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wiki-agent

Query-driven targeted ingest from a specific AI agent's raw history. Use this skill when the user invokes /wiki-claude, /wiki-codex, /wiki-hermes, /wiki-openclaw, /wiki-copilot, /wiki-pi — with or without a search topic. Different from wiki-history-ingest (which bulk-ingests everything new): this skill finds sessions about a SPECIFIC TOPIC in a specific agent's history and ingests just those, then returns a synthesized answer immediately usable in the current session. Primary use case: you're working in agent A and want to pull in how you solved X in agent B's history. Cross-referencing, not archiving. Also trigger on: "what did I work on in codex about X", "search my claude sessions for Y", "pull in hermes knowledge about Z", "find that conversation where I did X in codex".

68

Quality

83%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

76%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.

A highly actionable, well-organized skill body that gives concrete commands, scoring, and templates for a multi-step cross-agent ingest workflow. Its main gaps are a redundant use-patterns section and the absence of an explicit validation/verification checkpoint on the batch vault-write step, which caps workflow clarity.

Suggestions

Add an explicit validation step after Step 5/Step 7 (e.g., verify each written page parses with required frontmatter and that .manifest.json entries match files processed) with a fix-and-retry loop, which would lift the batch-operation workflow-clarity cap.

Trim or fold the 'Cross-Agent Use Patterns' section, since its examples duplicate the Command Routing table and the no-query default already covered above.

Rephrase the description's primary-use-case clause in third person ('Use when working in agent A and needing context from agent B's past sessions') to avoid the specificity penalty for second-person voice.

DimensionReasoningScore

Conciseness

The body is mostly lean — agent routing, history-root, and extraction tables are dense reference material with concrete paths and signal fields rather than padded prose. The 'Cross-Agent Use Patterns' section largely restates the command routing table and could be trimmed, but overall it respects Claude's competence.

4 / 5

Actionability

Fully executable guidance throughout: an explicit scoring formula with constants ('score = base * (0.35 + 0.65 * 0.5 ** (age_days / 90))'), ripgrep commands ('rg -i "<query terms>" <session.jsonl>'), concrete per-agent file paths, a YAML frontmatter template, JSON manifest entries, and QMD bash commands — copy-paste ready and covering common cases.

5 / 5

Workflow Clarity

The Step 1–7 sequence is clear with some checkpoints (Step 1 existence guard, Step 5 dedup-against-existing-page check, Step 6 no-results fallback), but this is a batch operation processing multiple sessions and writing vault pages/tracking files with no explicit validate→fix→retry loop on the writes, so the rubric's batch-operation cap at 3 applies.

3 / 5

Progressive Disclosure

No bundle files exist (references/, scripts/, assets/ are absent), and the single SKILL.md is well organized with clear section headers; references to llm-wiki/SKILL.md and WRITING.md are clearly signaled and one level deep. The per-agent extraction tables are long but are the core operational content, leaving only minor organization gaps.

4 / 5

Total

16

/

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.

A strong description that clearly states what the skill does and when to use it, with comprehensive natural trigger phrases and explicit differentiation from the related bulk-ingest skill. The only weakness is second-person phrasing in the primary-use-case clause, which the rubric penalizes on specificity.

DimensionReasoningScore

Specificity

Lists several concrete actions ('Query-driven targeted ingest', 'finds sessions about a SPECIFIC TOPIC', 'ingests just those', 'returns a synthesized answer') matching the anchor for several specific actions, but the second-person phrasing ('you're working in agent A') triggers the rubric's one-point specificity penalty, capping it at 3.

3 / 5

Completeness

Explicitly answers both what ('Query-driven targeted ingest from a specific AI agent's raw history... ingests just those, then returns a synthesized answer') and when ('Use this skill when the user invokes /wiki-claude... Also trigger on: ...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive natural trigger coverage including the command synonyms (/wiki-claude, /wiki-codex, /wiki-hermes, /wiki-openclaw, /wiki-copilot, /wiki-pi) and the exact paraphrases a user would say ('what did I work on in codex about X', 'search my claude sessions for Y', 'pull in hermes knowledge about Z').

5 / 5

Distinctiveness Conflict Risk

Establishes a clear niche (targeted cross-agent ingest, 'Cross-referencing, not archiving') and explicitly differentiates from the sibling skill ('Different from wiki-history-ingest (which bulk-inests everything new)'), with distinct per-agent command triggers minimizing conflict risk.

5 / 5

Total

18

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
Ar9av/obsidian-wiki
Reviewed

Table of Contents

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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.