prompt-cache-skills

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Bu listing icin henuz AI raporu yok.

SUMMARY

Drop-in prompt-caching fixes for the LLM agent harness you use. Point your AI coding agent at this repo and it ships the patches.

README.md

prompt-cache-skills — Drop-in prompt-caching fixes for every LLM agent harness

Get Started 13 Skills 13 Completed Audits 10x Savings

License Audit Date PRs Welcome


Most popular OSS agent harnesses (Cline, Roo Code, Continue, OpenCode,
Aider) leave 30-90% off your API bill on the table because their
prompt-caching code is subtly wrong, off-by-default, or just missing
for some providers.

This repo is a set of drop-in skills that any AI coding agent
(Claude Code, Codex, Cline, Cursor, Devin, Gemini CLI, OpenCode…) can
read and apply on its own.

You don't read the diffs. You point your agent at this repo and say:

"Apply every skill in this repo that matches the harnesses I use."

The agent reads each SKILL.md, checks if it applies to your setup,
lands the diff, and verifies the fix on the wire. You go from broken
or partial caching to 80-99% cache hit rates without doing the
research yourself.


What you actually save

One row per completed audit, so the coverage matches the scorecard:

Harness Finding Cost impact today Fix / status
Claude Desktop Code Default Desktop Code launches embedded Claude Code; clean Mac logs show non-zero cache read/create counters by default Already gets Anthropic cache benefits; no prompt-caching fix needed No skill; working baseline
Codex CLI Correct OpenAI cache design: stable thread_id cache key Already gets OpenAI cache benefits No skill; reference implementation
Aider --cache-prompts off by default; 5min TTL/keepalive overhead Many users get 0% cache reads unless they opt in; shorter cache window Skills: default-on caching + 1h TTL
OpenCode Strong Anthropic path, but proxy/Bedrock edge cases exist Some OpenAI-compatible→Anthropic/Bedrock routes miss cache Skills: proxy detection + Bedrock doc-block fix
Roo Code Anthropic volatile-message bug; Bedrock custom ARN gap Wastes breakpoints; custom ARNs can drop to 0% cache reads Skills: volatile-msg fix + Bedrock custom ARN fix
Cline Anthropic volatile-message bug; OpenAI lacks prompt_cache_key Wastes Anthropic breakpoint; OpenAI native can get 0% cache reads Skills: volatile-msg fix + OpenAI cache key + timestamp pin
Continue Cache opt-in default; Gemini explicit caching missing; volatile-message bug Many users get 0% cache reads; Gemini relies on implicit luck Skills: default-on + volatile-msg + Gemini explicit cache
Hermes / Nous Multi-provider cache plumbing works; xAI wire showed cached tokens No verified savings bug in this audit No skill; working audit
Codex Desktop ChatGPT Codex backend cache-scope headers observed/inferred No verified savings bug in this audit No skill; inferred working
Devin CLI Raw CLI model path is opaque Codeium/Devin protobuf Cache behavior not inspectable from public CLI capture No skill; unverified managed backend
Windsurf / Cascade Closed desktop; model turn not captured from CLI Cache behavior unverified No skill; needs desktop capture
Antigravity Closed desktop; no model turn captured Cache behavior unverified No skill; needs desktop capture
Grok CLI CLI answered, but model call did not hit mitmproxy Cache behavior unverified No skill; needs transport-aware capture

13 skills total cover the verified patchable OSS bugs. See
skills/README.md for the full index.


How to use it

Three-step process: Point → Detect and Patch → Verify and Save

Option A — point any AI coding agent at this repo

In your agent of choice (Claude Code, Codex, Cline, Cursor, Devin, etc.):

Read https://github.com/OnlyTerp/prompt-cache-skills

Apply every skill in skills/ that matches the harnesses I currently
use. For each one:
1. Confirm the target file exists in my project at the cited path.
2. Apply the diff.
3. Run the SKILL's Verify steps and confirm the assertion passes.
4. If verify fails, revert and tell me why.

That's it. The agent picks up the rest from each SKILL.md's
machine-readable frontmatter and instructions.

Option B — install as a skill bundle in Claude Code / Devin / etc.

If you use one of the agents that supports a skills directory:

# Claude Code
git clone https://github.com/OnlyTerp/prompt-cache-skills ~/.claude/skills/prompt-cache-skills

# Devin
git clone https://github.com/OnlyTerp/prompt-cache-skills ~/.config/devin/skills/prompt-cache-skills

# OpenCode
git clone https://github.com/OnlyTerp/prompt-cache-skills ~/.config/opencode/skills/prompt-cache-skills

Then ask your agent:

Run the prompt-cache-skills bundle on this codebase.

Option C — read and apply by hand

Each skills/<name>/SKILL.md is a complete fix: target,
symptom, diff, verification. Apply the relevant ones manually if you
don't trust your agent to do it.


What's in here

Repository structure — skills, audits, docs, and tools

prompt-cache-skills/
├── skills/                       ← the fixes (this is what your agent reads)
│   ├── cline-fix-volatile-msg/
│   ├── cline-openai-cache-key/
│   ├── cline-pin-timestamp/
│   ├── roo-fix-volatile-msg/
│   ├── roo-bedrock-custom-arn/
│   ├── continue-fix-volatile-msg/
│   ├── continue-enable-defaults/
│   ├── continue-gemini-explicit/
│   ├── opencode-detect-openai-compat/
│   ├── opencode-bedrock-doc-blocks/
│   ├── opencode-mistral-cache-key/
│   ├── aider-1h-ttl/
│   └── aider-cache-default-on/
├── audits/                       ← evidence: completed audits + queued stubs
│   ├── cline.md
│   ├── roo-code.md
│   ├── aider.md
│   ├── opencode.md
│   ├── continue.md
│   ├── codex-cli.md              ← (reference, already correct)
│   ├── claude-code.md
│   ├── hermes-nous.md
│   ├── codex-desktop.md
│   ├── devin-cli.md
│   ├── windsurf-cascade.md
│   ├── antigravity.md
│   ├── grok-cli.md
│   └── queued stubs: crush, goose, aichat, gptme, avante-nvim, kilo-code
├── docs/                         ← the underlying API mechanics
│   ├── concepts/                 ← per-provider caching reference
│   ├── gotchas.md                ← 16 numbered footguns
│   ├── verification.md           ← how to confirm caching on wire
│   └── scorecard.md              ← completed audits graded at a glance
├── tools/                        ← scripts to verify caching + doc consistency
│   ├── check_cache.py            ← fire request twice, dump cache_* fields
│   ├── check_docs_consistency.py ← assert counts/tables/links don't drift
│   ├── audit_harness.sh
│   └── replay_harness.md
└── AGENTS.md                     ← entry point for AI agents reading this repo

Why this exists

Cost comparison: $7.50 without caching vs $0.75 with caching — 10x savings

If your agent harness sends 30,000 tokens of system prompt + tools per
turn, on Claude 4.7 Opus that's $0.15 per turn uncached vs $0.015
cached — a 10x difference. A 50-turn coding session costs $7.50 vs
$0.75. You're paying 10x what you should be because the harness
you use either:

  • doesn't set cache_control at all,
  • sets it on volatile content that thrashes the cache,
  • doesn't set prompt_cache_key for OpenAI,
  • has caching gated behind a config flag you never set, or
  • just doesn't implement it for one of your providers.

None of these are hard to fix. They're all 5-15 line diffs. The
hard part is knowing which one applies to your harness and getting it
right. This repo does that work for you.


The grade card

Audit scorecard — 13 harnesses grouped by cache status: working, needs fixes, and unverified

13 completed harness audits, dated 2026-05-27. The original 7 include the default Claude Desktop Code baseline, source-recon audits for Codex CLI, Aider, OpenCode, Roo Code, Cline, and Continue, plus extended source/wire/local-install audits for Hermes/Nous, Codex Desktop, Devin CLI, Windsurf/Cascade, Antigravity, and Grok CLI. Six more files in audits/ are queued stubs, not completed audits.

Harness Anthropic OpenAI Bedrock Gemini Managed/other
Claude Desktop Code working (default Desktop Code verified) n/a n/a n/a n/a
Codex CLI n/a working n/a n/a n/a
Aider working automatic n/a n/a n/a
OpenCode working working partial n/a n/a
Roo Code partial working partial n/a n/a
Cline partial broken unverified n/a n/a
Continue partial partial partial broken n/a
Hermes / Nous working working (Responses) n/a unverified xAI working
Codex Desktop n/a working* n/a n/a ChatGPT Codex backend inferred
Devin CLI n/a n/a n/a n/a unverified (opaque protobuf)
Windsurf / Cascade n/a n/a n/a n/a unverified (desktop not captured)
Antigravity n/a n/a n/a unverified unverified (desktop not captured)
Grok CLI n/a n/a n/a n/a unverified (model call not captured)

* RE-backed or inferred from captured/companion wire shape where public source is unavailable; see the linked audit for caveats.

Full per-provider breakdown with file:line citations in
docs/scorecard.md.


Headline findings

Seven headline findings from the audit

  1. The "last 2 user messages" pattern is a copy-paste bug that
    propagated Cline → Roo → Continue. All three burn a breakpoint on
    the volatile current turn. Same one-line fix in each.
  2. Cline OpenAI native is silently broken — no prompt_cache_key,
    no prefix-stability work. Users on Cline+OpenAI pay full price.
  3. Gemini explicit caching is universally unimplemented. Only
    implicit (best-effort, free) caching engages, even on long
    sessions with massive stable system prompts where explicit gives
    a guaranteed 75% discount.
  4. Codex CLI is the reference for OpenAI-side caching — thread_id
    as cache key, preserved across compaction and into sub-agents.
  5. OpenCode's system-prompt split is the best Anthropic pattern.
  6. Hermes / Nous has real multi-provider cache plumbing — source
    covers Anthropic/OpenRouter/Nous/Qwen and xAI wire capture showed
    prompt_cache_key, x-grok-conv-id, and non-zero cached tokens.
  7. Closed managed surfaces need transport-aware capture. Devin,
    Windsurf, Antigravity, and Grok CLI are marked unverified where the
    model channel was protobuf, desktop-only, or proxy-invisible.

Trust but verify

Every skill ships with a Verify section that captures the wire and
confirms the fix landed. Don't take our word for it — the
tools/check_cache.py script fires any
request body twice (cold + warm) and prints the diff of cache_*
token fields.

Run it before and after applying a skill. You should see
cache_read_input_tokens (Anthropic) or cached_tokens (OpenAI) or
cachedContentTokenCount (Gemini) go from 0 to most of your input.


Contributing

We accept new skills, new harness audits, and corrections. See
CONTRIBUTING.md. The bar is: a captured request
body + a verified hit-rate change. We don't take vibe submissions.

By participating you agree to our Code of Conduct.

Security

Found something that looks like a credential leak path, a request
construction bug that leaks user secrets, or any other security
issue? See SECURITY.md for the disclosure process.
Don't open a public issue.

Changelog

Releases tracked in CHANGELOG.md.

License

Skills and audit prose: CC-BY-4.0. Code (tools/): MIT.


If this saved you money, star the repo and share it.
The whole point is that everyone gets caching working at once.

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