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 lingxling/awesome-skills-cn --skill accint-framesgit clone --depth 1 https://github.com/lingxling/awesome-skills-cnWrote 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/lingxling/awesome-skills-cn/accint-frames)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/accint-frames"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/accint-frames/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/lingxling/awesome-skills-cn/accint-frames"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/accint-frames.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 | $0.00032 | $0.00422 |
| Opus 5 | $0.00016 | $0.00211 |
| Sonnet 5 | $0.00006 | $0.00084 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
accint-frames 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.
This is a copy
92% identical to accint-frames — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
frames
When to Use
Use this skill when you need drain acc's deliberation queue — open/waiting brain_frames checkpointed by headless runs — via acc_act(runtime="continue").
Routing sugar over the two MCP verbs — no logic lives here.
- List the queue:
acc frames(CLI, read-only observation). - For each open/waiting frame: read its typed hole + retrieved context, deliberate,
then submit via
acc_act(runtime="continue", input={"frame_id": ..., "submit_token": ..., "proposal_text": ...}). - End
proposal_textwithPREDICT: <0.00-1.00> <why>; acc strips that line before the owner sees it and uses it to calibrate the Work Model against later outcomes. - An identical duplicate submit replays the cached result — resubmitting is safe.
- Surface each resolution's
commitmentid and cited[ids]; drain the queue fully before taking new work — checkpointed frames are work headless runs saved for you.
Limitations
- Use this skill only when the task clearly matches its upstream source and local project context.
- Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
- Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.
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 · 36 lines · 32 tokens per session scan A 601c8f4156bf
accint-frames is a skill published in the GitHub repository lingxling/awesome-skills-cn (281 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 422 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to accint-frames, differing in 8 lines, and is treated as a copy.
Other skills, from other repositories
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context-degradation
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context-compression
REDUCING context size — summarization strategies, anchored iterative summarization, tokens-per-task optimization, compaction triggers, and probe-based evaluation. Use when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions structured…
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Foundational theory of context engineering — what context IS, how attention works, progressive disclosure principles, and context budgeting basics. Use when the user asks to "understand context", "explain context windows", "learn context engineering", or discusses context components, attention mechanics, or context…
context-optimization
EXTENDING effective context capacity — KV-cache optimization, observation masking, context partitioning, and retrieval strategies. Use when the user asks to "optimize context", "implement KV-cache", "partition context", "mask observations", or mentions extending context capacity or cache-friendly prompt design. NOT…
memory-integration
Use to maintain context across sessions - integrates episodic-memory for conversation recall and mcpmemory knowledge graph for persistent facts.