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 Ghosteken/agent-harness --skill accint-solvegit clone --depth 1 https://github.com/Ghosteken/agent-harnessWrote 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/ghosteken/agent-harness/accint-solve)<a href="https://agentmods.dev/skills/ghosteken/agent-harness/accint-solve"><img src="https://agentmods.dev/badge/skills/ghosteken/agent-harness/accint-solve/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/ghosteken/agent-harness/accint-solve"><img src="https://agentmods.dev/badge/skills/ghosteken/agent-harness/accint-solve.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 | $0.00029 | $0.01775 |
| Opus 5.5 | $0.00012 | $0.00710 |
| Sonnet 5.5 | $0.00006 | $0.00355 |
| Haiku 4.5 | $0.00003 | $0.00178 |
Grade A, and why
accint-solve 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 1mo 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
100% identical to accint-solve — 0 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.
How it starts
The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AccInt Solve
Overview
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.
When to Use This Skill
- Use when starting non-trivial coding-agent work where prior decisions, debugging history, repo-specific habits, or maintainer feedback may matter.
- Use when a task may require multiple attempts and you want an explicit commitment ID that can later receive a real outcome.
- Use when AccInt returns a continuation frame and the agent must reason locally before submitting a proposal back to the memory loop.
- Use after verification, merge, deployment, maintainer response, or other reality signal to close the commitment with an honest outcome.
- Do not use when the host has no AccInt MCP tools configured; first install or configure AccInt, then rerun the workflow.
How It Works
Step 1: Confirm the AccInt MCP tools exist
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.
Step 2: Retrieve before planning
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.
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.
- 1mo ago First seen · 206 lines · 29 tokens per session scan A 3f03802c016a
accint-solve is a skill published in the GitHub repository Ghosteken/agent-harness (4 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 1,775 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to accint-solve, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
context-optimizer
Optimize token usage and context management. Use when sessions feel slow, context is degraded, or you're running out of budget.
compact-guard
Smart context compaction with state preservation. Saves critical files, task progress, and working state before compaction, restores after. Use before manual compact or when auto-compact triggers.
learn-rule
Capture a correction or lesson as a persistent learning rule with category, mistake, and correction. Stores, categorises, and retrieves rules for future sessions. Use after mistakes or when the user says "remember this", "don't forget", "note this", or "learn from this".
recall
Use before assuming you're the first to hit something in an octomux loop/scheduled task — pull what past runs learned about the thing you're touching via octomux recall --query.
recall-conversations
Read what was said in an earlier conversation — the parent's previous chats with Quill, or a child's conversation with the tutor inside an activity — from the platform's conversation logs.
accint-solve
Route agent work through AccInt's MCP memory loop: retrieve prior outcomes, resolve frames, and close commitments with evidence.