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faceless-channel

Use only when the user asks to produce a finished multi-scene narrator-led video and explicitly requests a faceless channel, YouTube automation, narrated explainer/story/education, documentary, storybook or myth retelling, or kids song video. Topic alone is never enough: a generic video of/about something, including a historical topic, uses ordinary video generation. Do not use for planning or ideas, single clips, silent animation, image-to-video, footage edits, ads, product demos, or UGC. This workflow requires a consistent non-photoreal style, reusable assets, narrator voiceover, and burned subtitles.

Quality

87%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

81%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

An exceptionally actionable, well-gated workflow document: every phase has a pass/fail gate, every risky batch operation has a bounded retry and validation loop, and commands are copy-paste ready. The costs are token weight and repetition — 526 lines with duplicated golden rules restated inside the phase sections — plus a slightly over-indirected reference layout (three directory aliases for one folder, one second-hop reference).

Suggestions

Merge rules 16 and 18 (both forbid atempo/speed-change/pitch-shift) into one rule, and cut the Phase 3 'Shot-variety rules' block to a pointer at rule 22 instead of restating it — this is the largest conciseness win.

Move the 8-point 'OpenAI batch generation + stage review contract' and the 'ChatGPT intake policy' sections into a reference file (e.g. references/batch-contract.md alongside intake-and-dispatch.md), leaving a 5-10 line summary plus gates in the body, to bring the body closer to a true overview.

Collapse FACELESS_STYLES_DIR / FACELESS_MODES_DIR / FACELESS_FLOW_DIR into a single FACELESS_REFERENCES_DIR alias, and link vo_and_captions.md directly from the body (or fold it into the delegation notes for the narrator/subtitles skills) so every reference is exactly one hop from SKILL.md.

DimensionReasoningScore

Conciseness

At 526 dense lines the body is mostly high-value operational detail rather than concepts Claude already knows, but it could be noticeably tightened: rules 16 and 18 are near-duplicates ('Never atempo/speed-change/pitch-shift audio to fit' vs 'NO time-stretching in post. Never atempo/speed-up/slow-down/pitch-shift the audio to fit'), rule 2's cut-count rules are restated in Phase 3, rule 22's shot-variety rules are re-derived as the Phase 3 'Shot-variety rules', and rule 14 is expanded into the 8-point batch contract. Not level 2: almost every sentence carries platform-specific, non-inferable operational instruction, so there is little padded explanation; not level 4-5: the duplication and sheer inline volume (25 golden rules plus the full intake policy) still cost tokens on every load.

3 / 5

Actionability

Fully executable guidance throughout: exact preflight commands ('sandbox_exec({restart:true, command:"set -e; for b in ffmpeg ffprobe python3 curl jq awk; do command -v \"$b\" >/dev/null; done; ..."})'), exact validator invocations with exit-code semantics ('python3 ${HF_WORKFLOWS}/.../validate_motion_script.py --script script_manifest.json --duration-seconds {requested_seconds}' ... 'Exit code 1 BLOCKS every later generation phase'), a literal manifest JSON shape, numeric targets (9.4-9.8s speech per 10s block, 30-34 words per line, -16 LUFS, groups of at most six, timeout_seconds:25), and a retry ladder with an explicit ~8-attempt budget cap. Not level 4: the commands and parameters are copy-paste ready and cover the common failure cases, not just the happy path.

5 / 5

Workflow Clarity

The pipeline is sequenced 'Phases 0→8, in order' with a GATE after each phase, validation is explicit and blocking (preflight blocks the run; validator 'rewrite only the fields listed in invalid_blocks/errors, then rerun until valid:true'), feedback loops exist for every risky stage (NSFW retry ladder with seed retry → reword → reframing, submission-failure re-grouping, 20-minute stall escalation), and a FINAL QC CHECKLIST closes the process. Not level 4: batch operations have bounded retries, ledger reconciliation, and explicit terminal-state rules, which is exactly the anchor-5 pattern including the checklist.

5 / 5

Progressive Disclosure

Structure is good: all 11 markdown files referenced from the body exist in references/, are invoked with clear signals about when and why to open them ('Read ${FACELESS_FLOW_DIR}/references/intake-and-dispatch.md completely before starting Phase 0'), and long templates are correctly delegated out of the body. Not level 5: the 526-line body still inlines large blocks that arguably belong in references (the 8-point batch contract, the ChatGPT intake policy); the triple alias FACELESS_STYLES_DIR/FACELESS_MODES_DIR/FACELESS_FLOW_DIR points three names at one directory, adding indirection; and vo_and_captions.md is only reachable via a second hop (SKILL.md → generation-and-delivery.md), a shallow 2-level chain. Not level 3: references are clearly signaled, one level deep for everything the body names, and easy to navigate.

4 / 5

Total

17

/

20

Passed

Description

90%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An excellent trigger-focused description with explicit when-to-use, when-not-to-use, and disambiguation rules that minimize conflict risk. Its only weakness is that the capability statement ('what it does') is compressed to a single action plus deliverable requirements, leaving coverage of the workflow's concrete steps to the body.

Suggestions

Add one or two concrete capability actions after the trigger clause, e.g. 'Runs a phased pipeline: style-locked asset generation, 10s block-by-block clip generation, narrator voiceover, and burned-subtitle assembly into one final video' — this would lift specificity from 3 to 4-5 without hurting conciseness.

Optionally name the supported channel presets (Explainer, History, Kids, Picture Story, Fairy Tale & Myth) in the description so users can trigger on those natural words too.

DimensionReasoningScore

Specificity

The description names the domain and a single concrete action — 'produce a finished multi-scene narrator-led video' — plus deliverable requirements ('a consistent non-photoreal style, reusable assets, narrator voiceover, and burned subtitles'), but does not list several distinct actions like the anchor-4 example does. It is not level 2 because the action and its concrete requirements are specific rather than generic; it is not level 4/5 because the 'what' is compressed into one action while the bulk of the text is spent on when-to-use guidance.

3 / 5

Completeness

Both questions are answered explicitly with concrete trigger phrases: 'Use only when the user asks to produce a finished multi-scene narrator-led video and explicitly requests a faceless channel...' states the when, and 'This workflow requires a consistent non-photoreal style, reusable assets, narrator voiceover, and burned subtitles' plus the deliverable statement cover the what. Not level 4: the when-clause is not merely present but enumerated with synonyms and a 'Topic alone is never enough' disambiguation rule.

5 / 5

Trigger Term Quality

Comprehensive natural trigger phrases users would actually say: 'faceless channel', 'YouTube automation', 'narrated explainer/story/education', 'documentary', 'storybook or myth retelling', 'kids song video', plus explicit anti-triggers ('single clips, silent animation, image-to-video, footage edits, ads, product demos, or UGC'). Not level 4: no common variation of the domain's natural vocabulary is missing — the negative list doubles as synonym coverage.

5 / 5

Distinctiveness Conflict Risk

A clear niche with distinct triggers and an explicit exclusion list ('Do not use for planning or ideas, single clips, silent animation, image-to-video, footage edits, ads, product demos, or UGC') that prevents overlap with adjacent video skills. Not level 4: conflict risk is actively minimized, not just minor — the 'generic video of/about something, including a historical topic, uses ordinary video generation' rule resolves the most likely wrong-skill trigger.

5 / 5

Total

18

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (527 lines); consider splitting into references/ and linking

Warning

referenced_paths_exist

Referenced path issues: 1 missing, 1 deeper-than-1-level

Warning

Total

14

/

16

Passed

Repository

openai/plugins

Path

plugins/higgsfield/skills/faceless-channel/SKILL.md

Reviewed

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