agentleFS
yogsoth-aiyogsoth-ai/paper-reading/skills/acu-nugget-recall/SKILL.md

acu-nugget-recall

Skill1 starsChanged 3 months agoApache-2.0View on GitHub

Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Write 01-atomic-unit-writing.json, 02-atomic-unit-matching.json, and 03-recall-aggregate.json.

    line 35

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

ACU / Nugget Recall

Orchestration Pattern

  1. Require target_summary; never generate the scored summary inside this tactic.
  2. Fetch the paper and create acu-nugget-recall/.
  3. Extract units before exposing the summary to the unit-writing step. ACU uses extracted/binary units; Nugget uses authored, importance-tagged units.
  4. Match all units against target_summary in one call.
  5. Aggregate the results and write 03-recall-aggregate.json.

Write 01-atomic-unit-writing.json, 02-atomic-unit-matching.json, and 03-recall-aggregate.json. Record the summary source. Report every unmatched unit, not just the score. For a single-summary Nugget run, carry the method's weak per-topic reliability caveat alongside the number.

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