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/lexfrei/ccc/shrinknpx skills add lexfrei/ccc --skill shrinkgit clone --depth 1 https://github.com/lexfrei/cccWhat 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 | $0.00136 | $0.00846 |
| Opus 5 | $0.00068 | $0.00423 |
| Sonnet 5 | $0.00027 | $0.00169 |
| Haiku 4.5 | $0.00014 | $0.00085 |
Grade A, and why
shrink 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 3d 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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When one culprit hides among n toggles, testing halves finds it in log2 n to 2·log2 n runs instead of n — the upper end pays for testing the second half after a pass: 32 flags → 5-10 runs, 1000 config lines → 10-20. The method is group testing; delta debugging is its generalization to multiple culprits.
Vocabulary: each suspect has a suspect value (as in the failing config) and an innocent value (as in the known-good one). "Test a subset" means: subset at suspect values, everything else innocent, run, record fail/pass.
Step 0 — baselines, always first
Two runs before any search:
- All-innocent must pass. If it fails, the difference between good and bad is not in the suspect list — stop and widen the list.
- All-suspect must fail. If it passes, the culprit interacts with something outside the list (environment, timing, data) — stop, the search space is wrong.
Skipping these two runs is how a whole binary search gets spent chasing a phantom.
Step 1 — split-half for a single culprit
Split the suspects in half, test one half. Fails → culprit inside, recurse into it. Passes → test the other half; fails → recurse there. Repeat to one suspect.
Both halves passing is a finding, not an error: the failure needs suspects from both halves at once. Re-split along a different boundary first (group by subsystem instead of by list order); if every split shows the same pattern, it is a genuine interaction — take the surviving suspects to the taguchi skill and rank them as factors.
Step 2 — delta debugging for multiple culprits
When more than one culprit is plausible, split-half alone can chase the first one and lose the rest. The ddmin scheme:
- Partition the current suspect set into k subsets (start k=2).
- Test each subset alone, and each complement (everything except that subset).
- A failing subset → it becomes the new suspect set, back to k=2.
- A failing complement → the removed subset was irrelevant, drop it, keep k.
- Nothing fails → double k (finer granularity); k above the set size means every remaining suspect is needed — that set is the answer.
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.
- 3d ago First seen · 43 lines · 136 tokens per session scan A 964ea1af7582
shrink is a skill published in the GitHub repository lexfrei/ccc (9 stars, last pushed 3d ago), licensed BSD-3-Clause. It adds 136 tokens to every session and 846 once invoked, about $0.0007 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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