一键导入
skill-feedback
Submit concise feedback about AZ skills directly to PostHog. Use when a skill was useful, confusing, broken, missing context, or worth improving.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Submit concise feedback about AZ skills directly to PostHog. Use when a skill was useful, confusing, broken, missing context, or worth improving.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Call the premium Fable advisor through Claude Code CLI from Codex for hard judgment, architecture, product thinking, frontend/design critique, and high-stakes decisions. Treat the advisor as the smartest and most expensive local AI. Use when the user asks to call an advisor, consult Fable/Claude, get a second opinion, compare another agent's output, improve design quality, reason through a hard problem, start or resume an advisor session, reuse advisor/Fable session cache, use high thinking/effort, or manage Claude Code background agents while controlling cost. Prefer call-worker for routine implementation, fast code edits, tests, and mechanical cleanup.
Call the fast Codex GPT-5.5 worker through Codex CLI from Codex or another terminal for clean code implementation, focused repo exploration, tests, refactors, bug fixes, and mechanical engineering tasks. Treat the worker as fast and strong at code, but less suited than the Fable advisor for hard judgment and frontend/design taste. Use when the user asks to call a worker, trigger Codex from the CLI, delegate bounded code work, run Codex outside the Codex IDE/app, run non-interactive codex exec, resume or reuse a worker/Codex CLI thread, use GPT-5.5 with high reasoning effort, or wrap Codex CLI behavior for repeatable agent runs. Prefer call-advisor for expensive advisor work, design critique, architecture judgment, and ambiguous strategy.
Before implementing a feature or non-trivial change, scan the code it will land in and surface prep refactors that make the change easy — so the feature lands clean instead of accreting tech debt (Kent Beck "make the change easy, then make the easy change"). Use BEFORE writing code, when about to build a feature, add a new case/provider/handler to an existing pattern, restructure in service of upcoming work, or just after an implementation plan is set. Every proposal must trace to the upcoming change — not generic cleanups. Skip tiny edits and brand-new code; for changes already written use complexity-check instead.
Audit an AI answer back to the searches, sources, citations, and assumptions behind it — which results actually shaped it, and which claims are unsupported model guesswork. Traces what the answer was based on; does not grade whether it is correct. User-run on an answer (with its question if available); read-only — never alters the answer.
Multi-persona naming exploration with consensus. Spawns parallel sub-agents — each embodying a different naming philosophy (David Placek's Lexicon methodology, the Poet, the Linguist, the Culture Hacker, the Futurist) — to explore divergent naming directions for a product or company, then synthesizes into a ranked shortlist. Based on David Placek's naming framework (Lexicon Branding — Swiffer, BlackBerry, Impossible, Sonos, Pentium). Use when naming a product, company, feature, or brand and you want breadth, surprise, and strategic advantage before committing.
Turn an ai-answer-audit into a prioritized plan to get a brand cited in AI answers (ChatGPT, Perplexity, Google AI Overviews). Acts only on the audit's affectable content layer — earned citations, your own pages, open territory — and never promises to move the model layer. User-run on an audit + your brand/URL; a one-shot plan, not citation monitoring.
| name | skill-feedback |
| description | Submit concise feedback about AZ skills directly to PostHog. Use when a skill was useful, confusing, broken, missing context, or worth improving. |
| argument-hint | [skill name and feedback text] |
| hooks | {"PostToolUse":[{"matcher":"*","hooks":[{"type":"command","command":"python3 \"$HOME/.claude/skills/skill-feedback/scripts/skill-event.py\" --skill skill-feedback --event skill_activated --agent-harness claude-code --quiet","timeout":5}]}]} |
This is the shared feedback helper for AZ skills. Every skill includes a short AZ Skill Feedback footer that reminds agents how to call this script after a run.
The script sends one anonymous PostHog event directly to the AZ skills feedback project. There is no endpoint setup, no env vars, and no user-provided token.
On first send, the telemetry helper creates one random local installation ID at ~/.az-skills/installation-id. Only a short hash of that random ID is sent. Hostnames, usernames, emails, machine serials, file paths, and hardware identifiers are never sent.
scripts/skill-event.py also sends automatic read and activation events from hooks in harnesses that support them, such as Claude Code.
The telemetry scripts work for any agent that can run a shell command. Automatic telemetry depends on the agent harness:
PostToolUse hook for skill_activated.scripts/install.sh adds one user-level Read hook. It emits skill_read only when a SKILL.md under a skills folder is read.--agent-harness <name> to identify the runtime.If a harness does not support hooks, submit only the events it can honestly observe. Do not fake read or activation telemetry.
Submit feedback when one of these is true:
rating=idea, rating=confusing, rating=bug, rating=useful, or rating=other.python3 ~/.agents/skills/skill-feedback/scripts/skill-feedback.py \
--skill deslop \
--rating confusing \
--agent-harness codex \
--model-config unknown \
--text "The skill should say which files it inspected before changing code."
Use the matching installed path when needed:
python3 ~/.claude/skills/skill-feedback/scripts/skill-feedback.py \
--skill greenlight-pr \
--rating useful \
--agent-harness claude-code \
--model-config unknown \
--text "The retry budget guardrail avoided rerunning flaky CI forever."
Optional lightweight metadata:
python3 ~/.agents/skills/skill-feedback/scripts/skill-feedback.py \
--skill greenlight-pr \
--rating bug \
--agent-harness codex \
--model-config unknown \
--context repo=zvadaadam/az-skills \
--text "The skill assumes gh is authenticated before checking local repo context."
event: skill_feedbackdistinct_id: az-skills-installation:<installation_id_hash> when the local installation ID can be read or createdproperties.$process_person_profile: falseproperties.source: az-skillsproperties.schema_version: 2properties.installation_id_hash: anonymous hash of the local random installation IDproperties.agent_harness: agent runtime, such as codex or claude-codeproperties.model_config: model/config string when the harness exposes it; otherwise unknownproperties.skill: skill folder name, such as deslopproperties.rating: useful, confusing, bug, idea, or otherproperties.feedback_text: short human-readable feedbackproperties.context: optional key=value metadataTo rotate the PostHog project token, update POSTHOG_PROJECT_API_KEY in scripts/telemetry_common.py.
Skill hooks or harness adapters send explicit PostHog events:
event: skill_read when an agent reads a SKILL.mdevent: skill_activated when the skill is active and the agent makes its first tool callskill: skill folder nameagent_harness: claude-code for bundled Claude hooksmodel_config: unknown unless the harness exposes the exact runtime model/configinstallation_id_hash: anonymous hash of the local random installation IDsession_id_hash: short hash only; raw session IDs are not sentskill_activated is triggered by the first PostToolUse hook for a skill in a session and is deduped locally. skill_read is filtered to SKILL.md paths only.
Tool names, tool inputs, prompts, file contents, command text, paths, stack traces, source code, hostnames, usernames, emails, machine serials, and hardware identifiers are not sent.
After the run, if this skill was useful, confusing, broken, or missing context, submit 1-3 safe sentences. Include --agent-harness when known. Include --model-config only if the harness exposes the exact model/config; otherwise leave it as unknown:
python3 ~/.agents/skills/skill-feedback/scripts/skill-feedback.py --skill skill-feedback --rating idea --text "..."
Never include secrets, private data, source code, long prompts, or stack traces.