Logseq Review Workflow Eval
logseq/logseq
Compare two revisions of the Logseq logseq-review-workflow skill by running the same review prompt against isolated before and after skill snapshots, collecting both outputs, and producing a…
Accept an ordinary user question inside a DocMason workspace, route it to the right inner workflow, and preserve conversation-native logs automatically.
$ npx skills add JetXu-LLM/DocMason --skill ask -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install JetXu-LLM/DocMason ask --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/JetXu-LLM/DocMason.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/canonical/ask .claude/skills/ask && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ask" agent skill from https://github.com/JetXu-LLM/DocMason/tree/main/skills/canonical/ask into .claude/skills/ask/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ask", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/JetXu-LLM/DocMason/tree/main/skills/canonical/askType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add JetXu-LLM/DocMason --skill ask -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install JetXu-LLM/DocMason ask --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/JetXu-LLM/DocMason.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/canonical/ask .agents/skills/ask && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ask" agent skill from https://github.com/JetXu-LLM/DocMason/tree/main/skills/canonical/ask into .agents/skills/ask/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ask", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add JetXu-LLM/DocMason --skill ask -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install JetXu-LLM/DocMason ask --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/JetXu-LLM/DocMason.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/canonical/ask .cursor/skills/ask && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ask" agent skill from https://github.com/JetXu-LLM/DocMason/tree/main/skills/canonical/ask into .cursor/skills/ask/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ask", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/JetXu-LLM/DocMason.git --path skills/canonical/ask--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add JetXu-LLM/DocMason --skill ask -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install JetXu-LLM/DocMason ask --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/JetXu-LLM/DocMason.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/canonical/ask .gemini/skills/ask && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ask" agent skill from https://github.com/JetXu-LLM/DocMason/tree/main/skills/canonical/ask into .gemini/skills/ask/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ask", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install JetXu-LLM/DocMason askInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add JetXu-LLM/DocMason --skill ask -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/JetXu-LLM/DocMason.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/canonical/ask .github/skills/ask && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ask" agent skill from https://github.com/JetXu-LLM/DocMason/tree/main/skills/canonical/ask into .github/skills/ask/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ask", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add JetXu-LLM/DocMason --skill ask -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install JetXu-LLM/DocMason ask --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/JetXu-LLM/DocMason.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/canonical/ask .opencode/skills/ask && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ask" agent skill from https://github.com/JetXu-LLM/DocMason/tree/main/skills/canonical/ask into .opencode/skills/ask/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ask", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
askAccept an ordinary user question inside a DocMason workspace, route it to the right inner workflow, and preserve conversation-native logs automatically.
Ask is an agent skill from JetXu-LLM/DocMason. Accept an ordinary user question inside a DocMason workspace, route it to the right inner workflow, and preserve conversation-native logs automatically.
Its SKILL.md is about 7.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `workflow.json`).
It sits in Knowledge Management. The repository describes itself as: DocMason is a repo-native agent that turns your complex office files into a local LLM knowledge base and your second brain. The repo is the app. Codex is the runtime. The licence is Apache-2.0.
8 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 362417b. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Ask loads about 7.1k tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 3,327 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from JetXu-LLM/DocMason at commit 362417b, republished under its Apache-2.0 licence (© JetXu-LLM). 3,327 words, ~7,134 tokens.
.claude/skills/ask/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.ask is the canonical skill at skills/canonical/ask/SKILL.md.
It is the user-facing top-level workflow for ordinary natural-language requests.
Use this skill as the default top-level workflow for a new ordinary user request in this workspace unless the request is clearly explicit operator work.
The user should be able to ask naturally. They should not need to name internal workflow IDs or repository mechanics first.
If the request is clearly an explicit setup, status, sync, adapter-maintenance, or operator-review request, switch to the matching top-level workflow instead of forcing it through ask.
ask itself remains the only ordinary natural-language front door, and a request counts as ordinary front-door execution only after it has been opened as a canonical ask turn under this workflow.Plan Mode or a similar host planning/collaboration mode should not discourage the host from opening canonical ask for an ordinary user request; if the current mode still blocks ask, the host should use a concise in-band alert or approval question and continue the same user task in the most natural available way rather than silently defaulting to operator-direct or side-path work.Canonical Ask Contract below for the exact ordinary-ask opening and completion rules. Generated adapter guidance may restate that contract, but it does not replace this workflow surface, and compatible hosts must use that supported entry surface rather than helper stitching, ad hoc probing, or direct lifecycle calls.retrieve or trace remain legal operator tools, but they do not complete the ordinary ask contract by themselves.ask owns front-door legality, same-turn governance, workspace gating, and routing.
The routed inner workflow owns the deeper evidence loop.
native turn means the host's own chat turn before DocMason opens canonical ask handling.canonical ask turn means the governed DocMason turn for the current request.runtime ownership means the current request has been opened into that canonical ask turn and is now governed by DocMason.native ledger means host-side audit capture that is not canonical ask truth by itself.interaction-ingest means the runtime holding area for reconciled host activity before any governed promotion.runtime/answers/, runtime/runs/, and runtime/logs/.workflow.json remains routing metadata. It does not define the executable host call contract.conversation_id, turn_id, run_id, answer_file_path, and log_contextfront_door_state = canonical-askcat <<'JSON' | ./.venv/bin/python -m docmason _ask
{ ...payload... }
JSONopenprogressfinalizeopen request envelope:{
"action": "open",
"question": "<user question>",
"host_provider": "<provider>",
"host_thread_ref": "<stable host thread ref>",
"host_identity_source": "<host identity source>",
"semantic_analysis": {
"question_class": "answer",
"question_domain": "workspace-corpus",
"route_reason": "<one concise reason>"
}
}semantic_analysis is best-effort. For an ordinary native Codex ask, question_class, question_domain, and one concise route_reason are usually enough.question_class chooses workflow shape. It is not a difficulty score: use answer for a direct answer, explanation, or source-backed summary; use composition for a new evidence-backed work product or synthesis; use retrieval, provenance, or runtime-review only for explicit evidence, citation, or runtime-review requests.question_domain chooses evidence basis. It is not a mirror of question_class: prefer workspace-corpus, external-factual, or general-stable when one of those is the real basis, and reserve composition for composition-shaped evidence planning rather than setting it merely because question_class = composition.question_class = composition with question_domain = workspace-corpus.open normalizes missing supported routing fields, derives defaults such as support_strategy, and may refine reference resolution or workspace notices from repository truth..venv is available, call hidden open directly with best-effort semantic_analysisCODEX_THREAD_ID identity from the execution environment; do not hand-fill placeholder host thread references such as codex-desktop-thread or codex-native-threadworkspace-status, workspace-bootstrap, source search, implementation source, or tests first just to decide whether canonical ask may open, whether a named source exists, or which semantic_analysis fields are acceptedopen returns a governed blocker, waiting state, or explicit operator routeopen, preserve the returned conversation_id, turn_id, run_id, answer_file_path, log_context, and support_contract for the rest of the same canonical ask turn.execute means canonical ask is open and the host should continue through the chosen inner workflow.awaiting-confirmation means pause the same turn and wait for the user's confirmation reply.awaiting-user-decision means pause the current work for a material decision that only
the user can authorize. It never implies a timeout, default, or skip.waiting-shared-job means pause the same turn and wait for governed shared-job settlement.completed means the turn is committed and a final business answer may be returned.boundary means the turn is committed as a governed boundary and that boundary reply may be returned.blocked means no final business answer may be returned yet.next_step is a derived convenience field and should stay aligned with that status law:execute -> continue-inner-workflowawaiting-confirmation -> wait-for-user-confirmationawaiting-user-decision -> wait-for-user-decisionwaiting-shared-job -> wait-for-shared-jobcompleted -> return-final-answerboundary -> return-boundary-answerblocked -> do-not-return-final-answerresult_explanation is a derived convenience field, not a new truth surface.result_explanation.show_to_user = true for a blocker or evidence boundary, translate its summary, why, and next_step into one concise user-facing explanation in the user's language.user_status_line.show_to_user = false, do not add extra result-explanation prose.result_explanation text into the canonical answer markdown.admissibility_repair is present only for same-turn repairable finalize failures.open or same-turn reuse already performs governed preanswer work:open, treat the returned status, question_class, question_domain, analysis_origin, route_reason, inner_workflow_id, support_strategy, reference_resolution, source_scope_policy, support_contract, and notices as the source of truth for the next step. Do not re-derive them from side-path probing before honoring that result.docmason retrieve or docmason trace inside the same canonical ask turn, export each returned log_context field as DOCMASON_<FIELD> and then call the public command normally--json --compact for interactive inspection; if full nested retrieve or trace detail is genuinely needed, redirect full --json to a local file and inspect it selectively instead of loading the raw payload straight into the live chat contextjq assumptions such as .matchessession_idresultsreference_resolutionsource_scope_policytrace_idsession_idanswer_statereference_resolutionsource_scope_policyissue_codesprepare_ask_turn(), complete_ask_turn(), or trace_answer_file(...) as a substitute for the supported pathexport DOCMASON_CONVERSATION_ID="<conversation_id>"
export DOCMASON_TURN_ID="<turn_id>"
export DOCMASON_RUN_ID="<run_id>"
export DOCMASON_ENTRY_WORKFLOW_ID="ask"
export DOCMASON_INNER_WORKFLOW_ID="<inner_workflow_id>"
export DOCMASON_FRONT_DOOR_STATE="canonical-ask"
./.venv/bin/python -m docmason retrieve "<query>" --json --compact
./.venv/bin/python -m docmason trace --answer-file "<answer_file_path>" --json --compactprogress request envelope:{
"action": "progress",
"conversation_id": "<conversation_id>",
"turn_id": "<turn_id>",
"completion_status": "covered | blocked",
"hybrid_refresh_summary": { "...": "..." }
}{
"action": "progress",
"conversation_id": "<conversation_id>",
"turn_id": "<turn_id>",
"decision_frontier": {
"class": "judgment-authority-gap | high-cost-expression-choice",
"question": "<material decision question>",
"why_user_is_required": "<why evidence and reasoning cannot decide it>",
"evidence_boundary": "<what evidence establishes and cannot decide>",
"options": [
{
"id": "<stable option id>",
"label": "<short label>",
"impact": "<real downstream consequence>",
"recommended": true
},
{
"id": "<stable option id>",
"label": "<short label>",
"impact": "<real downstream consequence>",
"recommended": false
}
],
"affected_outputs": ["<specific model or artifact scope>"],
"invalidates": []
}
}A legal gate has two or three real options, explains their downstream impact, marks exactly one current recommendation, and names the affected output scope. Do not persist a vague or non-material question.
After progress returns awaiting-user-decision, present the same gate through the host's
native Plan or structured-input UI, without a timeout or default, and wait. If no such UI is
available, ask the one material question concisely in-band.
The user's answer opens a new linked turn with continuation_type=decision-resolution, the
exact gate_id as resolves_gate_id, and either one legal option_id or a non-empty
free_form decision. Never settle the old turn in place or reinterpret an unrelated message
as consent.
completion_status is optional when the caller is only re-entering a waiting-shared-job turn to let the hidden wrapper reconcile deterministic repo-owned shared-job truth.
supply completion_status only when the host is actively settling a still-unsettled governed multimodal refresh.
when the current-turn hybrid_refresh_work.json lists render or focus-render assets and the host can inspect images, inspect the relevant assets lightly and include render_inspection_used plus inspected_render_assets in hybrid_refresh_summary.
when a turn is paused in waiting-shared-job, re-enter through hidden open reuse, hidden progress, or hidden finalize; do not grep runtime/control_plane/ or shared-job files manually.
finalize request envelope:
{
"action": "finalize",
"conversation_id": "<conversation_id>",
"turn_id": "<turn_id>",
"answer_text": "<exact final business answer>",
"answer_file_path": "<answer_file_path>",
"response_excerpt": "<short excerpt>",
"session_ids": ["<selected_session_id>"],
"trace_ids": ["<selected_trace_id>"],
"workflow_outcome": {
"support_basis": "kb-grounded | mixed | external-source-verified | model-knowledge | governed-boundary",
"session_ids": ["<selected_session_id>"],
"trace_ids": ["<selected_trace_id>"],
"bundle_paths": ["<composition bundle path>"]
}
}answer_text is the preferred terminal input. The wrapper writes it atomically to the
canonical answer path, binds the exact digest, reuses only a digest-matching trace, and runs
one exact trace when necessary. The successful response keeps answer_text clean and returns
one separate user_status_line; internal IDs and support state remain in governance_detail.
When the exact final answer is already present at answer_file_path, callers may omit
answer_text and use the file-based handshake below.
session_ids and trace_ids are optional advanced-caller fields. Omit them when the turn has exactly one ask-owned retrieve session and one final trace candidate. Supply them only when the caller has already selected the canonical pair among multiple ask-owned candidates.
workflow_outcome is the preferred finalize-time handoff for workflow-owned facts. Supply it when the inner workflow already knows the correct support_basis, selected session_ids / trace_ids, support-manifest linkage, bundle linkage, or bounded degradation metadata. Older callers may keep using the compatible top-level finalize fields.
Legal closure handshake for one canonical ask turn:
./.venv/bin/python -m docmason trace --answer-file "<answer_file_path>" --json --compact
cat <<'JSON' | ./.venv/bin/python -m docmason _ask
{
"action": "finalize",
"conversation_id": "<conversation_id>",
"turn_id": "<turn_id>",
"answer_file_path": "<answer_file_path>",
"response_excerpt": "<short excerpt>"
}
JSONcompleted or boundary permits a final business reply to the userexecute, awaiting-confirmation, awaiting-user-decision,
waiting-shared-job, and blocked do notIf the environment cannot satisfy those capabilities, stop and explain the blocker instead of improvising.
ask turn, and run steps 2 through 4 as the governed open or same-turn reuse phase before any inner workflow execution.Canonical Ask Contract.open, form a thin agent-authored work_brief from the user's actual goal
and medium; include only fields that matter for this turn: deliverable and use, audience,
in/out scope, success criteria, distinctions that must survive, named evidence and
exemplars, confirmed method/storyline/expression grammar, and high-cost artifact risksemantic_analysis; keep one concise route_reason, and
set needs_latest_workspace_state only when fresh local workspace truth is
actually required, and include compact evidence_requirements only when the question
needs channel guidancerevision_of turn id and
the narrow continuation_type, revision_scope, and affected_outputs; adjacency in the
same chat is not sufficient authority to inherit evidence or accepted scopesresolves_gate_id plus either a
legal option_id from that gate or an explicit non-empty free_form decision; do not
reinterpret arbitrary later text as gate approvalopen normalize supported routing fields, resolve user-native source references when the user names a document, path, page, slide, sheet, heading, or similar locator, and return the governed turn bindingopen or same-turn reuse, let the governed ask path choose the smallest evidence basis that can support the answer correctly and truthfully before deeper workflow execution.workspace-corpus -> KB-firstcomposition -> KB-first with explicit evidence planningexternal-factual -> web-firstgeneral-stable -> model knowledge when the boundary is explicitopen, check workspace state only when the answer really depends on workspace truth.runtime/bootstrap_state.json as the cached readiness markerself-containedself-contained means the repo-local steady-state runtime is trusted for ordinary ask workmixed or degraded, let the ask helper repair or surface the governed boundary instead of answering from a partially trusted runtimeknowledge-base-sync instead of bluffing a workspace-grounded answeroriginal_doc/ rather than git-tracked repo searchawaiting-confirmationyes or no replies as approve or declineyes continues the same taskno commits the same turn as abstained + governed-boundaryprepared, awaiting-confirmation, or waiting-shared-job, prefer reusing that governed state over rerunning shared-state mutationwaiting-shared-job or awaiting-confirmation, do not bypass the governed path by answering from original_doc/, knowledge_base/staging/, or .staging-buildstatus = execute, let the canonical ask turn continue through the narrowest ask-routed inner workflow that matches the ask.workspace-bootstrap or knowledge-base-sync, honor that governed state first instead of forcing one of the ask-routed evidence workflowsgrounded-answergrounded-compositionretrieval-workflowprovenance-traceruntime-log-reviewretrieve, trace, raw-source inspection, or internal helper probing may support that live turn, but they do not replace canonical ask ownership or its completion rulesfocus_render_assets or render spans when the question is genuinely visual or layout-sensitive, and only then consider governed refresh or source fallbacksupport_contract:work_brief, revision lineage, accepted scopes, and turn
evidence packet before deciding whether evidence work is necessaryevidence-gap, reasoning-uncertainty, and
judgment-authority-gap: solve the first two autonomously; persist a decision gate only
for the third or for a genuinely expensive expression choiceconstraint-update and evidence-neutral decision-resolution, inherit legal evidence,
skip sync and retrieval, preserve unrelated accepted scopes, retrace only the exact revised
answer or artifact, and finalizeaccepted_by_user: true; reopen it only after explicit user authority with
reopened_by_user: true. Dependency drift marks a scope at-risk but never silently
reopens or replaces itevidence-refresh or mixed, retrieve only the evidence delta and invalidate only
explicitly affected or dependency-linked outputsprogresscovered settlement, rerun retrieve and trace exactly on the post-refresh evidence; if the result is commit-admissible but still only partially supported, finalize honestly as partially-grounded instead of starting a second refreshruntime/answers/<conversation_id>/<turn_id>.mdruntime/agent-work/ when neededCanonical Ask Contract: run the final trace on the exact answer-file version, then call hidden finalize, passing selected artifact IDs only when the turn is ambiguousstatus = execute with a repairable support_fulfillment, keep the same turn open, do one contract-aware rewrite and retrace, then finalize once moreanswer_state, support_basis, optional support_manifest_path, and linked session or trace IDsanswer_text unchanged, followed outside the artifact by exactly one
concise completion line; use user_status_code as the stable meaning and render the line
naturally in the user's language (completed-and-verified or
completed-with-evidence-boundary)governance_detail only for blockers, evidence boundaries, diagnostics, or explicit
user inspection; never append trace IDs, manifest paths, repair codes, or release-entry
notices to a successful copy-ready artifactexternal-source-verified, model-knowledge, or governed-boundary outcomes as product failures just because the KB path was not fully grounded.original_doc/ or rerender source files by reflex.grounded-answer and grounded-composition remain inner specialist workflows.© JetXu-LLM, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file in skills/canonical/ask of JetXu-LLM/DocMason.
Open the folder on GitHubat commit 362417b
Ask next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Ask this skillJetXu-LLM/DocMason | 147 | — | ~7.1k | Automated safety check: Pass | Apache-2.0 | |
| Logseq Review Workflow Evallogseq/logseq | 45k | — | ~1k | Automated safety check: Pass | AGPL-3.0 | |
| Baoyu URL To Markdownsdyckjq-lab/llm-wiki-skill | 2.5k | 2 repos | ~3.2k | Automated safety check: Pass | None | |
| Obsidian CLIAtmosphere/atmosphere | 3.8k | 13 repos | ~795 | Automated safety check: Pass | Apache-2.0 | |
| Esm Cjs Risk Scanlogseq/logseq | 45k | — | ~3.3k | Automated safety check: Pass | AGPL-3.0 | |
| Capture Conversationoutline/outline | 41k | — | ~474 | Automated safety check: Pass | Custom licence |
logseq/logseq
Compare two revisions of the Logseq logseq-review-workflow skill by running the same review prompt against isolated before and after skill snapshots, collecting both outputs, and producing a…
sdyckjq-lab/llm-wiki-skill
Fetch any URL and convert to markdown using Chrome CDP. An agent skill from sdyckjq-lab/llm-wiki-skill.
Atmosphere/atmosphere
Interact with Obsidian vaults using the Obsidian CLI to read, create, search, and manage notes, tasks, properties, and more.
logseq/logseq
Scan Logseq ClojureScript Node/Electron targets for npm module loading risks, especially ESM-only packages that may fail when loaded through js/require or shadow-cljs require-based shims.
outline/outline
Save the current conversation, a decision, or a set of notes as a document in an Outline collection; use when the user wants to keep what was discussed in their knowledge base.
Astro-Han/karpathy-llm-wiki
A skill your agent uses when building or maintaining a personal LLM-powered knowledge base.
JetXu-LLM/DocMason
Generate or refresh the local Claude adapter surface for DocMason from canonical committed sources.
JetXu-LLM/DocMason
Answer a user question through DocMason's canonical grounded workflow using retrieval, provenance tracing, render escalation, and a final answer-state check.
JetXu-LLM/DocMason
Produce evidence-backed research, planning, drafting, or composition output from the published DocMason knowledge base while preserving provenance and answer-file discipline.
JetXu-LLM/DocMason
Stage, incrementally refresh, validate, and publish the DocMason knowledge base from the local source corpus.
JetXu-LLM/DocMason
Write bilingual Phase 3 knowledge objects for staged DocMason sources from rendered evidence and extracted structure.
JetXu-LLM/DocMason
Run the hidden operator-only evaluation loop over local runtime/eval artifacts without exposing that surface in normal first-contact workflows.
Categories
Accept an ordinary user question inside a DocMason workspace, route it to the right inner workflow, and preserve conversation-native logs automatically. Ask is an agent skill from JetXu-LLM/DocMason. Accept an ordinary user question inside a DocMason workspace, route it to the right inner workflow, and preserve conversation-native logs automatically.
Ask fits situations like: knowledge Management work in your project.
Run `npx skills add JetXu-LLM/DocMason --skill ask -a claude-code`. Or copy the skill folder (skills/canonical/ask in JetXu-LLM/DocMason) into .claude/skills/ask in your project. Claude Code loads it when a task matches its description.
Run `npx skills add JetXu-LLM/DocMason --skill ask -a codex`. Or copy the skill folder (skills/canonical/ask in JetXu-LLM/DocMason) into .agents/skills/ask in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add JetXu-LLM/DocMason --skill ask -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ask, .gemini/skills/ask, .github/skills/ask and .opencode/skills/ask in your project.
Going by SKILL.md and its folder, Ask needs the command-line tools its instructions call (python). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Ask is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 7.1k tokens (SKILL.md is roughly 29k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Ask: Logseq Review Workflow Eval (logseq/logseq, 45k stars), Baoyu URL To Markdown (sdyckjq-lab/llm-wiki-skill, 2.5k stars), Obsidian CLI (Atmosphere/atmosphere, 3.8k stars) and Esm Cjs Risk Scan (logseq/logseq, 45k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
JetXu-LLM (a GitHub user) maintains it in JetXu-LLM/DocMason, which has 147 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on September 30, 2026.
Source: JetXu-LLM/DocMason on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.