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/dinhanhthi/coding-friend/cf-asknpx skills add dinhanhthi/coding-friend --skill cf-askgit clone --depth 1 https://github.com/dinhanhthi/coding-friendWrote 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/dinhanhthi/coding-friend/cf-ask)<a href="https://agentmods.dev/skills/dinhanhthi/coding-friend/cf-ask"><img src="https://agentmods.dev/badge/skills/dinhanhthi/coding-friend/cf-ask.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 | $0.00068 | $0.02927 |
| Opus 5 | $0.00034 | $0.01463 |
| Sonnet 5 | $0.00014 | $0.00585 |
| Haiku 4.5 | $0.00007 | $0.00293 |
Grade A, and why
cf-ask 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 4d 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 — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cf-ask
CLI Requirement: OPTIONAL — Uses the memory MCP from
coding-friend-clifor fast indexed search and storage. Without the CLI: falls back to grep overdocs/memory/and direct file writes. Full functionality preserved, slower memory recall. See CLI requirements.
Answer the question: $ARGUMENTS
Purpose
Quick, focused Q&A about the codebase. Proactively explores code to find the answer, then saves the Q&A to project memory so it can be referenced later.
- Unlike
/cf-research: single focused answer, no multi-doc output - Unlike
/cf-remember: proactively explores the codebase to answer vs extracting knowledge already in conversation
Folder
Output goes to {docsDir}/memory/ (default: docs/memory/). Check .coding-friend/config.json for custom docsDir if it exists.
IMPORTANT — path resolution:
- Use
MAIN_REPO_ROOTfrom the SessionStart bootstrap context (injected via session-init.sh). If absent, fall back to runningpwdfor$CWDand use$CWDasMAIN_REPO_ROOT. - Read config from
CF_CONFIG_FILE(=$MAIN_REPO_ROOT/.coding-friend/config.json) — do NOT search sub-folders - Use
CF_DOCS_ROOTas the docs base dir (=$MAIN_REPO_ROOT/{docsDir}wheredocsDircomes from config, defaultdocs) - Always resolve
file_pathas an absolute path:{CF_DOCS_ROOT}/memory/{category}/{name}.md - Never use relative paths in write specs — they may resolve incorrectly when the working directory contains nested git repos
Workflow
Step 0: Custom Guide
Custom guide — auto-loaded below (if the raw command shows instead of its output, run it yourself):
bash "<plugin-root>/lib/load-custom-guide.sh" cf-ask
If output is not empty, integrate returned sections: ## Before → before first step, ## Rules → apply throughout, ## After → after final step.
Step 1: Parse the Question
- Read
$ARGUMENTSas the question - If no question provided, ask the user what they want to know
- Identify keywords and likely relevant areas (modules, features, patterns)
- Classify the question type — check if the question is a flow question. A flow question asks about how something works end-to-end, how components interact, or what happens when a process runs. Trigger words: "how does X work", "flow of", "lifecycle", "sequence", "process", "when X happens", "walk me through", "how are X connected", "what triggers", "what happens when", "pipeline", "chain". Non-flow questions (lookup/definition/pattern/why) do NOT trigger this path.
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.
- 4d ago First seen · 285 lines · 68 tokens per session scan A cf5c8b5320f3
cf-ask is a skill published in the GitHub repository dinhanhthi/coding-friend (3 stars, last pushed 5d ago), licensed MIT. It adds 68 tokens to every session and 2,927 once invoked, about $0.0003 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.
Other skills, from other repositories
persist-memory
Persist information to long-term memory at /.clacky/memories/. Use when the user asks you to remember/note something, or when reviewing a finished conversation for facts worth keeping. Handles file naming, topic merging, frontmatter, and size limits.
recall-memory
Recall relevant long-term memories on demand. Given a topic or question, judges relevance from pre-loaded metadata, loads only relevant files, and returns a concise summary to the main agent.
doris-debug-resource-isolation
Use for Doris Workload Group / resource tag queue starvation, CPU/memory isolation leaks, and workload policy debugging. Commands: SHOW WORKLOAD GROUPS, EXPLAIN resource.
acontext-installer
Install Acontext, Login & Init Acontext Project, Add Skill Memory to Agent.
campaign-operations-knowledge-builder
将活动目标、用户路径、渠道分工、物料资产、时间节奏、风险预案和复盘结论等资料,整理成符合 Agent Knowledge v0.6 document-first 标准、可被 AI 安全调用的运营类知识库。适用于用户要求“整理活动 / Campaign 运营知识库”“沉淀运营 SOP”“把运营资料变成项目资料”“维护运营知识库”的场景。.
organization-knowhow-knowledge-builder
将团队 SOP、交付流程、角色职责、项目复盘、FAQ、决策边界和升级机制,整理成符合 Agent Knowledge v0.6 document-first 标准、可被 AI 安全调用的组织经验知识库。适用于用户要求“整理组织知识库”“沉淀团队 SOP”“把交付经验变成项目资料”“维护组织 know-how”的场景。.