Avoid Ai Writing

Pending first signal refresh

Agent Skills JavaScript Grade D Listed
15.0/100
Rank #239 of 331
Compare Avoid Ai Writing

Thin or incomplete trust evidence. Review carefully before production use.

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Listing state
Listed
Evidence coverage
Grade D · 1/5 signals
Last push
2026-08-24 · 2d ago
Recent change
New

Is Avoid Ai Writing safe? Avoid Ai Writing scores 15.0/100 (Grade D), ranked #239 of 331 tracked open-source AI agent projects, on evidence coverage D (1 of 5 independent signal types). The public evidence: no package-provenance attestation found; no OSSF Scorecard result yet; recent commits are unsigned; last pushed 2026-08-24. Every point is earned from checkable signals — never paid placement. How scoring works →

Ranked neighbours in Agent Skills

Quick Trust Read

What Would Improve It
Add or improve OSSF Scorecard coverage so safety checks are easier to verify.
Recent Changes
2026-08-26
Newly Listed
First tracked at rank #239
Maintainer Checklist
Add Scorecard coverage Expose the repository to OpenSSF Scorecard checks so supply-chain posture is easier to verify.
Publish provenance Add package provenance or release attestations so users can verify where shipped artifacts came from.
Increase signed commits Raise the share of verified-signed commits to make maintainer identity and release history easier to trust.
77.3
Activity sub-score · out of 100
#239

How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption. Grade D reflects the trust score band: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Evidence coverage D is separate — it grades how many independent signal types back the score (1 of 5), so a high score on thin evidence stays visible. Full methodology →

Signals refreshed 2026-08-26 18:33 UTC · Repo last pushed 2 days ago

Activity & Reach

Stars
3.3k
Forks
296
Last Push
2026-08-24
2 days ago
Commits (4 wk)
39
Downloads (7d)
HN mentions (30d)
Open Issues
19
Rank Change
NEW

Analysis

HVTrust Dimensions vs Agent Skills

15.0 / 100 · 40.0% confidence

Avoid Ai Writing Agent Skills average (331 agents)

Safety / Integrity50% OSSF Scorecard · 30% provenance · 20% signed commits
0 / 25
1.7 below avg 1.7
Identity / Provenance60% listing status · 40% build provenance
10.8 / 18
in line with avg 11.1
Transparency50% declared license · 50% OSSF Scorecard
0 / 17
4.4 below avg 4.4
Maintenance60% last-push freshness · 40% commit activity
18.3 / 20
3.8 above avg 14.5
AdoptionLog-scaled stars · package downloads
8.4 / 20
in line with avg 8.3

Activity Inputs

77.3 / 100
StarsRepository reach
21.1 / 30
FreshnessLast push recency
24.7 / 25
ActivityRecent commits
20.0 / 25
CommunityFork signal
11.5 / 20

Supply Chain Trust

Package Provenance
None
No package attestations found
OSSF Scorecard
Not available
Signed Commits
Unable to query

Is Avoid Ai Writing safe?

Public trust evidence for Avoid Ai Writing is thin: several supply-chain signals are missing or weak. This does not mean the project is unsafe — it means an outside observer cannot easily verify the usual integrity checks. Treat with extra scrutiny.
Does Avoid Ai Writing publish package provenance?
No published build provenance is currently detected for Avoid Ai Writing. This is common for open-source projects but means consumers cannot independently verify that the package on the registry matches the GitHub source.
Does Avoid Ai Writing have an OpenSSF Scorecard?
No OpenSSF Scorecard data is currently published for Avoid Ai Writing. Maintainers can enable the Scorecard GitHub Action to get a public score; without it, automated supply-chain hygiene is harder for outsiders to verify.
Is Avoid Ai Writing actively maintained?
Actively maintained. The repository was pushed to within the last 2 day(s).
What license does Avoid Ai Writing use?
Avoid Ai Writing ships under no SPDX license detected. A declared, OSI-approved license is one of the transparency signals HVTrust scores.

Not a safety endorsement. HVTracker describes what public signals show, not whether a project is safe for your use case. Run your own security review before adopting in production.

AI agent surface

MCP, providers, tool surface
Scored in HVTrust

These runtime-trust fields — detected from public repo docs and manifests — contribute a bounded adjustment to this project's HVTrust score alongside supply-chain evidence. The exact values each field can add or subtract are documented in the methodology → Compare this surface across every listed agent in the capability matrix →

MCP Server Support
None detected
No MCP server signal detected.
Detailed evidence is not shown in the public view.
External Service Dependencies
None detected
No clear third-party provider dependency detected.
Credential signal: No explicit API-key/config marker detected.
Tool / Plugin Surface
None detected
No clear plugin system or broad tool surface detected.
Detailed evidence is not shown in the public view.
Package Provenance Drift
N/A
No package source configured
Detailed evidence is not shown in the public view.
  • MCP signal live
  • External deps live
  • Tool / plugin surface live
  • Package provenance drift live

Maintain Avoid Ai Writing?

For maintainers

HVTrust scores Avoid Ai Writing from public signals only — we never contact maintainers first. If a signal is wrong, stale, or missing (provenance you publish, a Scorecard you run, signed releases), tell us and we'll review it. Corrections are public and tracked on GitHub.

Reputation Timeline

Signal history
Listed 1
2026-08-26
Newly Listed
First tracked at rank #239

Embed Badge Badge guide for maintainers →

For maintainers
HVTrust 15.0 Grade D
Markdown:
[![HVTrust](https://hvtracker.net/badge/avoid-ai-writing.svg)](https://hvtracker.net/agents/avoid-ai-writing)
HTML:
<a href="https://hvtracker.net/agents/avoid-ai-writing"><img src="https://hvtracker.net/badge/avoid-ai-writing.svg" alt="HVTrust"></a>

Other agents in Agent Skills

Data sources
GitHub REST API (repo, commits, stars, forks, license) · npm Registry (downloads, provenance)
Each agent's signals refresh once daily across 6 staggered batches. Methodology v4.3 · Raw JSON