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/maxbaluev/accreted-intelligence/solvenpx skills add maxbaluev/accreted-intelligence --skill solvegit clone --depth 1 https://github.com/maxbaluev/accreted-intelligenceWrote 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/maxbaluev/accreted-intelligence/solve)<a href="https://agentmods.dev/skills/maxbaluev/accreted-intelligence/solve"><img src="https://agentmods.dev/badge/skills/maxbaluev/accreted-intelligence/solve.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 | $0.00028 | $0.00251 |
| Opus 5 | $0.00014 | $0.00125 |
| Sonnet 5 | $0.00006 | $0.00050 |
| Haiku 4.5 | $0.00003 | $0.00025 |
Grade A, and why
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 3d 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.
What it actually says
solve
Routing sugar over the two MCP verbs — no logic lives here.
- Call
acc_act(runtime="solve", input="<the goal>"). - If the result is final: surface the answer, the
commitmentid, and the cited[ids]. - If the result is a brain_frame: it is YOUR deliberation turn — the frame is typed
(which hole, what was retrieved, what is predicted). Reason over it, then submit via
acc_act(runtime="continue", input={"frame_id": ..., "submit_token": ..., "proposal_text": ...}). - End
proposal_textwithPREDICT: <0.00-1.00> <why>; acc strips that line before the owner sees it and uses it to calibrate the Work Model against later outcomes. - Never leave a received frame unresolved; never solo-derive outside the loop.
- Close the commitment honestly later with
acc_act(runtime="outcome", ...).
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.
- 3d ago First seen · 19 lines · 28 tokens per session scan A 643f38564c30
solve is a skill published in the GitHub repository maxbaluev/accreted-intelligence (7 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 28 tokens to every session and 251 once invoked, about $0.0001 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.
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