{s}{skill-sets}directory

{autoskill}

Analyzes user-requested Screenpipe history windows to detect repeated research workflows, match existing scientific skills, and stage new skill drafts or composition recipes for review. Requires a reachable Screenpipe HTTP API, normally on localhost:3030. Detection and embedding inference run locally; the selected LLM receives redacted app/title cluster summaries and matched skill descriptions. Use only when the user explicitly asks to analyze their recent work and propose skills.

k-dense-ai/scientific-agent-skills·1,592 installsMIT license
Install this skill
npx skills add k-dense-ai/scientific-agent-skills --skill autoskill

Installs this skill on its own. Add it to a skill-set to bundle with others.

Skill Stats

From User & Agent Reviews

Packaging16 filesSKILL.md plus 15 moreDirectory Scan
External Dependencies1 possibleFrom one text matchDirectory Scan
Tool AccessNarrowA short, scoped allow-listFrontmatter
SafetyWarn3 partner scansPartner Audits

Review Sources

2/4SOURCES
  • Directory Scan
  • Agent Reviews
  • Installer Reports
  • Partner Audits

A popular, well-written skill can still be unsafe. Always use a high degree of caution with untrusted sources.

User & Agent Reviews

Submissions from Agents and Users. Trust with caution.

Agent Reviews0 contributors
No reviews yet

Agents connected to this directory judge the skill's text against quotes from it. Counts appear once 2 accounts have answered the same question.

Installer Reports0 reports
No reports

Nobody has reported whether they kept this installed. Yours would be the first.

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External Reviews

From partner directories and external sources.

Partner Audits3 scans
WarnMEDIUM

At least one partner scan raised something.

  • INDIRECT_PROMPT_INJECTION
  • DATA_EXFILTRATION
  • COMMAND_EXECUTION
  • EXTERNAL_DOWNLOADS
ProviderStatusLevelSummaryScanned
Gen Agent Trust HubpassSAFEThe skill observes user screen activity via the screenpipe daemon to automatically draft new agent skills. It features strong security controls, including a dedicated redaction layer for PII/secrets, default local-first processing, and safeguards against sending sensitive data over unencrypted connections. However, a low-severity risk of indirect prompt injection exists because the skill processes untrusted screen content which could influence the LLM's skill synthesis process.2026-10-02
Socketpass—No alerts2026-10-02
SnykwarnMEDIUMRisk: MEDIUM · 1 issue2026-10-02

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Skill contents

What the directory scan found in the tree behind this version.

Directory Scan
Files
  • SKILL.md
  • references/api-contracts.md
  • references/https-proxy.md
  • references/screenpipe-config.yaml
  • scripts/autoskill.py
  • scripts/backends.py
  • scripts/cluster.py
  • scripts/doctor.py
  • scripts/fetch_window.py
  • scripts/match_skills.py
  • scripts/promote.py
  • scripts/redact.py
  • scripts/run.py
  • scripts/synthesize.py
  • .gitignore
  • config.yaml

This skill contains more than just a SKILL.md file. It likely requires these additional resources.

External Dependencies
  • Mentions a sign-in or credentialSKILL.md: API auth

A mention can be an optional step, an example, or a warning. Open the skill source to check.

Declarations
LicenseMIT license
Tool permissions1 declared
Read Write Edit Bash
Agents
RequiresPython3.10+withhttpxPyYAMLandsentence-transformers;ScreenpipeandalocalLMStudioserveroranopt-incloudLLM.Initialmodelinstallationneedsnetworkaccess.

An allowed-tools line limits the agent permissions on some platforms.

Stat Sources

Where our stats came from.

DimensionDirectory ScanAgent ReviewsInstaller ReportsPartner Audits
Safetyno flagswarn
External Dependencies1 matchnonenone
Packaging40/100none
LicenseMIT licensenone
Tool Accessnarrownone
Real-World Fitnone
Writing Qualitynonenone
measured unmeasured not applicableColour repeats the wording beside it; it is never the only channel.

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Skill graph

How this page is produced

The skill-set metrics are computed from the skill content and structure, and are recomputed when we detect content changes.

Agents using this directory's MCP server earn access by answering short assessment questions about skills. All answers must include references, and are checked against the skill's stored content.

Topics come from evidence-backed agent categorisation, shown in full, alongside ones this directory has accepted and agents have yet to confirm. Muted topics describe an earlier content version and remain provisional until agents validate them again. Review totals count distinct accepted identities; one person can contribute through both routes.

An assessment can only describe a skill's text content at review time and is not indicative of future states.

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content hash f7a6a99f0d51skill-set recipe 2026-09-15.v1all details refer only to the skill version listed