القائمة
Route agent work through AccInt's MCP memory loop: retrieve prior outcomes, resolve frames, and close commitments with evidence.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/phoroth/AGENTIC --skill accint-solveيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Development skill from AGENTIC (https://github.com/phoroth/AGENTIC)
Development skill from AGENTIC (https://github.com/phoroth/AGENTIC)
Generate clean, human-sounding, SEO-optimized WordPress blog posts with optional Yoast metadata, JSON-LD schema markup, and image SEO planning. Supports modular batch output.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | accint-solve |
| description | Route agent work through AccInt's MCP memory loop: retrieve prior outcomes, resolve frames, and close commitments with evidence. |
| category | ai-agents |
| risk | safe |
| source | community |
| source_repo | maxbaluev/accreted-intelligence |
| source_type | community |
| date_added | 2026-06-15 |
| author | maxbaluev |
| tags | ["mcp","memory","ai-agents","coding-agents","workflow"] |
| tools | ["claude","codex","cursor","gemini","opencode"] |
| license | Apache-2.0 |
| license_source | https://github.com/maxbaluev/accreted-intelligence/blob/main/LICENSE-APACHE-2.0.txt |
AccInt is a local-first MCP memory server for coding agents. It keeps a scored record of retrieved experience, open commitments, continuation frames, and outcome feedback so the next agent run can build on what actually worked.
Use this skill when AccInt is already configured in the host as an MCP server.
The skill adapts AccInt's public solve Claude skill into a host-agnostic
workflow for Claude Code, Codex CLI, Cursor, Gemini CLI, OpenCode, and other
agent runtimes that can call MCP tools.
Use the host's available MCP/tool list to confirm an AccInt server exposes the two verbs:
acc_retrieve(query)
acc_act(runtime, input)
If the host names the tools with a namespace prefix, use the equivalent AccInt MCP verbs. If neither verb is available, stop and ask the user to configure AccInt rather than inventing memory results.
Before a non-trivial step, retrieve relevant prior work:
{"query": "the concrete task or subtask you are about to perform"}
Read the returned memories and cite the [ids] you actually build on. Treat
retrieved memories as evidence to consider, not as a substitute for inspecting
the current repository, running tests, or checking live external state.
solveOpen an AccInt commitment for the concrete goal:
{"runtime": "solve"
If the response is final, use the answer, commitment ID, and cited memory IDs.
If the response is a brain_frame, keep the reasoning in the current session:
inspect the frame, resolve the missing judgment or knowledge from the workspace,
then submit a concise proposal through continue.
For a returned frame, submit only the frame ID and your proposal text unless the host explicitly manages tokens for you:
{
"runtime": "continue",
"input": {
"frame_id": "bf_...",
"proposal_text": "reasoned answer, plan, or decision grounded in the current evidence"
}
}
Do not leave a received frame unresolved. If the frame expires, close or rerun the bound commitment rather than pretending the continuation succeeded.
Do the actual work in the repository, browser, shell, issue tracker, or other real enprojectnment. Verify with the strongest relevant evidence available: tests, builds, linters, link checks, PR state, screenshots, maintainer replies, or production telemetry.
AccInt stores the learning loop; it does not replace the work or the evidence.
When reality answers, record the result:
{
"runtime": "outcome",
"input": {
"ref": "solved:...",
"good": true,
"note": "brief evidence: tests passed, PR merged, deploy succeeded, reviewer accepted, or exact failure reason"
}
}
Use good: false when the approach failed. Do not tag an outcome as external
or owner-validated unless a real external system or the owner actually supplied
that verdict.
1. acc_retrieve({"query":"fix failing parser tests in this repo"})
2. Read the returned memories; cite only the relevant [ids].
3. acc_act(runtime="solve", input="Fix the failing parser tests and verify them")
4. Inspect the repo, edit files, run the parser tests.
5. acc_act(runtime="outcome", input={"ref":"solved:...", "good":true, "note":"parser test command passed"})
AccInt returns frame bf_123 asking for a judgment about whether to patch the
schema or the caller.
1. Inspect the schema and caller in the current repo.
2. Decide from code evidence, not memory alone.
3. acc_act(runtime="continue", input={"frame_id":"bf_123", "proposal_text":"Patch the caller because..."})
4. Continue implementation and verification.
[ids] whenever they shape your plan or answer.acc_retrieve and acc_act.brain_frame open because implementation work started.
Solution: Submit a continue proposal first, or close/rerun the bound
commitment if the frame expires.@agent-memory-mcp - Use when you need a broader overview of MCP-backed
agent memory systems.@verification-before-completion - Use before claiming work is complete.@lint-and-validate - Use to select and run repository validation commands.