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AC pachca-ai-agent

Решить, бот или MCP, и сделать AI-агента в Пачке в виде бота: его зовут упоминанием в чате или треде либо пишут ему в личный чат, он собирает контекст треда, показывает таймер agent-thinking, отвечает в треде и оформляет длинный ответ карточкой .md. Когда: «AI-агент в Пачке», «агент отвечает по упоминанию в треде», «агент не слышит упоминание», «бот-агент в личных сообщениях», «как агенту собрать контекст треда», «реакция agent-thinking», «длинный ответ агента карточкой .md», «бот или MCP», "AI agent bot in Pachca", "agent replies when mentioned", "agent does not hear a mention", "read thread context", "thinking indicator", "agent reply as an .md card", "bot vs MCP". Not for: working in Pachca from Claude or Cursor through MCP → pachca, pachca-messaging, pachca-search; drafts and scheduled messages (a bot cannot make them), sending a file or report from Claude, Cursor or the CLI → pachca-messaging; webhooks, tokens and settings of any bot → pachca-bots; acting on behalf of an employee → pachca-oauth.

pachca/openapi Agent Skills author: pachca 4 files body ≈ 4 458 tokens Open the sourcegithub.com↗ analyzed 5 h ago

Решить, бот или MCP, и сделать AI-агента в Пачке в виде бота: его зовут упоминанием в чате или треде либо пишут ему в личный чат, он собирает контекст треда…

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: openapi

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 57/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 24 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4458 tokens
    • 100Steps. 65 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +3Description length 1015: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +5Description quotes 15 example trigger phrases
    • +4Description says when NOT to use the skill
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 65 items
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +2Bilingual instructions (RU + EN)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.