Skill Selection Evals
Eval-only skill for measuring skill routing accuracy. Not invoked directly — contains selection evals that test whether the agent picks the correct skill for a given prompt.
Install
$ agentstack add skill-raddue-crucible-skill-selection-evals ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README — it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-raddue-crucible-skill-selection-evals) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming — see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps — measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Skill-Selection Evals
This is not an executable skill. It contains evaluation data for measuring the accuracy of skill selection (routing) decisions.
Purpose
Crucible's 52 execution evals measure quality once a skill is invoked. Selection evals measure whether the right skill gets invoked in the first place.
Eval Types
- Direct selection: Given a prompt, does the agent pick the correct skill?
- Negative selection: Given a prompt that sounds like skill X but is not, does the agent avoid the false positive?
- Context-dependent: Same verb, different context, different correct skill.
- Cascade ordering: Multi-skill tasks requiring correct invocation order.
Boundaries Tested
- test-methodology — TDD vs test-coverage vs adversarial-tester
- review-direction — temper vs review-feedback
- adversarial-scope — red-team vs inquisitor vs audit vs siege
- completion-claims — verify vs finish
- bug-handling — debugging vs verify vs audit
- build-vs-raw-dispatch — build (full idea→PR pipeline) vs a single-skill dispatch (planning, test-driven-development, …)
- ui-implement-vs-verify — mock-to-unity (implement/fix UI from a mock) vs ui-verify (verify / drift-delta report)
- onboarding-entry — getting-started (silent conversation-start bootstrap) vs workshop (on-demand catalog tour) vs project-init (whole-repo codebase onboarding)
- explore-vs-improve — recon (task-scoped investigation) vs project-init (whole-repo + cross-repo onboarding) vs prospector (architectural-friction discovery)
Difficulty Ratings
Each eval is rated easy/medium/hard based on routing ambiguity. This enables stratified baseline measurement — distinguishing between improvements that lift hard cases (high value) vs confirming easy cases already work (low signal).
See Also
evals/evals.json— the eval dataGRADING.md— grading criteria and baseline measurement protocol
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: raddue
- Source: raddue/crucible
- License: MIT
Install and usage instructions live in the source repository linked above.
Reviews
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Versions
- v0.1.0 Imported from the upstream source.