Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add melodic-software/claude-code-plugins/plugin install performanceWrote 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/melodic-software/claude-code-plugins/protect)<a href="https://agentmods.dev/skills/melodic-software/claude-code-plugins/protect"><img src="https://agentmods.dev/badge/skills/melodic-software/claude-code-plugins/protect/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/skills/melodic-software/claude-code-plugins/protect"><img src="https://agentmods.dev/badge/skills/melodic-software/claude-code-plugins/protect.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 | $0.00101 | $0.02813 |
| Opus 5.5 | $0.00040 | $0.01125 |
| Sonnet 5.5 | $0.00020 | $0.00563 |
| Haiku 4.5 | $0.00010 | $0.00281 |
Grade A, and why
protect 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 18d 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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arguments. [<counter or claim>]. e.g. /performance:protect the 4-to-1 spawn reduction
Purpose
Answers "what keeps this win from eroding?"
Performance wins decay in a codebase that keeps moving: nothing asserts the new cost, so the next change that adds it back passes every test. A checked-in counter ceiling, checked in CI, turns the win into a build failure the next regression has to answer.
Run it once, after /performance:verify reports MET on a counter. It proposes files; a human
approves them. It never merges, and no model runs in CI: the check is a script and a number.
Read ${CLAUDE_PLUGIN_ROOT}/reference/techniques.md#g-protect-the-win
for the technique entries this skill applies.
1. Choose the counter
- Counters only, never durations. A duration ceiling moves with runner load, fails at random, and teaches people to raise it. Ratchet the headline counter from the verify report.
- Measure it in the state CI will see. A counter behind a cache has a cold value and a warm value; a guard with a per-user cache can count 6 spawns cold and 1 warm. CI runners start cold, so a ceiling measured warm fails there. Pin the state in the command (unset or empty the cache location), and measure that.
- It must be deterministic.
ratchet.py addmeasures twice and refuses a counter whose two runs disagree (${CLAUDE_PLUGIN_ROOT}/reference/harness-integrity.mdrule 1). Fix what varies before ratcheting.
2. The ceilings file
Propose .performance/ratchets.json, created by
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/ratchet.py add --name <n> --field <f> --goal <g> --command <cmd>
from the repository root:
{"counters": [{"name": "guard-spawns", "command": "<shell command>", "field": "spawns",
"ceiling": 6, "goal": "<the verified goal this protects>"}]}
commandruns through the shell from the repository root and prints a<field>=<number>token.- The command must exit non-zero when the subject fails. A subject that errors out early spends
fewer spawns and passes any ceiling.
spawn-census.shexits 0 whatever the subject did and printsrc=<n>, so append| grep -F ' rc=0 '. - The ceiling is the measured value, never a round number above it. Headroom is room for a regression.
- CI has no plugin installed, so the proposal vendors
ratchet.py(one stdlib file) into the repository, for example.performance/ratchet.py, unless the repository already carries it.
What ships with it
1 file 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.
- 18d ago Changed · +27 lines 9319b71fe713
- 20d ago Changed · +2 lines 4a29423c052a
- 25d ago First seen · 193 lines · 101 tokens per session scan A e4f337f02c3f
protect is a skill published in the GitHub repository melodic-software/claude-code-plugins (26 stars, last pushed today), licensed MIT. It adds 101 tokens to every session and 2,813 once invoked, about $0.0004 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 2026-09-25.
Other skills, from other repositories
benchmark-before-optimize
For performance work: measure before changing, profile to find bottlenecks, compare before and after.
test-structural-invariants
For data structure validation: test lengths, relationships, constraints that must hold, verify setup is correct.
test-with-examples
For example-driven development: test cases as specifications, input/output pairs, documentation through examples.
verify-solution-properties
For solution checking: independent verification that result is correct, separate from how it was computed.
verify-with-inline-tests
For test co-location: tests in same file as code, run with main, tests as documentation and examples.
test-deploy
Interactive cold-start validation of every systemprompt distribution channel. Walks through Docker Hub, GHCR, binary install.sh, Homebrew, Scoop, Helm, APT, RPM, Winget, Nix, Railway, Render, and Coolify — one at a time — runs the install recipe, captures pass/fail, and reports the state of the distribution surface…