agentleFS
Faaz17Faaz17/Agents-and-Skills/skills/project-ops/score-audit/SKILL.md

score-audit

Skill1 starsChanged 2 months agoMITView on GitHub

Compute ERC-26's current expected qualification score against the rubric, identify the cheapest unclaimed point, and recommend what to build next in point order. Use when deciding priorities, when time is short, or before the 2026-09-06 grasp decision.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Prefer certain points over uncertain ones: when the hours are comparable.

    line 107
  2. Distinctive:

    Never recommend work that isn't on the rubric: No dashboards, no backup perception pipelines, no visualisations.

    line 108
  3. Distinctive:

    If the 2026-09-06 grasp decision is due or overdue, say so and give the arithmetic for both paths.

    line 110

≈ “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

/score-audit, expected score and the next cheapest point

The point-budget equivalent of a weight budget. Adapted from SumoX-26's /bom-builder, which did the same job for grams.

Answers one question: given where the project actually is, what is the expected qualification score, and what is the cheapest point still unclaimed?

The model

Qualification %  =  0.50 × (trial points / 19)
                 +  0.20 × code
                 +  0.10 × video
                 +  0.20 × report

Full breakdown in docs/SCORING.md.

Steps

  1. Read results/trials.csv. Take the best trial score, since the committee takes the best of three attempts. If the file is empty or missing, say so plainly: trial points are 0 banked, and everything else is projection.
  2. Score the trial component honestly, per item, marking each banked or projected:

    ItemPointsStatus
    Column publish+1banked / projected / not started
    Column annotated image+2
    Navigate to column (needs the grasp)+3
    Row publish+1
    Row annotated image+2
    Grasp+3
    Navigate to bin (needs delivery)+3
    Place, dropped +2 or gentle +4
    Collisions observed-0.5 each

    Apply the two contingencies. Navigation to the column scores nothing without a successful grasp, and navigation to the bin scores nothing without delivery. Do not count them as banked while the grasp is unproven.

  3. Assess the three deliverables against their stated criteria, not against a feeling:

    ComponentWeightAssess by
    Code20%Are perception, planning and execution in distinct nodes? Does every stage have a timeout and a retry budget? Are recovery paths present and tested? Are comments explanatory?
    Video10%Does a take exist that satisfies all six constraints? Has a full run been timed against the 5-minute ceiling?
    Report20%Do the six sections exist? Do results/trials.csv and the RViz screenshots contain the required numbers and figures?

    The video and report should be scored as near-zero until the artifacts exist. A plan to write a report is not 20%.

  4. Compute the expected score on the 100-point scale, showing the arithmetic, and separate what is banked from what is projected.
  5. Find the cheapest unclaimed point. Rank remaining work by qualification points per hour, and prefer certainty. Reference values:

    WorkQualification pointsCertainty
    Each vision publish2.6High. Pure OpenCV
    Each vision annotated image5.3High
    Gentle place instead of drop1.05High once the grasp works. Hours of Cartesian pathing
    The grasp, with its withheld navigation points15.8Low. The keystone
    Delivery chain after the grasp18.4Medium
    A recovery pathPart of the 20% code componentHigh
    The report's six sectionsup to 20High, if the data exists
    The videoup to 10High. A checklist
    Speed optimisation0 outside a tien/a
  6. State the one recommendation, with the arithmetic behind it.

Output format

## Score audit, <date>, <days> days to 2026-09-15

### Trial component (50%)
Best logged trial: <n>/19  →  <n × 0.50/19 × 100> qualification points
Banked: <list>
Projected: <list, with what each depends on>

### Code (20%)
<assessment against modularity, error handling, comments>  →  <estimate>

### Video (10%)
<does a compliant take exist>  →  <estimate>

### Report (20%)
<do the sections and the data exist>  →  <estimate>

### Expected qualification score
Banked:    <n> / 100
Projected: <n> / 100

### Cheapest unclaimed point
**Do next:** <one thing>
**Worth:** <qualification points>
**Cost:** <rough hours>
**Certainty:** high / medium / low
**Instead of:** <what this displaces, and why that's right>

Rules

  • Banked means a logged trial recorded it. Everything else is projected and must be labelled so. This distinction is the entire point of the skill.
  • Apply the contingencies. The two navigation +3s are the most commonly over-counted points in this challenge, because they look earned when the robot drives correctly and they are not.
  • Score the deliverables as near-zero until the artifacts exist. An intention is not a mark.
  • Prefer certain points over uncertain ones when the hours are comparable.
  • Never recommend work that isn't on the rubric. No dashboards, no backup perception pipelines, no visualisations. There are 29 days.
  • Recompute rather than reusing a previous audit's numbers. The point of this skill is to reflect the current state.
  • If the 2026-09-06 grasp decision is due or overdue, say so and give the arithmetic for both paths.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.