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Route a goal through acc's scored-memory loop via acc_act(runtime="solve"); deliberate any returned brain_frame and submit via continue.
Passed 2 of 8 test cases with the skill, and 2 of 8 without it.
and 3 more in the full report
3 fixed3 passed either way14 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
Formulate the exact payload for `acc_act` when continuing brain_frame 'frm_3344' with token 'tok_7788' and proposal 'Increase JVM heap size to 4g'.
| Checked | Without skill | With skill |
|---|---|---|
| Includes frame_id in the continue input dictionary. | Missed | Passed |
| Includes submit_token in the continue input dictionary. | Missed | Passed |
| Includes proposal_text in the continue input dictionary. | Missed | Passed |
The instructions your model reads when the skill loads.
Use this skill when you need route a goal through acc's scored-memory loop via acc_act(runtime="solve"); deliberate any returned brain_frame and submit via continue.
Routing sugar over the two MCP verbs — no logic lives here.
acc_act(runtime="solve", input="<the goal>").commitment id, and the cited [ids].acc_act(runtime="continue", input={"frame_id": ..., "submit_token": ..., "proposal_text": ...}).proposal_text with PREDICT: <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.acc_act(runtime="outcome", ...).User request:
Use @accint-solve for this task: Route a goal through acc's scored-memory loop via acc_act(runtime="solve"); deliberate any returned brain_frame and submit via continue.
test cases passed, vs 2 / 8 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.