Conciseness

The body is 356 lines with real padding: the ~90-line 'Common Issues' section re-covers failure cases already handled in Step 1 ('If a reachable backend returns 401 or 403, stop...') and the Version Compatibility section, and the SemVer rules are illustrated with five redundant worked examples ('Skill version 2.1.0 is compatible with Blueprint version 2.1.0 / 2.2.0 / 2.1.5...'). Most sections are efficient and AI-Q-specific, so it sits between 'mostly efficient' (3) and 'several padded sections' (2) — closer to 3 because the padding is concentrated rather than pervasive.

Actionability

Every step gives an exact, executable command ('python3 $SKILL_DIR/scripts/aiq.py chat "<USER_QUESTION>"', 'research_poll <JOB_ID>', 'status <JOB_ID>'), all of which exist in scripts/aiq.py, with expected output shapes stated ('{"status": "deep_research_running", "job_id": "<JOB_ID>"}'). This matches the anchor for fully executable, copy-paste-ready commands covering the common cases.

Workflow Clarity

The five steps are clearly sequenced (resolve backend → health check → send request → poll → present), with explicit validation checkpoints ('Run health before sending research requests', 'Stop on failed jobs and do not retry automatically') and feedback loops for every failure mode: unreachable backend, 401/403, incompatible version, and interrupted polling ('If has_report: true or job_status.status: success, fetch the report'). This matches the anchor for clear sequence with explicit validation and error-recovery loops; no destructive or batch operations apply.

Progressive Disclosure

Structure is good: the 'Available Scripts' table clearly signals the single bundle file and its arguments, the References table points one level deep to 'scripts/aiq.py' and '../aiq-deploy/SKILL.md' (both real, verified), and navigation is easy. It falls short of 5 because the ~90-line Common Issues and Version Compatibility sections are inlined in a 356-line SKILL.md where they could live in a references/ file, a 'minor organization gap' rather than the misplacement of the anchor-3 example.

Specificity

The description names the domain precisely ('a reachable NVIDIA AI-Q Blueprint backend') and one action ('run deep research or AI-Q research'), matching the anchor 'Names domain and 1-2 concrete actions, but not comprehensive'. It is above a 2 ('Processes PDF files'-level domain naming with no action) but below a 4 because it omits the concrete behaviors the body provides — submitting queries, polling async jobs, resuming interrupted jobs, presenting cited reports.

Completeness

Both parts are present and explicit: 'Use when asked to run...' clearly answers when, and 'run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend' answers what at a trigger level. It matches the anchor 'Has both what and when; when could be more explicit or specific' — here the what is the weaker half, since it says what to run rather than what the skill does (call the local helper script), keeping it below the 5 anchor's fully concrete pairing.

Trigger Term Quality

'deep research' and 'AI-Q research' are exactly the natural phrases users would say ('Use when asked to run deep research or AI-Q research...'), giving good keyword coverage. It falls short of the 5 anchor because natural variations the body itself lists — 'use AI-Q to answer...', 'ask AI-Q about...', 'research ...' — are absent from the description.

Distinctiveness Conflict Risk

The qualifier 'through a reachable NVIDIA AI-Q Blueprint backend' carves out a clear niche and distinguishes it from generic research skills, matching 'Mostly distinct; minor overlap risk with closely related skills'. The residual risk is that a plain 'deep research on X' request with no AI-Q context could match this skill instead of a general deep-research skill — minor, not the anchor-3 'could still overlap with similar skills' level.