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 skills add ychampion/cskill-agents --skill cache-edit-pin-and-replaygit clone --depth 1 https://github.com/ychampion/cskill-agentsWrote 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/ychampion/cskill-agents/cache-edit-pin-and-replay)<a href="https://agentmods.dev/skills/ychampion/cskill-agents/cache-edit-pin-and-replay"><img src="https://agentmods.dev/badge/skills/ychampion/cskill-agents/cache-edit-pin-and-replay/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/ychampion/cskill-agents/cache-edit-pin-and-replay"><img src="https://agentmods.dev/badge/skills/ychampion/cskill-agents/cache-edit-pin-and-replay.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 | $0.00038 | $0.00440 |
| Opus 5 | $0.00019 | $0.00220 |
| Sonnet 5 | $0.00008 | $0.00088 |
| Haiku 4.5 | $0.00004 | $0.00044 |
Grade A, and why
cache-edit-pin-and-replay 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 11d 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 — 29 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SKILL: Cache Edit Pin and Replay
Domain: context-management Trigger: Apply when the cached microcompact flow must persist tool-result deletions and re-send them across turns without rewriting the cache edits on every retry. Source Pattern: Distilled from reviewed cache-edit replay and microcompact lifecycle implementations.
Core Method
Keep one pending cache-edit block alive until the next API request consumes it, then pin that block to the originating user-message position so the same edit can be replayed on later turns or retries. Track which tool results have already been sent so you do not regenerate or resend identical edits after every response. Reset the cached edit state only on explicit lifecycle boundaries such as a session reset or manual cache flush. This preserves cache-edit continuity across turns and makes replay deterministic.
Key Rules
- Keep new cache edits pending until the next API request actually consumes them.
- Pin consumed edits to their originating user-message position so later requests can replay them deterministically.
- After a request completes, mark the related tool results as already sent so the same edits are not requeued.
- Reserve state resets for explicit lifecycle transitions rather than clearing the cache-edit state after every turn.
- Document the lifecycle so future callers know when to zero out the cached state versus when to leave it for reuse.
Example Application
When a microcompact pass deletes tool results to shrink the cached prefix, queue those deletions for the next request, pin them to the relevant user-message position when they are consumed, and replay them on later turns if the cached conversation needs to be reconstructed.
Anti-Patterns (What NOT to do)
- Do not clear
pendingCacheEditsbefore the API request consumes them; otherwise the next request no longer knows what to delete. - Do not unpin or reset the cached state after every turn — doing so loses the ability to rehydrate cache edits for a replayed conversation.
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.
- 11d ago First seen · 29 lines · 38 tokens per session scan A 3675d9a1a67b
cache-edit-pin-and-replay is a skill published in the GitHub repository ychampion/cskill-agents (36 stars, last pushed 5mo ago), licensed MIT. It adds 38 tokens to every session and 440 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 2026-08-30.
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