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participation-warmup-planner

Use when the user asks to "plan the participation ramp before we promote", "how much account history or karma do we need in this community", or "design entry incentives and member lifecycle for our own Discord"; produces the per-community pre-promotion warming plan — account-history/tenure expectations (Estimated, named sources), a give-before-ask ledger spec, a per-community etiquette + rule digest with last-verified dates, and the warming → active graduation criteria that channel-registry requires as state-transition evidence — plus the owned-community variant (entry paths + member lifecycle for your own Discord/Slack/forum/企业微信私域). Not for launch-day submissions or T-0 threads — use community-launch-runner. 社区预热/先给后取/账号养成/毕业标准

70

Quality

89%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

78%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.

The body is a well-structured, mostly lean planning skill with concrete specs, clear sequencing, and proper externalization of detail. Repetition of the registry-writer guardrail and the absence of an explicit feedback loop on the batch propose step keep it just short of top marks.

Suggestions

Consolidate the repeated 'channel-registry is the sole writer of memory/channels/' + 'operation: propose via registry-events.py' guardrail into a single stated rule referenced once, instead of restating it in the Skill Contract, Instructions, Save Results, and Next Best Skill sections.

Add an explicit validate→fix→retry feedback loop for the batch 'operation: propose' submission in step 8 (e.g., confirm the event was accepted into channels.ndjson and re-propose on rejection), since proposing state-transition evidence is a batch write.

Tighten step 4's ledger spec by stating the concrete ratio-column example inline once and referencing it, rather than re-describing give/ask semantics in prose.

DimensionReasoningScore

Conciseness

Body is dense and assumes Claude's competence (no basic-concept padding), but the guardrail 'channel-registry is the sole writer of memory/channels/' and the 'operation: propose request to registry-events.py' phrasing recur across the Skill Contract, Instructions, Save Results, and Next Best Skill sections — a minor repetition that could be consolidated.

4 / 5

Actionability

Concrete, executable specifics throughout (ledger columns 'date, community, give/ask, link, note'; graduation example '≥N weeks tenure … zero rule strikes … first non-promotional post accepted without moderator action'; exact paths and connector scripts), with only minor abstraction left in step framing. As an instruction-only planning skill it appropriately substitutes concrete spec for code.

4 / 5

Workflow Clarity

An 8-step numbered sequence with explicit stops (NEEDS_INPUT routing in step 1), the cross-community rule-conflict check and Measured/User-provided/Estimated labeling as checkpoints, and a 'Done when' checklist. The batch propose-to-registry step lacks an explicit validate→fix→retry feedback loop, so it sits just below the top anchor.

4 / 5

Progressive Disclosure

Clear overview body with a dedicated Reference Materials section of well-signaled, one-level-deep links (echo-benchmark.md, owned-community-loop.md, CONNECTORS.md, SECURITY.md, and sibling skills); detailed reference content is appropriately externalized rather than inlined.

5 / 5

Total

17

/

20

Passed

Description

100%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.

The description is information-dense, third-person, and answers what/when/boundaries with concrete trigger phrases and explicit conflict disambiguation. Its only weakness is density bordering on verbosity, but no token is fluff.

DimensionReasoningScore

Specificity

Lists multiple concrete deliverables — 'account-history/tenure expectations (Estimated, named sources), a give-before-ask ledger spec, a per-community etiquette + rule digest with last-verified dates, and the warming → active graduation criteria' plus the owned-community variant — giving comprehensive, not vague, coverage.

5 / 5

Completeness

Explicitly answers both what ('produces the per-community pre-promotion warming plan …') and when ('Use when the user asks to …') with concrete trigger phrases, and adds an explicit out-of-scope clause ('Not for launch-day submissions or T-0 threads — use community-launch-runner').

5 / 5

Trigger Term Quality

Quotes natural user phrasings ('plan the participation ramp before we promote', 'how much account history or karma do we need in this community', 'design entry incentives and member lifecycle for our own Discord') plus Chinese synonyms (社区预热/先给后取/账号养成), covering common variations a user would actually say.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (pre-promotion warming ramp) and explicitly disambiguates from the sibling launch skill ('Not for launch-day submissions or T-0 threads — use community-launch-runner'), minimizing wrong-skill triggering.

5 / 5

Total

20

/

20

Passed

Validation

75%

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

Validation12 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 34 suspicious

Warning

referenced_paths_exist

Referenced path issues: 2 missing, 2 deeper-than-1-level

Warning

Total

12

/

16

Passed

Repository
aaron-he-zhu/aaron-marketing-skills
Reviewed

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