Add CondonFM skill

README.md

@@ -55,6 +55,7 @@ flow as well. Skills are also discoverable by partner harnesses directly from th
 | **RFdiffusion** | De novo protein backbone and binder design (NIM). | [`rfdiffusion-nim`](nim-skills/rfdiffusion-nim) |
 | **Proteina-Complexa** | Protein binder design for protein and small molecule targets. Combines a pretrained flow-based generative model (built on La-Proteina) with inference-time optimization for high-quality binder generation. | [`complexa-setup`](open-models-skills/proteina-complexa/complexa-setup), [`complexa-target`](open-models-skills/proteina-complexa/complexa-target), [`complexa-design`](open-models-skills/proteina-complexa/complexa-design), [`complexa-sweep`](open-models-skills/proteina-complexa/complexa-sweep), [`complexa-evaluate-pdbs`](open-models-skills/proteina-complexa/complexa-evaluate-pdbs) |
 | **KERMT** | Pretrained graph neural network for molecular property prediction (ADMET). Multi-task extension of GROVER with accelerated data loading via cuik-molmaker. SOTA on real-world ADMET data. | [`kermt-setup`](open-models-skills/kermt/skills/kermt-setup), [`kermt-infer`](open-models-skills/kermt/skills/kermt-infer), [`kermt-embed`](open-models-skills/kermt/skills/kermt-embed), [`kermt-finetune`](open-models-skills/kermt/skills/kermt-finetune), [`kermt-continue-pretrain`](open-models-skills/kermt/skills/kermt-continue-pretrain), [`kermt-pretrain-scratch`](open-models-skills/kermt/skills/kermt-pretrain-scratch), [`kermt-add-cmim-pretrain`](open-models-skills/kermt/skills/kermt-add-cmim-pretrain), [`kermt-monitor`](open-models-skills/kermt/skills/kermt-monitor) |
+| **CodonFM** | Open codon foundation models: set up public Encodon checkpoints, extract coding-sequence embeddings, score masked-codon variants, and fine-tune on labeled data. | [`codonfm-setup`](open-models-skills/codonfm/codonfm-setup), [`codonfm-embed`](open-models-skills/codonfm/codonfm-embed), [`codonfm-score`](open-models-skills/codonfm/codonfm-score), [`codonfm-finetune`](open-models-skills/codonfm/codonfm-finetune) |
 | **Parabricks** | Agent-ready skills built on Parabricks for accelerated genomic analysis and workflows. | [`parabricks`](library-skills/parabricks), [`genomics-workflow-acceleration`](library-skills/genomics-workflow-acceleration) |
 | **nvMolKit** | GPU-accelerated cheminformatics library for molecular fingerprinting, Tanimoto/cosine similarity, Butina clustering, conformer generation (ETKDGv3), MMFF geometry optimization, and substructure search. | [`nvmolkit-usage`](library-skills/nvmolkit-usage) |
 | **cuEquivariance** | Build equivariant neural-network primitives (segmented tensor products, CG coefficients). | [`cuequivariance`](library-skills/cuequivariance) |

components.d/codonfm.yml

@@ -0,0 +1,14 @@
+# SPDX-License-Identifier: Apache-2.0 AND CC-BY-4.0
+# Copyright (c) 2026 NVIDIA Corporation. All rights reserved.
+
+name: CodonFM
+repo: NVIDIA-BioNeMo/CodonFM
+ref: main
+description: >-
+  Codon foundation-model setup, coding-sequence embeddings, masked-codon
+  variant scoring, and fine-tuning with public Encodon checkpoints.
+# Keep the model family at depth 1 under the group root, as with KERMT,
+# so the grouped catalog's orphan pruner recognizes its ownership.
+skills:
+  - path: skills
+    catalog_dir: open-models-skills/codonfm

docs/sync-findings.md

@@ -10,6 +10,27 @@ Audit date: 2026-07-16 (first dry-run sync, off `origin/main`).
 
 ## Sourced components (public GitHub)
 
+### CodonFM — `NVIDIA-BioNeMo/CodonFM@main`, path `skills`
+
+Audited 2026-10-08 at `26457c38a20a34c1eeb4b36b01c6aa879598a2bb`.
+
+- Four skills: `codonfm-setup`, `codonfm-embed`, `codonfm-score`, and
+  `codonfm-finetune`. Each carries an eval dataset, `BENCHMARK.md`,
+  `skill-card.md`, and `skill.oms.sig`. NVIDIA/skills' source-onboarding checks
+  pass for all four, including signed-file digests and measured PASS reports.
+- **SRC-4**: setup, scoring, and fine-tuning instructions contain Markdown
+  links to files outside the published skill tree, including `../../src/runner.py`
+  and `../../notebooks/4-EnCodon-Downstream-Task-riboNN.ipynb`. They resolve in
+  the product checkout but break in catalog copies. Fix these upstream using
+  public source links or explicit runtime paths in the user's CodonFM checkout,
+  then regenerate the source signatures before syncing the changes.
+- **SRC-9**: `skills/stage_eval_context.py` is source-repository fixture
+  maintenance tooling, not an installed skill. The grouped import preserves it
+  to match the upstream tree; it is not included in the generated aggregate.
+  Move it outside the published `skills/` container upstream.
+- Catalog copies intentionally preserve the published source bytes and signing
+  artifacts. Do not patch these portability issues in the vendored copies.
+
 ### nvMolKit — `NVIDIA-BioNeMo/nvMolKit@main`, path `agent-skills/nvmolkit-usage`
 - ✅ 1 clean, self-contained skill.
 - ⚠️ **SRC-2**: `path` points directly at a single skill dir, not a `skills/`

open-models-skills/codonfm/codonfm-embed/BENCHMARK.md

@@ -0,0 +1,122 @@
+# Skill Benchmark: codonfm-embed
+
+> ✅ **Overall verdict: PASS — Recommended for publication**
+
+## Publication Recommendation
+
+Recommended for publication based on the completed evaluation evidence in this report.
+
+## Evaluation Metadata
+
+- Skill: `codonfm-embed`
+- Evaluation date: 2026-10-07
+- Evaluator version: `1.5.6`
+- Agents: Claude Code (`aws/anthropic/bedrock-claude-opus-4-8`), Codex (`openai/openai/gpt-5.5`)
+- Tasks: 4 evaluation tasks (4 positive)
+- Dataset digest: `sha256:8ef619d6a9074d8b7fdc6224f220bb79aad6e37e2222f3290580290b7a6afbeb` (skill-evaluator-dataset-snapshot/1)
+- Attempts per task: 1
+- Environment: `k8s-sandbox`
+- Tier 2 evidence: required for publication
+- Tier 3 evidence: required for publication
+
+Each task attempt ran in its own isolated sandbox pod.
+
+## What This Report Answers
+
+The three-tier evaluation checks whether the skill:
+
+- is safe to use;
+- produces correct answers;
+- is discovered and activated when needed;
+- helps the agent complete the user's goal and expected workflow; and
+- avoids wasted skill and tool usage.
+
+## Results at a Glance
+
+| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
+|---|---:|---:|
+| Overall | 96.1% — baseline ran, but no comparable score was available; uplift unavailable | 93.4% — baseline ran, but no comparable score was available; uplift unavailable |
+| Security | 75.0% → 100.0% (+25.0 points) | 75.0% → 100.0% (+25.0 points) |
+| Correctness | 100.0% → 100.0% (±0.0 points) | 100.0% → 100.0% (±0.0 points) |
+| Discoverability | 96.3% — baseline ran, but no comparable score was available; uplift unavailable | 86.3% — baseline ran, but no comparable score was available; uplift unavailable |
+| Effectiveness | 85.6% → 90.6% (+5.0 points) | 98.8% → 93.8% (-5.0 points) |
+| Efficiency | 93.6% — baseline ran, but no comparable score was available; uplift unavailable | 86.9% — baseline ran, but no comparable score was available; uplift unavailable |
+
+**How to read this table:** baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.
+
+Example: `47.0% → 92.0% (+45.0 points)` means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.
+
+## Token Usage
+
+Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.
+
+| Agent | Dataset case | With skill | Without skill | Delta | Change | Coverage |
+|---|---|---:|---:|---:|---:|---|
+| claude-code | All cases | 1,152,349 | 5,695,914 | -4,543,565 | -79.77% | skill 4/4; base 4/4 |
+| claude-code | codonfm-embed-001 | 544,672 | 1,934,434 | -1,389,762 | -71.84% | skill 1/1; base 1/1 |
+| claude-code | codonfm-embed-002 | 243,222 | 950,364 | -707,142 | -74.41% | skill 1/1; base 1/1 |
+| claude-code | codonfm-embed-003 | 265,873 | 2,038,952 | -1,773,079 | -86.96% | skill 1/1; base 1/1 |
+| claude-code | codonfm-embed-004 | 98,582 | 772,164 | -673,582 | -87.23% | skill 1/1; base 1/1 |
+| codex | All cases | 487,427 | 1,838,975 | -1,351,548 | -73.49% | skill 4/4; base 4/4 |
+| codex | codonfm-embed-001 | 106,660 | 518,881 | -412,221 | -79.44% | skill 1/1; base 1/1 |
+| codex | codonfm-embed-002 | 159,116 | 322,872 | -163,756 | -50.72% | skill 1/1; base 1/1 |
+| codex | codonfm-embed-003 | 157,215 | 924,873 | -767,658 | -83.00% | skill 1/1; base 1/1 |
+| codex | codonfm-embed-004 | 64,436 | 72,349 | -7,913 | -10.94% | skill 1/1; base 1/1 |
+| ALL AGENTS | Dataset aggregate | 1,639,776 | 7,534,889 | -5,895,113 | -78.24% | skill 8/8; base 8/8 |
+
+Prompt tokens include cached reads, so total tokens are `prompt + completion` (cached is not added twice). The Efficiency score uses `(prompt - cached) + completion`. N/A means the relevant trajectory counters were not available; coverage is never estimated.
+
+## Tier Status
+
+| Tier | Purpose | Status | Evidence |
+|---|---|---|---|
+| Tier 1 | Static validation | **PASSED WITH OBSERVATIONS** | 11 validator(s); 3 finding(s) |
+| Tier 2 | Semantic deduplication | **PASSED** | 2 validator(s); 0 finding(s) |
+| Tier 3 | Live agent evaluation | **PASS** | 2 agent(s); 4 task(s) |
+
+## Findings and Observations
+
+<details>
+<summary>Show detailed findings and successful checks</summary>
+
+- **MEDIUM** QUALITY/quality_correctness: Instructions don't mention 'run_script' (`skills/codonfm-embed/SKILL.md`)
+- **MEDIUM** SECURITY/Unknown (LP3): MCP Least Privilege: Without declared permissions the skill's intent is opaque and cannot be validated. (`SKILL.md:1`)
+- **LOW** SCRIPT_LINT/magic_numbers: validate_inputs.py contains magic numbers (`skills/codonfm-embed/scripts/validate_inputs.py`)
+
+</details>
+
+## Scoring Methodology
+
+<details>
+<summary>Show dimension definitions, source signals, and thresholds</summary>
+
+| Dimension | Question | Scored signals |
+|---|---|---|
+| Security | Is it safe to use? | `security` (100%) |
+| Correctness | Is the answer correct? | `accuracy` (100%) |
+| Discoverability | Was the right skill loaded when needed? | `skill_execution` (100%) |
+| Effectiveness | Did the skill help complete the task? | `goal_accuracy` (50%) + `behavior_check` (50%) |
+| Efficiency | Did it avoid wasted tool calls and token usage? | `skill_efficiency` (50%) + `token_efficiency` (50%) |
+
+- Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
+- Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
+- Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
+- The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
+- Effectiveness is the equal-weight mean of goal completion (`goal_accuracy`) and expected workflow adherence (`behavior_check`).
+- Efficiency is 50% tool-call productivity (the backward-compatible `skill_efficiency` wire id) and 50% `token_efficiency`. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.
+
+Signals present in this run:
+
+- `security` (Security): unsafe operations, secret leakage, and unauthorized access.
+- `skill_execution` (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.
+- `skill_efficiency` (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).
+- `accuracy` (Accuracy): final-answer correctness against the reference answer.
+- `goal_accuracy` (Goal Accuracy): whether the user's goal was achieved.
+- `behavior_check` (Behavior Check): whether the expected workflow behavior was followed.
+- `token_efficiency` (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).
+
+</details>
+
+## Freshness
+
+Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.