Add v3 scoring model (Diet Egg) and Fresh Egg advisory

.github/workflows/good-egg.yml

@@ -14,5 +14,4 @@ jobs:
       - uses: 2ndSetAI/good-egg@v1
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
-          scoring-model: v2
           skip-known-contributors: 'false'

README.md

@@ -3,8 +3,7 @@
   Good Egg
 </h1>
 
-Trust scoring for GitHub PR authors using graph-based analysis of
-contribution history.
+Trust scoring for GitHub PR authors based on contribution history.
 
 ## Why
 
@@ -122,23 +121,31 @@ See [docs/mcp-server.md](https://github.com/2ndSetAI/good-egg/blob/main/docs/mcp
 
 ## Scoring Models
 
-Good Egg supports two scoring models:
+Good Egg supports three scoring models:
 
 | Model | Name | Description |
 |-------|------|-------------|
-| `v1` | Good Egg (default) | Graph-based scoring from contribution history |
+| `v3` | Diet Egg (default) | Alltime merge rate as sole signal |
 | `v2` | Better Egg | Graph score + merge rate + account age via logistic regression |
+| `v1` | Good Egg | Graph-based scoring from contribution history |
 
-To use v2, set `scoring_model: v2` in your `.good-egg.yml`, pass
-`--scoring-model v2` on the CLI, or set `scoring-model: v2` in the action
-input. See [Methodology](https://github.com/2ndSetAI/good-egg/blob/main/docs/methodology.md#better-egg-v2) for how the
-v2 model works.
+v3 is the default. To use an older model, set `scoring_model: v1` or
+`scoring_model: v2` in your `.good-egg.yml`, pass `--scoring-model v1` on
+the CLI, or set `scoring-model: v1` in the action input. See
+[Methodology](https://github.com/2ndSetAI/good-egg/blob/main/docs/methodology.md) for how each model works.
+
+### Fresh Egg Advisory
+
+Accounts less than 365 days old receive a "Fresh Egg" advisory in the
+output. This is informational only and does not affect the score. Fresh
+accounts correlate with lower merge rates in the validation data.
 
 ## How It Works
 
-Good Egg builds a weighted contribution graph from a user's merged PRs and
-runs personalized graph scoring to produce a trust score relative to your
-project. See [Methodology](https://github.com/2ndSetAI/good-egg/blob/main/docs/methodology.md) for details.
+The default v3 model (Diet Egg) scores contributors by their alltime merge
+rate: merged PRs divided by total PRs (merged + closed). Older models (v1,
+v2) build a weighted contribution graph and run personalized graph scoring.
+See [Methodology](https://github.com/2ndSetAI/good-egg/blob/main/docs/methodology.md) for details.
 
 ## Trust Levels
 

action.yml

@@ -23,7 +23,7 @@ inputs:
     required: false
     default: 'false'
   scoring-model:
-    description: 'Scoring model to use (v1 or v2)'
+    description: 'Scoring model to use (v1, v2, or v3)'
     required: false
   skip-known-contributors:
     description: 'Skip scoring for authors with merged PRs in the repo (true/false)'
@@ -40,7 +40,7 @@ outputs:
     description: 'GitHub username that was scored'
     value: ${{ steps.score.outputs.user }}
   scoring-model:
-    description: 'Scoring model that was used (v1 or v2)'
+    description: 'Scoring model that was used (v1, v2, or v3)'
     value: ${{ steps.score.outputs.scoring-model }}
   skipped:
     description: 'Whether scoring was skipped for an existing contributor (true/false)'

docs/configuration.md

@@ -27,23 +27,24 @@ Configuration values are resolved in this order (highest priority first):
 
 ## Scoring Model
 
-Good Egg supports two scoring models. Set the model at the top level of the
-config file:
+Good Egg supports three scoring models. Set the model at the top level of
+the config file:
 
 ```yaml
-scoring_model: v1   # default -- graph-based scoring only
+scoring_model: v3   # default -- Diet Egg -- alltime merge rate as sole signal
 scoring_model: v2   # Better Egg -- graph + external features via logistic regression
+scoring_model: v1   # Good Egg -- graph-based scoring only
 ```
 
-When using v2, PR comments are branded "Better Egg" instead of "Good Egg".
-See [methodology.md](methodology.md#better-egg-v2) for how the v2 model
+PR comments are branded "Diet Egg", "Better Egg", or "Good Egg" depending
+on the model. See [methodology.md](methodology.md) for how each model
 works.
 
 ## Full YAML Schema
 
 ```yaml
-# Scoring model selection: v1 (default) or v2
-scoring_model: v1
+# Scoring model selection: v3 (default), v2, or v1
+scoring_model: v3
 
 # Skip scoring for authors who already have merged PRs in the target repo.
 # When true (the default), existing contributors get an EXISTING_CONTRIBUTOR
@@ -127,12 +128,19 @@ v2:
 
 ### scoring_model
 
-Selects the scoring model. Set to `v1` (default) for graph-only scoring or
-`v2` for the Better Egg combined model. When set to `v2`, the parameters
-under the `v2:` block are used and the graph construction is simplified
-(no self-contribution penalty, no language normalization in repo quality, no
-diversity/volume adjustment). Language match personalization weighting
-(`same_language_weight`) is retained in v2.
+Selects the scoring model. `v3` (default, Diet Egg) uses alltime merge rate
+as the sole signal with no graph construction. `v2` (Better Egg) combines a
+simplified graph score with merge rate and account age via logistic
+regression. `v1` (Good Egg) uses graph-based scoring only.
+
+When set to `v2`, the parameters under the `v2:` block are used and the
+graph construction is simplified (no self-contribution penalty, no language
+normalization in repo quality, no diversity/volume adjustment). Language
+match personalization weighting (`same_language_weight`) is retained in v2.
+
+v3 does not use graph construction, so the `graph_scoring`, `recency`,
+`edge_weights`, and `language_normalization` sections have no effect. The
+`thresholds` section still controls trust level classification.
 
 ### v2 (Better Egg)
 
@@ -215,7 +223,7 @@ The following environment variables override individual config values:
 | `GOOD_EGG_HIGH_TRUST` | `thresholds.high_trust` | float |
 | `GOOD_EGG_MEDIUM_TRUST` | `thresholds.medium_trust` | float |
 | `GOOD_EGG_HALF_LIFE_DAYS` | `recency.half_life_days` | int |
-| `GOOD_EGG_SCORING_MODEL` | `scoring_model` | str (`v1` or `v2`) |
+| `GOOD_EGG_SCORING_MODEL` | `scoring_model` | str (`v1`, `v2`, or `v3`) |
 | `GOOD_EGG_SKIP_KNOWN_CONTRIBUTORS` | `skip_known_contributors` | bool (`true`/`false`) |
 
 ## Programmatic Configuration

docs/github-action.md

@@ -37,7 +37,7 @@ This posts a trust score comment on each pull request:
 | `comment` | No | `true` | Post a PR comment with the trust score |
 | `check-run` | No | `false` | Create a check run with the trust score |
 | `fail-on-low` | No | `false` | Fail the action if trust level is LOW |
-| `scoring-model` | No | `v1` | Scoring model: `v1` (Good Egg) or `v2` (Better Egg) |
+| `scoring-model` | No | `v3` | Scoring model: `v3` (Diet Egg), `v2` (Better Egg), or `v1` (Good Egg) |
 | `skip-known-contributors` | No | `true` | Skip scoring for authors with merged PRs in the repo |
 
 ## Outputs
@@ -47,7 +47,7 @@ This posts a trust score comment on each pull request:
 | `score` | Normalized trust score (0.0 - 1.0) |
 | `trust-level` | Trust level: HIGH, MEDIUM, LOW, UNKNOWN, BOT, or EXISTING_CONTRIBUTOR |
 | `user` | GitHub username that was scored |
-| `scoring-model` | Scoring model used: `v1` (Good Egg) or `v2` (Better Egg) |
+| `scoring-model` | Scoring model used: `v3` (Diet Egg), `v2` (Better Egg), or `v1` (Good Egg) |
 | `skipped` | Whether scoring was skipped for an existing contributor (`true`/`false`) |
 
 ## Custom Configuration
@@ -179,9 +179,10 @@ jobs:
 
 You can check whether scoring was skipped via the `skipped` output.
 
-## Using Better Egg (v2)
+## Selecting a Scoring Model
 
-To use the v2 scoring model, set the `scoring-model` input:
+The default model is v3 (Diet Egg), which scores by alltime merge rate. To
+use an older model, set the `scoring-model` input:
 
 ```yaml
 jobs:
@@ -191,12 +192,12 @@ jobs:
       - uses: 2ndSetAI/good-egg@v0
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
-          scoring-model: v2
+          scoring-model: v2  # or v1
 ```
 
-When using v2, PR comments are branded "Better Egg" and include a component
-score breakdown showing graph score, merge rate, and account age
-contributions. The `scoring-model` output reflects which model was used.
+PR comments are branded according to the model: "Diet Egg" for v3, "Better
+Egg" for v2, "Good Egg" for v1. v2 and v3 include a component score
+breakdown. The `scoring-model` output reflects which model was used.
 
 You can also set `scoring-model` via the `GOOD_EGG_SCORING_MODEL` environment
 variable, but the input takes precedence.
@@ -209,5 +210,7 @@ See the [examples/](../examples/) directory for complete workflow files:
   that posts a PR comment
 - [strict-workflow.yml](../examples/strict-workflow.yml) -- comment, check
   run, and fail-on-low
+- [diet-egg-workflow.yml](../examples/diet-egg-workflow.yml) -- v3
+  scoring model (default) with component breakdown
 - [better-egg-workflow.yml](../examples/better-egg-workflow.yml) -- v2
   scoring model with component breakdown

docs/library.md

@@ -114,26 +114,22 @@ result = await score_pr_author(
 )
 ```
 
-### v2 (Better Egg) Configuration
+### Scoring Model Selection
 
-To use the v2 scoring model, set `scoring_model` on the config:
+The default model is v3 (Diet Egg). To use an older model, set
+`scoring_model` on the config:
 
 ```python
 from good_egg import GoodEggConfig, score_pr_author
 
-config = GoodEggConfig(
-    scoring_model="v2",
-    v2={
-        "graph": {"half_life_days": 180, "max_age_days": 730},
-        "features": {"merge_rate": True, "account_age": True},
-        "combined_model": {
-            "intercept": -0.8094,
-            "graph_score_weight": 1.9138,
-            "merge_rate_weight": -0.7783,
-            "account_age_weight": 0.1493,
-        },
-    },
-)
+# v3 (default) -- merge rate only
+config = GoodEggConfig()
+
+# v2 -- graph + merge rate + account age
+config = GoodEggConfig(scoring_model="v2")
+
+# v1 -- graph only
+config = GoodEggConfig(scoring_model="v1")
 
 result = await score_pr_author(
     login="octocat",
@@ -142,12 +138,13 @@ result = await score_pr_author(
     config=config,
 )
 
-# v2 results include component scores
+# v3 and v2 results include component scores
 if result.component_scores:
-    print(f"Graph score: {result.component_scores['graph_score']:.3f}")
-    print(f"Merge rate: {result.component_scores['merge_rate']:.3f}")
-    print(f"Log account age: {result.component_scores['log_account_age']:.3f}")
-    print(f"Normalized score: {result.normalized_score:.3f}")
+    print(f"Merge rate: {result.component_scores.get('merge_rate')}")
+
+# v3 includes a fresh account advisory
+if result.fresh_account and result.fresh_account.is_fresh:
+    print(f"Fresh account: {result.fresh_account.account_age_days} days old")
 
 print(f"Scoring model: {result.scoring_model}")
 ```
@@ -176,7 +173,7 @@ the following fields:
 |-------|------|-------------|
 | `user_login` | `str` | GitHub username that was scored |
 | `context_repo` | `str` | Repository used as scoring context |
-| `raw_score` | `float` | Pre-normalization score: graph score (v1) or logit (v2) |
+| `raw_score` | `float` | Pre-normalization score: merge rate (v3), logit (v2), or graph score (v1) |
 | `normalized_score` | `float` | Normalized score (0.0 - 1.0) |
 | `trust_level` | `TrustLevel` | HIGH, MEDIUM, LOW, UNKNOWN, BOT, or EXISTING_CONTRIBUTOR |
 | `account_age_days` | `int` | Age of the GitHub account in days |
@@ -185,9 +182,10 @@ the following fields:
 | `top_contributions` | `list[ContributionSummary]` | Top repositories contributed to |
 | `language_match` | `bool` | Whether the user's top language matches the context repo |
 | `flags` | `dict[str, bool]` | Flags (is_bot, is_new_account, etc.) |
-| `scoring_model` | `str` | Scoring model used: `v1` or `v2` |
-| `component_scores` | `dict[str, float] \| None` | Component breakdown (v2 only): `graph_score`, `merge_rate`, `log_account_age` |
+| `scoring_model` | `str` | Scoring model used: `v1`, `v2`, or `v3` |
+| `component_scores` | `dict[str, float]` | Component breakdown (v3: `merge_rate`; v2: `graph_score`, `merge_rate`, `log_account_age`) |
 | `scoring_metadata` | `dict[str, Any]` | Internal scoring details |
+| `fresh_account` | `FreshAccountAdvisory \| None` | Advisory for accounts under 365 days old (None for bots and existing contributors) |
 
 `TrustScore` is a Pydantic model, so you can serialize it:
 

docs/mcp-server.md

@@ -82,15 +82,17 @@ Returns the full trust score as JSON, including all fields from the
 |------|------|----------|-------------|
 | `username` | `string` | Yes | GitHub username to score |
 | `repo` | `string` | Yes | Target repository in `owner/repo` format |
-| `scoring_model` | `string` | No | Scoring model: `v1` (Good Egg, default) or `v2` (Better Egg) |
+| `scoring_model` | `string` | No | Scoring model: `v3` (Diet Egg, default), `v2` (Better Egg), or `v1` (Good Egg) |
 | `force_score` | `boolean` | No | Force full scoring even for known contributors (default: `false`) |
 
 **Returns:** Full `TrustScore` JSON with all fields (user_login,
 context_repo, raw_score, normalized_score, trust_level,
 account_age_days, total_merged_prs, unique_repos_contributed,
 top_contributions, language_match, flags, scoring_model, component_scores,
-scoring_metadata). When `scoring_model` is `v2`, the response includes
-`component_scores` with graph_score, merge_rate, and log_account_age.
+scoring_metadata, fresh_account). v3 includes `component_scores` with
+`merge_rate`. v2 includes `graph_score`, `merge_rate`, and
+`log_account_age`. The `fresh_account` field contains a Fresh Egg advisory
+for accounts under 365 days old (null for bots and existing contributors).
 
 ### check_pr_author
 
@@ -102,17 +104,21 @@ Returns a compact summary suitable for quick checks.
 |------|------|----------|-------------|
 | `username` | `string` | Yes | GitHub username to check |
 | `repo` | `string` | Yes | Target repository in `owner/repo` format |
-| `scoring_model` | `string` | No | Scoring model: `v1` (Good Egg, default) or `v2` (Better Egg) |
+| `scoring_model` | `string` | No | Scoring model: `v3` (Diet Egg, default), `v2` (Better Egg), or `v1` (Good Egg) |
 | `force_score` | `boolean` | No | Force full scoring even for known contributors (default: `false`) |
 
-**Returns (v1):**
+**Returns (v3, default):**
 
 ```json
 {
   "user_login": "octocat",
   "trust_level": "HIGH",
   "normalized_score": 0.82,
-  "total_merged_prs": 47
+  "total_merged_prs": 47,
+  "scoring_model": "v3",
+  "component_scores": {
+    "merge_rate": 0.82
+  }
 }
 ```
 
@@ -143,18 +149,18 @@ Returns an expanded breakdown with contributions, flags, and metadata.
 |------|------|----------|-------------|
 | `username` | `string` | Yes | GitHub username to analyse |
 | `repo` | `string` | Yes | Target repository in `owner/repo` format |
-| `scoring_model` | `string` | No | Scoring model: `v1` (Good Egg, default) or `v2` (Better Egg) |
+| `scoring_model` | `string` | No | Scoring model: `v3` (Diet Egg, default), `v2` (Better Egg), or `v1` (Good Egg) |
 | `force_score` | `boolean` | No | Force full scoring even for known contributors (default: `false`) |
 
-**Returns (v1):**
+**Returns (v3, default):**
 
 ```json
 {
   "user_login": "octocat",
   "context_repo": "octocat/Hello-World",
   "trust_level": "HIGH",
   "normalized_score": 0.82,
-  "raw_score": 0.0045,
+  "raw_score": 0.82,
   "account_age_days": 3650,
   "total_merged_prs": 47,
   "unique_repos_contributed": 12,
@@ -171,19 +177,26 @@ Returns an expanded breakdown with contributions, flags, and metadata.
     "is_bot": false,
     "is_new_account": false
   },
-  "scoring_metadata": {}
+  "scoring_model": "v3",
+  "component_scores": {
+    "merge_rate": 0.82
+  },
+  "scoring_metadata": {
+    "closed_pr_count": 10
+  },
+  "fresh_account": null
 }
 ```
 
-**Returns (v2):**
+**Returns (v1):**
 
 ```json
 {
   "user_login": "octocat",
   "context_repo": "octocat/Hello-World",
   "trust_level": "HIGH",
   "normalized_score": 0.82,
-  "raw_score": 0.2871,
+  "raw_score": 0.0045,
   "account_age_days": 3650,
   "total_merged_prs": 47,
   "unique_repos_contributed": 12,
@@ -200,12 +213,6 @@ Returns an expanded breakdown with contributions, flags, and metadata.
     "is_bot": false,
     "is_new_account": false
   },
-  "scoring_model": "v2",
-  "component_scores": {
-    "graph_score": 0.78,
-    "merge_rate": 0.91,
-    "log_account_age": 3.45
-  },
   "scoring_metadata": {}
 }
 ```