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SkillL2medium risk

Research Brief Skill

Skill profile for source-grounded research briefs with claims, citations, and gaps.

DocsDirectory
Curated baseline · 52

A curated public baseline; every entry carries a verification level and risk labels. 25 from the automated pipeline

66
Recommended
Confidence: medium
65
Benchmark
Confidence: medium
Trial candidateL2 · Seed profile normalized into AgentMaps schema.
Why
Recommendation 66, fits Research, setup is easy.
Best for
Developers using Claude for research workflows.
Not for
Users expecting a fully managed marketplace install flow.
Boundary
Verified to L2; L3/L4 not covered, so this is not full runtime proof.
Remaining risk
Risk appears manageable for personal developer workflows when configured narrowly.
Next step
Open the source docs and compare 2-3 candidates for your task before trial.
Ready for low-risk trial
Open the source docs and compare 2-3 candidates for your task before trial.

What it is good for

Skill profile for source-grounded research briefs with claims, citations, and gaps. AgentMaps treats this as a skill candidate and scores it with a capped benchmark score plus a separate recommendation score that includes trust, platform fit, setup preference, and risk preference.

Use cases

  • - Gather sources
  • - Compare claims
  • - Produce cited briefs

Best for

  • - Developers using Claude for research workflows.
  • - Teams that want visible setup, verification, and risk evidence before adoption.

Not for

  • - Users expecting a fully managed marketplace install flow.
  • - Users who need enterprise SSO controls.

Limitations

  • - Scenario-level L5-L7 benchmark testing is not part of the current MVP record.
AI-native runtime contractCopilot capability

Runtime pattern before adoption

Best used as a copilot capability; AI suggests and drafts, while the user owns execution and adoption.

Visible states

  • Collect task context and platform constraints
  • Preview setup, source, and permission boundaries
  • Trial in a sandbox or low-permission environment
  • Record trial result before team adoption

User controls

  • Revise task/platform filters
  • Add to Compare
  • Open source for review
  • Cancel high-risk adoption
  • Submit pending/staging evidence

Approval gates

  • External network target approval

Failure recovery

  • On trial failure, fall back to source docs, alternatives, or pending/staging evidence submission.

Trial acceptance

  • Can a user find a task-fit candidate within two minutes
  • Can the UI explain why it is recommended and where it does not fit
  • Can the user identify token, write, shell, network, or local file risk
  • Can the user separate L1/L2 evidence from full runtime proof
  • Does a failed trial have fallback or manual takeover

Verification evidence

The matrix shows passed, partial, skipped, and not-tested boundaries. It is not production adoption approval.

L1 Metadata

Source, docs, license, or package metadata exists.

passed
L2 Static audit

Static review assigns permission and risk boundaries.

passed
L3 Install path

Install path can be checked, but not necessarily in your environment.

skipped
L4 Interface

Interface or entrypoint parsed; not a production safety approval.

skipped
  • Seed profile normalized into AgentMaps schema.
  • Source and documentation fields are present.
  • Static risk flags are assigned.
  • Install verification pending.
  • Interface parsing pending.
  • Benchmark score capped at 65 by L2 verification.

Trust profile

Heuristic estimate
Needs reviewstaticTrigger risk medium

Risk findings

  • - External Network
  • - Citation Quality

Verified evidence

  • - Seed profile normalized into AgentMaps schema.
  • - Source and documentation fields are present.
  • - Static risk flags are assigned.
  • - Install verification pending.

Score breakdown

Safety74
Execution proxy76
Maintenance86
Setup89
Interface85
Standards82
Task fit88
Portability85

Why it scores well

  • - Clear task fit for the selected scenario.
  • - Static verification evidence is available.
  • - Portable across multiple AI clients.

Watch outs

  • - Scenario testing is still pending.
  • - Production adoption still needs local validation.

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