-up:
...
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 110 lines · 33 tokens per session scan A ee553c69576e
agent-system-maintainer is a skill published in the GitHub repository configflux/weld (6 stars, last pushed 2d ago), licensed Apache-2.0. It adds 33 tokens to every session and 502 once invoked, about $0.0002 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
self-repair-pipeline
Runs the full entry point self-repair pipeline. Detects entry points in Ariadne packages or external codebases, triages false positives via sub-agents, plans fixes for each issue group with competing proposals and multi-angle review, and creates backlog tasks.
mcp-headless-test
Run live tests of the Ariadne MCP server using Claude Code in headless mode. Tests tool discovery, invocation, and filtering. Use when validating MCP server changes or checking integration with Claude Code.
release
Publishing a jMunch release (jcodemunch-mcp, jdocmunch-mcp, jdatamunch-mcp, jragmunch-cli), reviewing/merging/closing PRs, and responding to the community. Load before any version bump, PyPI upload, tag, GitHub release, MCP registry publish, or PR merge.
mcp-analytics
Check Ariadne MCP tool usage analytics — session counts, tool call stats, durations, and error rates.
release
Prepare a new release of Ariadne packages. Creates changesets, verifies build/tests, and drafts the PR to main.
observatory
Context for the jcodemunch-observatory weekly scorecard — what it tracks, the Monday 06:00 UTC cron, why scores must be pulled live rather than transcribed, the NestJS grade story, and the workflow's version dispatch input. Load before quoting, interpreting, or editing observatory scores or config.