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 commands/fivol/claude-make-better/feature-admingit clone --depth 1 https://github.com/fivol/claude-make-betterWrote 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/fivol/claude-make-better/feature-admin)<a href="https://agentmods.dev/commands/fivol/claude-make-better/feature-admin"><img src="https://agentmods.dev/badge/commands/fivol/claude-make-better/feature-admin.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 | $0.00013 | $0.00303 |
| Opus 5 | $0.00006 | $0.00151 |
| Sonnet 5 | $0.00003 | $0.00061 |
| Haiku 4.5 | $0.00001 | $0.00030 |
Grade B, and why
feature-admin scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
If the script exits immediately complaining it can't find `.claude/feature/config.json`, tell the What it actually says
Launch the admin dashboard: a single live view of every task workspace
(repos, PRs + CI, dev-server health & logs, per-task summary, and a copy-able
claude --resume command). It is read-only over the skill's state plus start / stop / reap
buttons — merging stays chat-driven.
Run it in the background (run_in_background: true) so the server outlives this turn — do
not block waiting on it. Start it from the workspace root (the directory tree that holds
.claude/feature/config.json; the script self-anchors, or pass --root DIR):
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/admin.py" --open $ARGUMENTS
Then tell the user the single dashboard URL the script printed on its first line and stop. The
script already picks the right one: the pretty http://admin.localhost when Caddy is up,
otherwise the local http://127.0.0.1:<port>. Don't echo both — surface only what it printed.
If the script exits immediately complaining it can't find .claude/feature/config.json, tell the
user to run it from inside a configured workspace or pass --root <workspace-root>.
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 · 25 lines · 13 tokens per session scan B 3aa133260a11
feature-admin is a command published in the GitHub repository fivol/claude-make-better (5 stars, last pushed 24d ago), licensed MIT. It adds 13 tokens to every session and 303 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). 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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.