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/CyrilLeMat/temper-skillsWrote 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/cyrillemat/temper-skills/temper)<a href="https://agentmods.dev/commands/cyrillemat/temper-skills/temper"><img src="https://agentmods.dev/badge/commands/cyrillemat/temper-skills/temper.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 5.1 | $0.00028 | $0.00184 |
| Opus 5 | $0.00014 | $0.00092 |
| Sonnet 5 | $0.00006 | $0.00037 |
| Haiku 4.5 | $0.00003 | $0.00018 |
Grade A, and why
temper 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 8d 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
Use the temper-skills skill to compile the decision logic of $ARGUMENTS into a
deterministic Python decision tree plus its adversarially-written validation dataset.
If $ARGUMENTS is a directory, run the skill's library sweep instead: audit every skill
in it and present the ranked findings table before tempering anything.
Run the adversarial loop on the Claude Code subscription using persona subagents:
draft the tree, critique it each round with the five personas (in parallel), arbitrate
and show the scored round panel, gate with the user, and converge when every persona
scores ≥ 8 with no new gray zone. Then export the tree deterministically with
python -m temper_skills.export_tree.
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.
- 8d ago First seen · 15 lines · 28 tokens per session scan A 332f3b23e3b1
temper is a command published in the GitHub repository CyrilLeMat/temper-skills (4 stars, last pushed 26d ago), licensed Apache-2.0. It adds 28 tokens to every session and 184 once invoked, about $0.0001 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.
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enum-udp
UDP scan + service follow-up — top ports first, full sweep only when justified.
review
Submit a story for verification (In Progress → In Review, no PR yet).
standup
Alias of /board — the async sprint snapshot.
diagram
Generate an Excalidraw architecture or flow diagram using the ExcaliClaude skill.