Public record
Software health reportschema 0.27.0 · metrics 1.13.0 · 2026-07-27 22:27 UTC

CompOmics / ms2rescore

Modular and user-friendly platform for AI-assisted rescoring of peptide identifications

PythonApache-2.0★ 66 stars⑂ 22 forkssince Aug 2017View on GitHub ↗

CompOmics/ms2rescore holds a health index of 72 out of 100, placing it in the Good band. It scores highest on Engineering Quality (90/100) and lowest on AI Readiness (43/100). It was last updated 3 days ago. 2 contributors account for most of its recent work.

72
overall / 100
Good

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At risk ceiling of 49. AI Readiness sits outside the overall score.

72
Excellent85-100Exemplary; meets essentially all checked criteria
Good70-84Healthy; minor gaps
Moderate50-69Acceptable with notable gaps; review recommended
At risk30-49Significant weaknesses; adoption warrants caution
Critical1-29Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

Ownership

129 followers114 public repossince Mar 2015

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIms2rescore4.0.1-453 days agoms2rescorems2pipdeeplcproteomicsmass-spectrometrypeptide-identificationrescoringmachine-learning

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

79Good · 22% of overall
How it's scored
36/36Push recency — last push 3 days ago
13.2/36Commit cadence — 19/52 weeks with commits
18/18Commit volume — 210 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year210
human_commit_share1
days_since_last_push3
active_weeks_last_year19
How it's scored
27/27Ships releases — 43 releases published
36/36Release recency — latest release 3 days ago
19.8/27Release cadence — a release every ~78.8 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count43
latest_release_tagv4.0.1
releases_from_tagsno
days_since_latest_release3
mean_days_between_releases78.8

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

56Moderate · 18% of overall
How it's scored
29.4/60Stars — 66 stars
11/25Forks — 22 forks
3.9/15Watchers — 6 watchers
Inputs used
forks22
stars66
watchers6
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
has_contributingyes
has_issue_templateno
has_code_of_conductno
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

73Good · 24% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
11.5/22.5Commit distribution — top contributor authored 49% of commits
9.5/13.5Contributor breadth — 7 contributors
10/10OpenSSF Scorecard: Contributors — project has 6 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled7
top_contributor_share0.489
How it's scored
41.4/46.8Issue resolution — 89% of issues closed
32.1/38.3PR acceptance — 110/131 decided PRs merged
1.5/15OpenSSF Scorecard: Code-Review — Found 1/7 approved changesets -- score normalized to 1
Inputs used
merged_prs110
open_issues14
closed_issues109
issue_closed_ratio0.886
closed_unmerged_prs21
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
15.2/25Owner reach — 129 followers of CompOmics
25/25Track record — 114 public repos, account ~11 yr old
Inputs used
followers129
owner_typeOrganization
is_verified
owner_loginCompOmics
public_repos114
account_age_days4,143
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 3 days ago
20/20Version history — 45 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesms2rescore
ecosystemspypi
any_deprecatedno
min_days_since_publish3

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 20% of overall
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
16/20OpenSSF Scorecard: CI-Tests — 4 out of 5 merged PRs checked by a CI test -- score normalized to 8
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://ms2rescore.readthedocs.io
10/10Repository description
10/10Topics — 5 topics
0/10Wiki
Inputs used
topicsproteomics, ms2pip, percolator, deeplc, peptide-identification
has_wikino
homepagehttps://ms2rescore.readthedocs.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

58Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2/2.5CI-Tests — 4 out of 5 merged PRs checked by a CI test -- score normalized to 8
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-Review — Found 1/7 approved changesets -- score normalized to 1
2.5/2.5Contributors — project has 6 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
0.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate4.7
Excluded from scoring (no data or not applicable): branch_protection. Remaining weights renormalized.
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages69
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:ms2rescore@4.0.1 runtime dependency closure — what installing the published package pulls in — 69 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score.

43At risk · 0% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
32/40Legible commit history — 60 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
legible_history_share0.6
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config
0/11Static type checking
10/10Reproducible environment — Dockerfile, lockfile
4/10Demonstrated agent practice — 2 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
1/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileyes
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0.02
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
53.9/55Manageable file sizes — 1/49 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes116,571
source_files_sampled49
oversized_source_files1
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — examples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_files

Key facts

66GitHub stars
7contributors
210commits, last 12 months
3days since last push
43releases
2bus factor
14open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 22 ⇿
0Stars
22Forks
41Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

048121620242212019-082023-012026-06
Major 2Minor 3Patch 13

Each point covers 7 days.

OpenSSF Scorecard 4.7 / 10
4.7aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-07-27 22:27 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
8CI-Tests4 out of 5 merged PRs checked by a CI test -- score normalized to 8
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 1/7 approved changesets -- score normalized to 1
10Contributorsproject has 6 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
1Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 1
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 18
RegistryPackageVersion constraintManifest
PyPIcascade-config>=0.4.0pyproject.toml
PyPIclick>=7pyproject.toml
PyPIcustomtkinter>=5,<6pyproject.toml
PyPIdeeplc>=4.0.0pyproject.toml
PyPIim2deep>=2.0.1pyproject.toml
PyPIjinja2>=3pyproject.toml
PyPIlxml>=4.5pyproject.toml
PyPIms2pip>=4.2.0pyproject.toml
PyPIms2rescore_rs>=0.5.0pyproject.toml
PyPInumpy>=1.25,<3.0pyproject.toml
PyPIpackaging>=25.0pyproject.toml
PyPIpandas>=1pyproject.toml
PyPIplotly>=5pyproject.toml
PyPIpsm_utils>=1.1pyproject.toml
PyPIpyarrow>=14pyproject.toml
PyPIpyteomics>=4.7.2pyproject.toml
PyPIrich>=12pyproject.toml
PyPIristretto-ms>=0.3.0pyproject.toml
All dependencies 210

Full resolved dependency set from the GitHub dependency graph: 19 direct and 191 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIcascade-config0.4.0direct
PyPIclick8.4.2direct
PyPIcustomtkinter5.2.2direct
PyPIdeeplc4.0.0direct
PyPIim2deep2.0.2direct
PyPIjinja23.1.6direct
PyPIlxml6.1.1direct
PyPIms2pip4.2.0direct
PyPIms2rescore-rs0.5.0direct
PyPInumpy2.4.6direct
PyPInumpy2.5.1direct
PyPIpackaging26.2direct
PyPIpandas2.3.3direct
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PyPIpsm-utils1.5.5direct
PyPIpyarrow25.0.0direct
PyPIpyteomics4.7.5direct
PyPIrich14.3.4direct
PyPIristretto-ms0.3.0direct
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Dependency advisories 0

Installing pypi:ms2rescore@4.0.1 pulls in 69 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable
{
  "data": {
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          "headline": "Move back some functions only used in rescoring.py",
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          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:47:50Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "bcd475395ae8cc3e41c00a3336bb450cd5adbddd",
          "body": "… into fix/rescoring-module",
          "is_bot": false,
          "headline": "Merge branch 'release/4.0' of https://github.com/CompOmics/ms2rescore…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:39:43Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "7a52505fbf5d000501632f8e54153751a30aaa83",
          "body": "…to_utils.py",
          "is_bot": false,
          "headline": "Move package-level private rescoring functions to new module _ristret…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:39:33Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "45c45ae90c959631c34a17b0a5a73c997f10d302",
          "body": null,
          "is_bot": false,
          "headline": "end to end test",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-17T10:57:21Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "24fadb75b22e88d41258b33d973fe6524f07bc61",
          "body": null,
          "is_bot": false,
          "headline": "docs: update in-depth-python-api and userguide",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T10:23:12Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "0ab30b69551e98415c44dc7ed42af0991ad3719e",
          "body": "Replace mokapot rescoring engine with ristretto",
          "is_bot": false,
          "headline": "Merge pull request #268 from CompOmics/feat/add-ristretto",
          "author_name": "ArthurDeclercq",
          "author_login": "ArthurDeclercq",
          "committed_at": "2026-07-17T09:03:16Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "c255a3fcfd7fa6f8f0d4e05509e7dceba0ac7fdc",
          "body": null,
          "is_bot": false,
          "headline": "fix import error for python 3.12",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-17T08:40:04Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "59de3f31e2cda5fc086e12d11048967cb955805c",
          "body": "Review findings from the cumulative-diff review, addressed:\n\n- rescoring: null never actually disabled rescoring (CascadeConfig's\n  none_overrides_value=False silently ignores it), and the \"skip\n  rescoring\" path that was reachable only in principle would have\n  written no output at all. Rather than\n[…]\nreference).\n\nAlso reformats CHANGELOG.md's \"Breaking changes\" section (non-standard\nKeep a Changelog category) into the canonical Added/Changed/Removed\nsections, with breaking items tagged [BREAKING].",
          "is_bot": false,
          "headline": "Remove ability to skip rescoring; run infer_score_direction per-run",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T16:18:24Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "0f24e598a48cca9c51664b8af7140fcdd1e5711f",
          "body": "Cumulative summary of the mokapot -> ristretto rescoring engine\nmigration (report_fdr, model choice, auto score-direction inference,\noutput-file renames, GUI log.html fix, and the resulting config/API\nremovals), merged into the existing Unreleased section.",
          "is_bot": false,
          "headline": "Add ristretto migration entries to CHANGELOG",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T15:50:30Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "fba6411e330732d1456e1d74b3f18ec60e7bd5a1",
          "body": "…of-date",
          "is_bot": false,
          "headline": "docs: minor cleanup; temporarily remove overview figure as it is out-…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T15:34:56Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "95a9ee6928ed58e02447e5976d956eee0a0ea214",
          "body": "The GUI's logging setup only ever attached a plain FileHandler for\n<prefix>.log.txt -- unlike the CLI, it never used a recording Rich console\nor called save_html(), so <prefix>.log.html (documented as always written)\nwas silently never created for GUI-launched runs. Added the same\nConsole(record=Tru\n[…]\naises.\n\nAlso add an INFO-level log line when rescoring actually starts, since the\nper-fold/rollup/competition detail ristretto itself logs is moving to\nDEBUG level (fixed at the source, in ristretto).",
          "is_bot": false,
          "headline": "Fix missing HTML log file in GUI runs; log when rescoring starts",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T15:07:55Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "617cec085aabe9688be7903546a398aa21330356",
          "body": "…nal API surface\n\n- Fix rescore() always returning empty feature_weights/n_iterations: it\n  rebuilds the final result via ristretto.evaluate() (no training, so those\n  fields come back empty), discarding the real values from the earlier\n  ristretto.rescore() call. Now carried over explicitly, includ\n[…]\ne that explicit rather than presenting it as public API. Also\n  privatized build_features_dataframe and fix_constant_pep, which were\n  only ever called internally within the rescoring workflow itself.",
          "is_bot": false,
          "headline": "Fix empty feature weights, simplify output naming, and minimize inter…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T14:55:43Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "dc62a277fad5dce4ed1b70d317dd237a5f3bf71e",
          "body": "A user-provided rescoring dict (e.g. \"rescoring\": {} or {\"model\": \"lda\"})\nis passed through as-is by the config cascade -- same as feature_generators\nand psm_generator, where an override is taken at face value rather than\nmerged with defaults (CascadeConfig's max_recursion_depth stops it from\nreachi\n[…]\nig[\"rescoring\"] to ristretto.rescore()\nvia **kwargs instead of naming train_fdr/model explicitly, so ristretto's\nown defaults (train_fdr=0.01, model=\"svm\") apply to whatever the user's\nconfig omitted.",
          "is_bot": false,
          "headline": "Fix KeyError on partial/empty rescoring config",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T13:10:51Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "49104a38ba6de291222a2d0afb7b84d97f3c69a5",
          "body": "Clarifies alongside the existing null (skip rescoring) note that\nrescoring: {} is equivalent to omitting the key, since cascade_config\ndeep-merges schema defaults into a partial dict. Also fixes a stale\nmention that rescoring only exposed train_fdr (model was added earlier).",
          "is_bot": false,
          "headline": "Document that an empty rescoring object uses the schema defaults",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:51:32Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "aa7a2c013d105819b3e32cb160a4ee28f97ea26e",
          "body": "…e-ristretto-fixes",
          "is_bot": false,
          "headline": "Merge remote-tracking branch 'origin/feat/add-ristretto' into worktre…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:50:04Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "2610434c03c257e1a540b44e89ed151f902f327d",
          "body": "Adds a top-level report_fdr setting (default 0.01) used for console-logged\nidentification counts, HTML report stats/charts, and FlashLFQ output\nfiltering. This is independent of rescoring.train_fdr, which only controls\nristretto's semi-supervised training. Standalone report regeneration\n(ms2rescore-report) gains a --fdr option to override the report_fdr stored\nin the original run's full-config.json, without rerunning rescoring.",
          "is_bot": false,
          "headline": "Add report_fdr config option to replace hardcoded 1% FDR threshold",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:49:32Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "8d8aec14de9bd1e2498584cf2f6425f3adc136ac",
          "body": "… into feat/add-ristretto",
          "is_bot": false,
          "headline": "Merge branch 'release/4.0' of https://github.com/CompOmics/ms2rescore…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:35:36Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "7d8ed98782fb24870f9dd2259246c95dc9c28a3b",
          "body": "Add rescoring.model config option (svm, the default iterative\nPercolator-style SVM; or lda, a single-pass Fisher LDA that's faster\nbut less powerful on hard-to-separate datasets), threaded through to\nristretto.rescore()'s model kwarg. Adds a GUI dropdown alongside the\nexisting training-FDR field.\n\nR\n[…]\nthe jsonschema2md CLI's\npinned Python interpreter is missing on this machine (pre-existing\nenvironment issue, unrelated to this change) -- the generated\nsections were mirrored exactly by hand instead.",
          "is_bot": false,
          "headline": "Expose ristretto's model choice; always write rescoring tables",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:28:34Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "519a3bce4dadd38f3bbae60bbce0325c38d94e76",
          "body": "ristretto-ms>=0.3.0 is now on PyPI, so the local ../ironbrew editable\npath override is no longer needed. Removes the now-empty\n[tool.uv.sources] override and its TODO, and regenerates uv.lock to\nresolve ristretto-ms from the registry.",
          "is_bot": false,
          "headline": "Depend on published ristretto-ms instead of the local editable path",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:13:13Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "cfe700f86ce527f199509d02908d2949192a6e55",
          "body": "Rescoring result tables are now plain TSV (not Parquet) at every\nidentification level, and only the post-rescoring (\"after\") result is\nwritten -- \"before\" is reconstructed on demand from the main PSM\nlist's provenance data, so it's never persisted. Also documents the\nmax_psm_rank_output > 1 caveat (multi-rank rows, not statistically\nrigorous FDR) and drops the now-obsolete after_report paragraph.",
          "is_bot": false,
          "headline": "Update output-files.rst for the TSV-only, after-only rescoring tables",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T11:28:32Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "b153875ecc24fcf27c8c128467c138ad4cf65e6c",
          "body": "max_psm_rank_output is a special feature for surfacing ambiguous\nresults (e.g. mumble's multiple candidate peptidoforms per spectrum),\nnot for statistically rigorous FDR control when > 1 -- that trade-off\nis now explicit and applied consistently everywhere, instead of\ncomputing a separate always-ran\n[…]\nindexing returns the same underlying PSM\nobject references, not copies, so evaluate_before_from_provenance now\ntakes a real deep copy before mutating scores, to avoid corrupting\nthe caller's psm_list.",
          "is_bot": false,
          "headline": "Rework max_psm_rank_output and report reconstruction",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T11:27:32Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "6e425d19010c4fe6c40c969728a239c44523df98",
          "body": "build_features_dataframe now includes a \"run\" column, and every\nristretto.rescore()/evaluate() call passes run_col=\"run\" (requires\nristretto-ms>=0.3.0). Competition and CV splitting now group by\n(run, spectrum_id) at the source, instead of spectrum_id alone --\npreviously, two different physical spec\n[…]\nntirely: \"run\" now\ncomes back natively on every RescoreResult.psms from ristretto itself,\nso there's no more index-alignment hack needed on the ms2rescore side.\n\nBumps the ristretto-ms pin to >=0.3.0.",
          "is_bot": false,
          "headline": "Fix multi-run spectrum_id collisions via ristretto's new run_col",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T09:49:38Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "5a7706a3a105beba66ece6bb14458bddb7d61145",
          "body": "write_rescoring_tables() writes an additional set of *_after_report\nParquet tables when max_psm_rank_output > 1 (the rank-1 report view,\ndistinct from the multi-rank *_after files), which wasn't mentioned\nanywhere in the output-files reference.",
          "is_bot": false,
          "headline": "Document after_report Parquet output files",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T09:11:06Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "723a9a09d380ce588140dc97a62a400bd190202a",
          "body": "…n report\n\ncompute_id_stats/identification_overlap consumed before_result unmasked,\nunlike core.py's console-log count, which excludes mumble-generated\nalternate candidates via psm.metadata['original_psm']. Attach that\nmask onto before_result.psms itself (evaluate_before), the same way\nrun already i\n[…]\nfor that level.\n\nAdds direct unit coverage for both: _n_identified's masking behavior,\nand identification_overlap's run-based disambiguation (which the old\nbare-spectrum_id grouping would undercount).",
          "is_bot": false,
          "headline": "Apply mumble original_psm mask and run+spectrum_id key consistently i…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T09:10:35Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "96b8dc0ac4362a22dbcbb746b022ac17ce9245ee",
          "body": "fix_constant_pep previously only filtered result.psms, leaving\npeptidoform/peptide/protein rollups computed on the decoy-contaminated\npopulation -- _write_group_metadata would then look up stale\nscores/qvalues/PEPs for surviving PSMs. Now recomputes q-values, PEP,\nand rollups from scratch via ristre\n[…]\nt while the\nreal output looked fine. Split the fix into a result-only core\n(_fix_constant_pep_result) plus the existing psm_list-aware wrapper,\nand apply the core check to report_result independently.",
          "is_bot": false,
          "headline": "Refresh rollups in fix_constant_pep and apply it to report_result too",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T09:05:14Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "98c5d51e0dc150b5eddb4afd283f1d3f94a189d7",
          "body": null,
          "is_bot": false,
          "headline": "commit new lock file",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-16T08:13:47Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "4726d4d10685d9d9181b5b9849d85e48ae357a0b",
          "body": "fix_constant_pep now takes/returns a (PSMList, RescoreResult) pair;\nrescore() returns (psm_list, after_result, after_report_result).\nRescoreResult.psms now always carries a run column. Adds coverage for\nthe new report_result (rank-1 view, distinct from a multi-rank final\nresult) and the after_report Parquet table written when they differ.\nAlso asserts rescoring_tables_unavailable is set when the ristretto\nParquet tables are missing.",
          "is_bot": false,
          "headline": "Update tests for rescore()'s 3-tuple return and RescoreResult schema",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T22:14:47Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "9abf1c917111df815ddc0c3c881fb1acf1663019",
          "body": "The \"Rescoring result tables\" table had inconsistent column-divider\npositions across its border/header/content rows (four different\nwidths), which docutils/Sphinx rejects as a malformed table. Rebuilt\nit with consistent column widths (verified programmatically: every\nrow shares identical +/| column positions).",
          "is_bot": false,
          "headline": "Fix malformed RST grid table in output-files.rst",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T22:11:37Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "f6af833dac16dc1bec5b28bc6c25b4b5aec45672",
          "body": "…p levels\n\ncreate_psm_dataframe: filter psm_list to rank==1 (the report is always\na single-best-PSM-per-spectrum view, regardless of max_psm_rank_output)\nand merge before/after by (run, spectrum_id) instead of (spectrum_id,\npeptidoform). The old peptidoform-keyed merge produced NaN\nscore_before/qval\n[…]\n after_report table (rank-1 view) over after (which may be\nmulti-rank) when both exist. The overview tab shows an explicit notice\nwhen tables are unavailable instead of silently rendering empty stats.",
          "is_bot": false,
          "headline": "Rework report before/after comparison to (run, spectrum_id) + 4 rollu…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T22:09:00Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "27c4c4c98371b91736ed060ffb7cba8857223cf9",
          "body": "ristretto's RescoreResult schema has no room for a passthrough run\ncolumn, so attach it after the fact (index-aligned against the\noriginal PSMList) on before_result, final_result, and report_result.\nEnables merging before/after by (run, spectrum_id) in the report,\nguarding against spectrum_id collis\n[…]\nset when it differs from after (i.e. max_psm_rank_output > 1), so\nstandalone report regeneration (ms2rescore-report) can use the same\nalways-rank-1 view as a live run instead of the multi-rank result.",
          "is_bot": false,
          "headline": "Attach run to RescoreResult.psms; persist a separate after_report table",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T22:03:35Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "b6093887be54e585d636f6ca6f53b28b68e77171",
          "body": "psm.get_usi() bakes the peptidoform into the string. Once\nrename_to_usi overwrote psm_list's spectrum_id (before rescore()),\nristretto's spectrum-grouped competition -- keyed on spectrum_id --\nsaw every candidate peptidoform for a spectrum as its own singleton\ngroup, breaking competition whenever ma\n[…]\nd from psm_list).\n\nRestore the mumble-candidate exclusion (via the original_psm metadata\nflag) when counting identified PSMs before rescoring, matching what\nthe deleted _log_id_psms_before used to do.",
          "is_bot": false,
          "headline": "Fix USI/spectrum-grouping bug and always report at rank-1",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T21:44:37Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "d46c24d4acc6dd65d03edde28a5241cfbe51c503",
          "body": null,
          "is_bot": false,
          "headline": "removed local paths",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-15T16:01:14Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "fe4573eedf3ce3de6568933c014d1431135915e1",
          "body": "_write_group_metadata crashed with KeyError for decoy PSMs whenever\nprotein-level FDR used picked-protein competition (id_decoy_pattern\nset): ristretto's picked_rollup strips the decoy pattern from group\nkeys, so strip it the same way before the lookup. Log a warning when\nplain rollup runs instead (\n[…]\nw, and correctly\ncompetes multiple candidate PSMs per spectrum (relevant since this\nruns before rank filtering). Warn when the inferred direction is\nlower-is-better, since that's the less common case.",
          "is_bot": false,
          "headline": "Fix protein rollup KeyError and improve score-direction inference",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T16:00:03Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "84cf197f5cfa4899e280ee8dc10828eb35692e17",
          "body": null,
          "is_bot": false,
          "headline": "bugfix im2deep, updated dependency packages and lockfile",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-15T15:48:18Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "391450bcccecccf57a809789e8847a5ad2a0ea86",
          "body": "Swap ms2rescore/rescoring_engines/mokapot.py for a new\nms2rescore/rescoring.py built on ristretto's rescore()/evaluate().\nUpdate config schema and defaults for ristretto's train_fdr, and\ndrop lower_score_is_better in favor of automatic score-direction\ninference. Rework report generation to consume ristretto's\nbefore/after RescoreResult objects. Update docs, examples, and add\nintegration tests for the new pipeline.",
          "is_bot": false,
          "headline": "Replace mokapot rescoring engine with ristretto",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T15:47:03Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "b02dd3e1fda48c3f9b17ae57537baa970ee3031e",
          "body": "Mumble doc",
          "is_bot": false,
          "headline": "Merge pull request #266 from CompOmics/mumble-doc",
          "author_name": "ArthurDeclercq",
          "author_login": "ArthurDeclercq",
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                "key": "fuzzing",
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                "detail": "no direct dependency carries a known advisory",
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                "key": "no_advisories_left_outstanding",
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          },
          {
            "key": "malicious_dependencies",
            "band": "excellent",
            "name": "Malicious dependencies",
            "note": null,
            "notes": [],
            "value": 100,
            "inputs": {
              "source": "osv",
              "meaning": "reported as a malicious package by the OpenSSF corpus; the remedy is removal or moving off the compromised name, never an upgrade of the same artifact. Versions the registry has since pulled are listed but not scored",
              "packages": [],
              "red_flag": false,
              "assessed_packages": 69,
              "malicious_packages": 0,
              "direct_malicious_packages": 0,
              "withdrawn_malicious_packages": 0,
              "installable_malicious_packages": 0
            },
            "components": [
              {
                "key": "no_dependency_reported_as_a_malicious_package",
                "name": "No dependency reported as a malicious package",
                "detail": "no dependency is reported as a malicious package",
                "points": 100,
                "status": "met",
                "details": [
                  {
                    "code": "no_malicious_dependencies",
                    "params": {}
                  }
                ],
                "max_points": 100
              }
            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
            "band": "excellent",
            "name": "High-Risk Jurisdiction Exposure",
            "note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
            "notes": [
              {
                "code": "jurisdiction_evidence_limits",
                "params": {}
              }
            ],
            "value": 100,
            "inputs": {
              "meaning": "self-published location evidence; not nationality or citizenship",
              "red_flag": false,
              "exposures": [],
              "policy_countries": [
                "Russia",
                "Iran",
                "North Korea"
              ],
              "review_only_matches": 0,
              "assessed_self_published_locations": 6
            },
            "components": [
              {
                "key": "policy_exposure_multiplier",
                "name": "Policy exposure multiplier",
                "detail": "no confirmed policy-scope location match",
                "points": 100,
                "status": "met",
                "details": [
                  {
                    "code": "jurisdiction_no_match",
                    "params": {}
                  }
                ],
                "max_points": 100
              }
            ]
          }
        ],
        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
      },
      {
        "key": "ai_readiness",
        "band": "at_risk",
        "name": "AI Readiness",
        "value": 43,
        "weight": 0,
        "metrics": [
          {
            "key": "ai_agent_context",
            "band": "at_risk",
            "name": "Agent context & guidance",
            "note": null,
            "notes": [],
            "value": 32,
            "inputs": {
              "has_llms_txt": false,
              "legible_history_share": 0.6,
              "agent_instruction_files": [],
              "agent_instruction_max_bytes": null
            },
            "components": [
              {
                "key": "agent_instructions",
                "name": "Agent instructions",
                "detail": "no CLAUDE.md / AGENTS.md / editor rules",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_agent_instructions",
                    "params": {}
                  }
                ],
                "max_points": 45
              },
              {
                "key": "machine_readable_docs_llms_txt",
                "name": "Machine-readable docs (llms.txt)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 15
              },
              {
                "key": "legible_commit_history",
                "name": "Legible commit history",
                "detail": "60 of 100 human commits state their intent (structured subject or explanatory body)",
                "points": 32,
                "status": "partial",
                "details": [
                  {
                    "code": "legible_history",
                    "params": {
                      "legible": 60,
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
            "band": "at_risk",
            "name": "Verify loop (build / test / typecheck)",
            "note": null,
            "notes": [],
            "value": 48,
            "inputs": {
              "has_nix": false,
              "has_tests": true,
              "lockfiles": [
                "uv.lock"
              ],
              "has_dockerfile": true,
              "typed_language": false,
              "bootstrap_files": [],
              "has_devcontainer": false,
              "has_linter_config": true,
              "typecheck_configs": [],
              "agent_commit_share": 0.02,
              "toolchain_manifests": [],
              "dependency_bot_commit_share": 0
            },
            "components": [
              {
                "key": "one_command_bootstrap",
                "name": "One-command bootstrap",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 18
              },
              {
                "key": "automated_tests",
                "name": "Automated tests",
                "detail": null,
                "points": 22,
                "status": "met",
                "details": [],
                "max_points": 22
              },
              {
                "key": "lint_format_config",
                "name": "Lint / format config",
                "detail": null,
                "points": 11,
                "status": "met",
                "details": [],
                "max_points": 11
              },
              {
                "key": "static_type_checking",
                "name": "Static type checking",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 11
              },
              {
                "key": "reproducible_environment",
                "name": "Reproducible environment",
                "detail": "Dockerfile, lockfile",
                "points": 10,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "Dockerfile, lockfile"
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
                "detail": "2 of the last 100 commits agent-authored or agent-credited",
                "points": 4,
                "status": "partial",
                "details": [
                  {
                    "code": "agent_authored_commits",
                    "params": {
                      "count": 2,
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "no automated dependency updates observed",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_dependency_automation",
                    "params": {}
                  }
                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 1",
                "points": 1,
                "status": "partial",
                "details": [],
                "max_points": 10
              }
            ]
          },
          {
            "key": "ai_code_legibility",
            "band": "moderate",
            "name": "Code legibility for models",
            "note": null,
            "notes": [],
            "value": 54,
            "inputs": {
              "primary_language": "Python",
              "largest_source_bytes": 116571,
              "source_files_sampled": 49,
              "oversized_source_files": 1
            },
            "components": [
              {
                "key": "type_checkable_code",
                "name": "Type-checkable code",
                "detail": "Python without a type-check config",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_typecheck_config_language",
                    "params": {
                      "language": "Python"
                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
                "detail": "1/49 source files over 60KB",
                "points": 53.9,
                "status": "partial",
                "details": [
                  {
                    "code": "oversized_source_files",
                    "params": {
                      "kb": 60,
                      "sampled": 49,
                      "oversized": 1
                    }
                  }
                ],
                "max_points": 55
              }
            ]
          },
          {
            "key": "ai_interfaces",
            "band": "at_risk",
            "name": "Machine-readable interfaces",
            "note": null,
            "notes": [],
            "value": 40,
            "inputs": {
              "example_dirs": [
                "examples",
                "notebooks"
              ],
              "has_mcp_signal": false,
              "api_schema_files": []
            },
            "components": [
              {
                "key": "api_schema_openapi_graphql_proto",
                "name": "API schema (OpenAPI/GraphQL/proto)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 40
              },
              {
                "key": "mcp_server",
                "name": "MCP server",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 20
              },
              {
                "key": "runnable_examples",
                "name": "Runnable examples",
                "detail": "examples, notebooks",
                "points": 40,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "examples, notebooks"
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          }
        ],
        "description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "warnings": [
    "Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-27T22:27:21.075230Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/c/CompOmics/ms2rescore.svg",
  "full_name": "CompOmics/ms2rescore",
  "license_state": "standard",
  "license_spdx": "Apache-2.0"
}

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v1.13.0, schema v0.27.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.