store-evaluation
Skill5 starsChanged 3 months agoMITView on GitHub
Evaluate a proposed store for inclusion in Alex_ACT_Plugin_Mall using a quality scorecard
In this repository6 other files it gives its agents.
- .github/copilot-instructions.mdCopilot instructions
- currency-auditSkill
- mall-self-curationSkill
- meditationSkill
- source-inventorySkill
- staleness-disciplineSkill
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
- Very specific: line 71
On accept: add the entry to sources/supported-stores.json per source-inventory.
≈ “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.
Files
Store Evaluation
Score a proposed source store against five dimensions. Stores below the threshold are rejected. Stores above are added to sources/supported-stores.json via source-inventory.
When to Use
- A user invokes
/add-sourcewith a candidate store - Periodic re-evaluation of borderline entries (every 6 months)
The Five Dimensions
Score 0–2 per dimension. Maximum score is 10. Threshold for inclusion: ≥ 7.
1. Maintenance Health (0–2)
| Score | Criterion |
| 0 | Repo archived, no commits in 12+ months, last release > 2 years old |
| 1 | Active but low velocity — commits in last 6 months, no recent release |
| 2 | Active maintenance — commits in last 90 days OR active issue triage OR recent release |
2. Adoption Signal (0–2)
| Score | Criterion |
| 0 | Solo project, < 50 stars, no external contributors |
| 1 | Some adoption — 50–500 stars, occasional external contributors |
| 2 | Real adoption — > 500 stars OR multiple maintainers OR sustained external contribution |
(Stars are noisy; weight them with maintenance + license signal.)
3. License Clarity (0–2)
| Score | Criterion |
| 0 | No license, custom restrictive license, or unclear terms |
| 1 | Permissive license but with vendor-specific restrictions |
| 2 | Clean OSI-approved license (MIT, Apache 2.0, BSD, ISC, etc.) |
4. Catalog Fit (0–2)
| Score | Criterion |
| 0 | Off-topic — no clear use case for an AI agent that consumes plugins from this Mall |
| 1 | Tangential — useful in narrow scenarios |
| 2 | Direct fit — agents that use this Mall would install plugins from this source in normal workflows |
5. Documentation Quality (0–2)
| Score | Criterion |
| 0 | No README, broken docs, or installation steps unclear |
| 1 | Basic README, install instructions present, examples sparse |
| 2 | Quality README + examples + clear installation + troubleshooting |
Decision
| Total score | Verdict |
| 9–10 | Strong accept — add to the registry |
| 7–8 | Accept — add with a note of any caveats |
| 5–6 | Defer — request more signal (stars, contributors, time) and re-evaluate in 90 days |
| 0–4 | Reject — document the rationale in docs/curation-log.md |
On accept: add the entry to sources/supported-stores.json per source-inventory. The Mall does NOT clone or modify the upstream repo — it scores it and surfaces it in the catalog; agents install directly from upstream at a pinned ref.
Output Template
# Store Evaluation — <store name>
**URL**: <repo URL>
**Date**: YYYY-MM-DD
**Evaluator**: <name>
## Scores
| Dimension | Score | Notes |
|---|---|---|
| Maintenance Health | x/2 | ... |
| Adoption Signal | x/2 | ... |
| License Clarity | x/2 | ... |
| Catalog Fit | x/2 | ... |
| Documentation Quality | x/2 | ... |
| **Total** | **x/10** | |
## Verdict
Strong accept | Accept | Defer | Reject
## Rationale
<2-3 sentences>
## Proposed registry entry (if accepted)
```jsonc
{
"name": "<kebab-name>",
"remote": "https://github.com/<org>/<repo>",
"pluginDir": "<plugins | skills | . | other>",
"quality": "<official | community-curated | community | domain | reference>",
"provenance": false,
"license": "<SPDX-id or null>",
"added_at": "YYYY-MM-DD"
}ACT Pass Trail
<markers>
```Anti-Patterns
| Anti-pattern | Correction |
| Scoring on stars alone | Stars are one signal of five; weight with maintenance and fit |
| "I like it" override | The scorecard is the contract. Document overrides as ADRs |
| Rejecting without recording | Every rejection sets precedent; document or future similar proposals will repeat the cycle |
Falsifiability
This scorecard needs revision if any of the following occur within 90 days:
- Accepted stores (score >= 7) are repeatedly pruned for quality or maintenance failures
- Deferred stores (score 5-6) consistently pass later without new evidence, indicating scoring too strict
- Reviewers produce materially different scores for the same store with no documented rationale
Track these in docs/curation-log.md tagged [STORE-SCORING].
Related
- source-inventory — adding accepted stores to the registry
- staleness-discipline — pruning gates
/add-sourceprompt
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
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.

