Public record
Software health reportschema 0.27.0 · metrics 2.5.0 · 2026-07-27 16:30 UTC

espressif / idf-component-manager

Tool for installing ESP-IDF components

PythonApache-2.0★ 63 stars⑂ 17 forkssince Sep 2021View on GitHub ↗
KindLibraryCommand-line toolhow this is determined

espressif/idf-component-manager holds a health index of 34 out of 100, placing it in the At Risk band. It scores highest on Engineering Quality (100/100) and lowest on Security (31/100). It was last updated today. A single contributor accounts for most of its recent work.

34
overall / 100
At Risk

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

34
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe 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.

The weighted overall 71 is calibrated to 84 on the published index scale (record calibration 2026-08-02). High-Risk Jurisdiction Policy applies a 50% multiplier to weighted overall health and gives it an At Risk ceiling of 34.

Ownership

Espressif SystemsOrganization
8,448 followers322 public repossince Oct 2014

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIidf-component-manager3.1.0-6813 days ago

Metrics by category

Vitality

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

98Exceptional · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
32.5/36Commit cadence — 47/52 weeks with commits
18/18Commit volume — 205 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year205
human_commit_share1
days_since_last_push0
active_weeks_last_year47

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 55 releases published
36/36Release recency — latest release 13 days ago
27/27Release cadence — a release every ~13.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count55
latest_release_tagv2.5.0
releases_from_tagsno
days_since_latest_release13
mean_days_between_releases13.9
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

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

59Moderate · 17% of overall
How it's scored
29.1/60Stars — 63 stars
10/25Forks — 17 forks
4.3/15Watchers — 7 watchers
Inputs used
forks17
stars63
watchers7
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
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services
has_pull_request_templateyes

Sustainability & Governance

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

62Moderate · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
9.3/22.5Commit distribution — top contributor authored 58% of commits
13.5/13.5Contributor breadth — 18 contributors
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled18
top_contributor_share0.585
How it's scored
38.2/42Issue resolution — 91% of issues closed
0/30PR acceptance — 0/8 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs0
open_issues9
closed_issues90
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.909
closed_unmerged_prs8
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): newcomer_pr_acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
25/25Owner reach — 8,448 followers of espressif
25/25Track record — 322 public repos, account ~11 yr old
Inputs used
followers8,448
owner_typeOrganization
is_verified
owner_loginespressif
public_repos322
account_age_days4,288

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 13 days ago
20/20Version history — 68 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesidf-component-manager
ecosystemspypi
any_deprecatedno
min_days_since_publish13

Engineering Quality

Are baseline engineering and documentation practices in place?

100Exceptional · 19% of overall

Engineering practices

100Exceptional
How it's scored
24/24CI workflows — 4 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
6.4/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigyes
has_linter_configyes
has_precommit_configyes
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://components.espressif.com/
10/10Repository description
10/10Topics — 2 topics
10/10Wiki
Inputs used
topicsesp-idf, package-management
has_wikiyes
homepagehttps://components.espressif.com/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

31At Risk · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — no data
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/30 approved changesets -- score normalized to 0
0.8/2.5Contributors — project has 1 contributing companies or organizations -- score normalized to 3
0/10Dangerous-Workflow — 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 9 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — no SAST tool detected
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate2.9
high_risk_jurisdiction_cap34
high_risk_jurisdiction_multiplier50
security_posture_after_multiplier14
security_posture_before_jurisdiction29
Excluded from scoring (no data or not applicable): ci_tests, packaging, signed_releases. Remaining weights renormalized. High-Risk Jurisdiction Policy applies a 50% multiplier and gives Security posture an At risk ceiling of 34.

Dependency advisories

100Exceptional
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_packages27
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:idf-component-manager@3.1.0 runtime dependency closure — what installing the published package pulls in — 27 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

43Weak · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
legible_history_share1
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
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileyes
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/255 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes48,189
source_files_sampled255
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — example, examples
Inputs used
example_dirsexample, examples
has_mcp_signalno
api_schema_files

Key facts

63GitHub stars
18contributors
205commits, last 12 months
0days since last push
55releases
1bus factor
9open 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 ★ / 17 ⇿
0Stars
17Forks
49Releases

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.

04812161622021-092023-122026-04
Major 2Minor 9Patch 32

Each point covers 5 days.

OpenSSF Scorecard 2.9 / 10
2.9aggregate

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 16:30 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
0Dangerous-Workflowdangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTno SAST tool detected
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 16
RegistryPackageVersion constraintManifest
PyPIpsutilpyproject.toml
PyPIrichpyproject.toml
PyPIrich-clickpyproject.toml
PyPIesp-pylibpyproject.toml
PyPIpyparsingpyproject.toml
PyPIruamel.yamlpyproject.toml
PyPIrequestspyproject.toml
PyPIrequests-filepyproject.toml
PyPIrequests-toolbeltpyproject.toml
PyPIjsonrefpyproject.toml
PyPIpydanticpyproject.toml
PyPIpydantic-corepyproject.toml
PyPIpydantic-settingspyproject.toml
PyPIpathvalidatepyproject.toml
PyPItyping-extensionspyproject.toml
PyPItruststorepyproject.toml
All dependencies 2

Full resolved dependency set from the GitHub dependency graph: 0 direct and 2 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIesp-docsindirect
PyPIurllib3indirect
Dependency advisories 0

Installing pypi:idf-component-manager@3.1.0 pulls in 27 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

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 v2.5.0, schema v0.27.0 — full methodology · metrics wiki.

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