Surface the most valuable next action by combining task stack, queue state, inbox pressure, health, and goals.

MITAuto-check: notesProductivity & Automation

Install Next

skills CLI
$ npx skills add agenticnotetaking/arscontexta --skill next -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install agenticnotetaking/arscontexta next --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/agenticnotetaking/arscontexta.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skill-sources/next .claude/skills/next && rm -rf skills-src

Use ~/.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/

Facts

Skill name
next
GitHub stars
3.5k
Token cost
~4.9k tokens
SKILL.md length
1,943 words
Files
2
Skills in repo
25
Repo updated
First seen
Licence
MIT

At a glance

Surface the most valuable next action by combining task stack, queue state, inbox pressure, health, and goals.

  • Works in 8 steps: Read Vocabulary → Reconcile Maintenance Queue → Collect Vault State → …
  • What should I do
  • SKILL.md covers Runtime Configuration (Step 0…, EXECUTE NOW, Edge Cases and Anti-Patterns
  • Calls jq

What it does

Next is an agent skill from agenticnotetaking/arscontexta. Surface the most valuable next action by combining task stack, queue state, inbox pressure, health, and goals. Recommends one specific action with rationale. Triggers on "/next", "what should I do", "what's next".

Its SKILL.md is about 4.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.json`).

It sits in Productivity & Automation, covering Email management. The repository describes itself as: Claude Code plugin that generates individualized knowledge systems from conversation. You describe how you think and work, have a conversation and get a complete second brain as… The licence is MIT.

When your agent uses it

  • What should I do
  • Tasks that involve Email management

Example prompts

  • “what should I do”
  • “s next”
  • “/next”

Requirements

  • Pre-approved tools (allowed-tools): Read, Grep, Glob, Bash

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Read Vocabulary
  2. Reconcile Maintenance Queue
  3. Collect Vault State
  4. Classify by Consequence Speed
  5. Generate Recommendation
  6. Deduplicate
  7. Output
  8. Log the Recommendation

What it can do on your machine

Read from SKILL.md and the folder at commit 2acfd5c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Grep
    • Glob
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • jq

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Next loads about 4.9k tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 1,943 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~4.9k

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.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Grep, Glob, Bash

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.

SKILL.md

The full file from agenticnotetaking/arscontexta at commit 2acfd5c, republished under its MIT licence (© agenticnotetaking). 1,943 words, ~4,887 tokens.

Download SKILL.mdSave it as .claude/skills/next/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
next
description
Surface the most valuable next action by combining task stack, queue state, inbox pressure, health, and goals. Recommends one specific action with rationale. Triggers on "/next", "what should I do", "what's next".
allowed-tools
Read, Grep, Glob, Bash
version
1.0
generated_from
arscontexta-v1.6
user-invocable
true
context
fork
model
sonnet

Runtime Configuration (Step 0 — before any processing)

Read these files to configure domain-specific behavior:

  1. ops/derivation-manifest.md — vocabulary mapping, domain context

    • Use vocabulary.notes for the notes folder name
    • Use vocabulary.inbox for the inbox folder name
    • Use vocabulary.note for the note type name in output
    • Use vocabulary.topic_map for MOC references
    • Use vocabulary.cmd_reduce for process/extract command
    • Use vocabulary.cmd_reflect for connection-finding command
    • Use vocabulary.cmd_reweave for backward-pass command
    • Use vocabulary.rethink for rethink command name
  2. ops/config.yaml — thresholds, processing preferences

    • self_evolution.observation_threshold (default: 10)
    • self_evolution.tension_threshold (default: 5)

If these files don't exist, use universal defaults and generic command names.

EXECUTE NOW

INVARIANT: /next recommends, it does not execute. Present one recommendation with rationale. The user decides what to do. This prevents cognitive outsourcing where the system makes all work decisions and the user becomes a rubber stamp.

Execute these steps IN ORDER:


Step 1: Read Vocabulary

Read ops/derivation-manifest.md (or fall back to ops/derivation.md) for domain vocabulary mapping. All output must use domain-native terms. If neither file exists, use universal terms (notes, inbox, topic map, etc).


Step 2: Reconcile Maintenance Queue

Before collecting state, evaluate all maintenance conditions and reconcile the queue. This ensures maintenance tasks are current before the recommendation engine runs.

Read queue file (ops/queue/queue.json or ops/queue.yaml). If schema_version < 3, migrate:

  • Add maintenance_conditions section with default thresholds
  • Add priority field to existing tasks (default: "pipeline")
  • Set schema_version: 3

For each condition in maintenance_conditions:

  1. Evaluate the condition:
ConditionEvaluation Method
orphan_notesFor each note in {vocabulary.notes}/, count incoming [[links]]. Zero = orphan.
dangling_linksExtract all [[links]], verify targets exist as files. Missing = dangling.
inbox_pressureCount *.md in {vocabulary.inbox}/.
observation_accumulationCount status: pending in ops/observations/.
tension_accumulationCount status: pending or open in ops/tensions/.
pipeline_stalledQueue tasks with status: pending unchanged across sessions.
unprocessed_sessionsCount files in ops/sessions/ without mined: true.
moc_oversizeFor each topic map, count linked notes.
stale_notesNotes not modified in 30+ days with < 2 links.
low_link_densityAverage link count across all notes.
methodology_driftCompare config.yaml modification time vs newest ops/methodology/ note modification time. If config is newer, methodology may be stale.
  1. If condition exceeds threshold AND no pending task with this condition_key exists:

Create maintenance task:

bash
TIMESTAMP=$(date -u +"%Y-%m-%dT%H:%M:%SZ")
MAINT_MAX=$(jq '[.tasks[] | select(.id | startswith("maint-")) | .id | ltrimstr("maint-") | tonumber] | max // 0' ops/queue/queue.json)
NEXT_MAINT=$((MAINT_MAX + 1))

jq --arg id "maint-$(printf '%03d' $NEXT_MAINT)" \
   --arg priority "{priority}" \
   --arg key "{condition_key}" \
   --arg target "{description}" \
   --arg action "{recommended command}" \
   --arg ts "$TIMESTAMP" \
   '.tasks += [{"id": $id, "type": "maintenance", "priority": $priority, "status": "pending", "condition_key": $key, "target": $target, "action": $action, "auto_generated": true, "created": $ts}]' \
   ops/queue/queue.json > tmp.json && mv tmp.json ops/queue/queue.json
  1. If condition is satisfied AND a pending task with this condition_key exists:

Auto-close it:

bash
TIMESTAMP=$(date -u +"%Y-%m-%dT%H:%M:%SZ")
jq --arg key "{condition_key}" --arg ts "$TIMESTAMP" \
   '(.tasks[] | select(.condition_key == $key and .status == "pending")).status = "done" |
    (.tasks[] | select(.condition_key == $key and .status == "pending")).completed = $ts' \
    ops/queue/queue.json > tmp.json && mv tmp.json ops/queue/queue.json
  1. If condition fires AND a pending task already exists:

Update the target description (specifics may have changed):

bash
jq --arg key "{condition_key}" --arg target "{new description}" \
   '(.tasks[] | select(.condition_key == $key and .status == "pending")).target = $target' \
   ops/queue/queue.json > tmp.json && mv tmp.json ops/queue/queue.json

Step 3: Collect Vault State

Gather all signals. Run independent checks in parallel where possible. Record each signal even if the check returns zero — absence of signal is itself informative.

SignalHow to CheckWhat to Record
Task stackRead ops/tasks.md — current priorities and open itemsTop items, open count, any deadlines
Queue stateRead ops/queue.yaml or ops/queue/queue.json — pending pipeline tasksTotal pending, by phase (create, reflect, reweave, verify), blocked phases
Inbox pressureCount *.md files in {vocabulary.inbox}/, find oldest by mtimeCount per subdirectory, age of oldest item in days
Note countCount *.md in {vocabulary.notes}/Total notes for context
Orphan notesFor each note, grep for [[filename]] across all files — zero hits = orphanCount, first 5 names
Dangling linksExtract all [[links]] from notes/, verify each target file existsCount, first 5 targets
Stale notesNotes not modified recently AND with low link density (< 2 links)Count
GoalsRead self/goals.md or ops/goals.md — current priorities, active threadsPriority list, active research directions
ObservationsCount files with status: pending in ops/observations/Count
TensionsCount files with status: pending or status: open in ops/tensions/Count
MethodologyCheck ops/methodology/ for recent captures (files modified in last 7 days)Count of recent, total count
HealthRead most recent report in ops/health/ — note timestamp and issuesLast run date, issue count, any critical issues
SessionsCheck ops/sessions/ for files without mined: true in frontmatterCount of unmined sessions
Recent /nextRead ops/next-log.md (if exists) — last 3 recommendationsPrevious suggestions to avoid repetition

Adaptation rules:

  • Directory names adapt to domain vocabulary (e.g., {vocabulary.inbox} instead of hardcoded "inbox")
  • Skip checks silently for directories that do not exist — do not report "ops/sessions/ not found"
  • A missing directory means that feature is not active, which is valid state

Signal collection commands:

bash
# Inbox pressure (adapt path to vocabulary)
INBOX_COUNT=$(find {vocabulary.inbox}/ -name "*.md" -maxdepth 2 2>/dev/null | wc -l | tr -d ' ')
OLDEST_INBOX=$(find {vocabulary.inbox}/ -name "*.md" -maxdepth 2 -exec stat -f "%m %N" {} \; 2>/dev/null | sort -n | head -1)

# Note count
NOTE_COUNT=$(ls -1 {vocabulary.notes}/*.md 2>/dev/null | wc -l | tr -d ' ')

# Pending observations
OBS_COUNT=$(grep -rl '^status: pending' ops/observations/ 2>/dev/null | wc -l | tr -d ' ')

# Pending tensions
TENSION_COUNT=$(grep -rl '^status: pending\|^status: open' ops/tensions/ 2>/dev/null | wc -l | tr -d ' ')

# Unmined sessions
SESSION_COUNT=$(grep -rL '^mined: true' ops/sessions/*.md 2>/dev/null | wc -l | tr -d ' ')

Step 4: Classify by Consequence Speed

Evaluate every signal against consequence speed — how fast does inaction degrade the system?

SpeedSignalsThresholdWhy This Priority
SessionInbox > 5 items, orphan notes (any), dangling links (any), 10+ pending observations, 5+ pending tensions, unprocessed sessions > 3Immediate — these degrade work quality right nowOrphans are invisible to traversal. Dangling links confuse navigation. Inbox pressure means lost ideas. Observation/tension thresholds mean the system is accumulating unprocessed friction.
Multi-sessionPipeline queue backlog > 10, research gaps identified in goals, stale notes > 10, inbox items aging > 7 days, methodology captures > 5 in same categorySoon — these compound over daysUnfinished pipeline batches block downstream connections. Stale notes represent decaying knowledge. Aging inbox means capture is outpacing processing.
SlowHealth check not run in 14+ days, {DOMAIN:topic map} oversized (>40 notes), link density below 2.0 average, low note count relative to timeBackground — annoying but not blockingThese are maintenance tasks. Important for long-term health but not urgent.

Threshold rule: 10+ pending observations OR 5+ pending tensions is ALWAYS session-priority. Recommend {DOMAIN:rethink} in this case.

Signal interaction rules:

  • Task stack items ALWAYS override automated recommendations (user-set priorities beat system-detected urgency)
  • Multiple session-priority signals: pick the one with highest impact (most items affected)
  • If inbox pressure AND queue backlog: recommend reducing inbox first (pipeline needs input before it can process)

Step 5: Generate Recommendation

Select the SINGLE most valuable action. The recommendation must be specific enough to execute immediately — a concrete command invocation, not a vague suggestion.

Priority cascade:

1. Task Stack First

If ops/tasks.md has open items, recommend from the task stack. User-set priorities override all automated recommendations because:

  • The user has context the system does not
  • Ignoring explicit priorities erodes trust
  • Task stack items represent deliberate decisions, not automated detection

Format: Recommend the specific task with context about why it was in the stack.

1.5. Session-Priority Maintenance Tasks

Read queue for maintenance tasks with priority: "session" and status: "pending". These represent vault health conditions that degrade THIS session.

Pick the highest-impact one:

  • orphan_notes: "{N} notes invisible to traversal"
  • dangling_links: "{N} broken links confusing navigation"
  • inbox_pressure: "{N} items aging in inbox"

Recommend the action field from the queue entry.

2. Session-Priority Signals

If no task stack items, pick the highest-impact session-priority signal:

SignalRecommendationRationale Template
Dangling links / orphans/health or specific fix command"You have [N] orphan notes invisible to traversal. Connecting them increases graph density and retrieval quality."
10+ observations or 5+ tensions/{DOMAIN:rethink}"[N] pending observations have accumulated. Pattern detection requires processing this backlog to evolve the system."
Inbox > 5 items/{DOMAIN:reduce} [specific file]"Your inbox has [N] items (oldest: [age]). [File X] has the highest connection potential based on [reason]."
Unprocessed sessions > 3/remember --mine-sessions"[N] sessions have uncaptured friction patterns. Mining them prevents methodology regressions."

When recommending inbox processing: Choose the specific inbox item that aligns best with current goals or has the most connection potential to existing notes. Recommend a concrete file, not "process some inbox."

Show full SKILL.md (770 more words)Show less
3. Multi-Session Signals

If no session-priority items:

SignalRecommendationRationale Template
Queue backlog > 10/ralph [N]"[N] pipeline tasks are pending. Your newest {DOMAIN:notes} lack connections, which means they can't participate in synthesis."
Stale notes > 10/{DOMAIN:reweave} [specific note]"[N] notes haven't been touched since [date]. [Note X] has the most connections and would benefit most from updating."
Research gaps/{DOMAIN:reduce} [file aligned with goals]"Your goals mention [topic] but your graph has few notes there. [Inbox item] addresses this gap."
Methodology convergence/{DOMAIN:rethink}"[N] methodology captures in the [category] area suggest a pattern worth elevating."

When recommending reweaving: Choose the most-connected stale note (highest link density + oldest modification). Reweaving high-connectivity notes has the highest ripple effect.

4. Slow Signals

If nothing pressing:

SignalRecommendationRationale Template
No recent health check/health"Last health check was [date]. Running one now catches structural issues before they compound."
Topic map oversizedRestructuring suggestion"[Topic map X] has [N] notes. Splitting into sub-topic-maps improves navigation and reduces cognitive load."
Low link density/{DOMAIN:reweave} on lowest-density note"Your graph has an average link density of [N]. Reweaving sparse notes increases traversal paths."
5. Everything Clean

If all signals are healthy:

next

  All signals healthy.
  Inbox: 0 | Queue: 0 pending | Orphans: 0 | Dangling: 0

  No urgent work detected.

  Suggested: Explore a new direction from goals.md
  or reweave older {DOMAIN:notes} to deepen the graph.

Rationale is always mandatory. Every recommendation must explain:

  1. WHY this action over alternatives
  2. What DEGRADES if this action is deferred
  3. How it connects to goals (if applicable)

Step 6: Deduplicate

Read ops/next-log.md (if it exists). Check the last 3 entries.

Deduplication rules:

  • If the same recommendation appeared in the last 2 entries, select the next-best action instead
  • This prevents the system from getting stuck recommending the same thing repeatedly when the user has chosen not to act on it
  • If the same recommendation is genuinely the highest priority (e.g., inbox pressure keeps growing), add an explicit note: "This was recommended previously. The signal has grown stronger since then ([before] → [now])."

Step 7: Output
next

  State:
    Inbox: [count] items (oldest: [age])
    Queue: [count] pending ([phase breakdown])
    Orphans: [count] | Dangling: [count]
    Observations: [count] | Tensions: [count]
    [any other decision-relevant signals]

  Recommended: [specific command/action]

  Rationale: [2-3 sentences — why this action,
  how it connects to goals, what degrades if deferred]

  After that: [second priority, if relevant]
  [optional: alignment with goals.md priority]

Command specificity is mandatory. Recommendations must be concrete invocations:

GoodBad
/{DOMAIN:reduce} inbox/article-on-spaced-repetition.md"process some inbox items"
/ralph 5"work on the queue"
/{DOMAIN:rethink}"review your observations"
/{DOMAIN:reweave} [[note title here]]"update some old notes"

State display rules:

  • Show only 2-4 decision-relevant signals — not all 14 checks
  • Zero-count signals that are healthy can be omitted (don't show "Orphans: 0" unless contrasting with a problem)
  • Non-zero signals at session or multi-session priority should always be shown

Step 8: Log the Recommendation

Append to ops/next-log.md (create if missing):

markdown
## YYYY-MM-DD HH:MM

**State:** Inbox: [N] | Notes: [N] | Orphans: [N] | Dangling: [N] | Stale: [N] | Obs: [N] | Tensions: [N] | Queue: [N]
**Recommended:** [action]
**Rationale:** [one sentence]
**Priority:** session | multi-session | slow

Why log? The log serves three purposes:

  1. Deduplication — prevents recommending the same action repeatedly
  2. Evolution tracking — shows what signals have been persistent vs transient
  3. /rethink evidence — persistent recommendations that go unacted-on may reveal misalignment between what the system detects and what the user values

Edge Cases

Empty Vault (0-5 notes)

Recommend capturing or reducing content. Maintenance is premature with < 5 notes — the graph does not have enough nodes for meaningful analysis.

next

  State:
    Notes: [N] — early stage vault

  Recommended: Capture or /{DOMAIN:reduce} content
  Rationale: Your graph has [N] notes. At this stage, adding
  content matters more than maintaining structure. Health checks,
  reweaving, and rethink become valuable after ~10 notes.
Everything Clean

Say so explicitly. Recommend exploratory work aligned with goals, or reflective work on older notes:

  No urgent work detected. Consider:
  - Exploring a research direction from goals.md
  - Reweaving older {DOMAIN:notes} to deepen connections
  - Reviewing and updating goals.md itself
No Goals File

Recommend creating self/goals.md or ops/goals.md first. Without priorities, recommendations lack grounding and the system cannot distinguish between "important to the user" and "detected by automation."

  Recommended: Create ops/goals.md
  Rationale: Without goals, /next can only recommend based on
  automated detection. Goals let the system align recommendations
  with what actually matters to you.
No ops/derivation-manifest.md

Use universal vocabulary. Do not fail — /next should always produce a recommendation regardless of configuration state.

Queue Not Active

Skip queue checks silently. Not all vaults use the pipeline architecture — some rely on manual processing. Do not report "queue not found" as an issue.

Multiple Session-Priority Signals

When several signals are at session priority simultaneously, pick the one that unblocks the most downstream work:

  • Dangling links block graph traversal → fix first
  • Observation threshold → rethink prevents methodology drift
  • Inbox pressure → processing prevents idea loss

If genuinely equal priority, pick the one the user has not been recommended recently (check next-log.md).

Stale /next Log

If ops/next-log.md has not been updated in 14+ days, the user may not be running /next regularly. Note this but do not make it a recommendation — /next is optional, not mandatory.


Anti-Patterns

These are patterns that /next must avoid:

Anti-PatternWhy It Is WrongWhat to Do Instead
Recommending everythingOverwhelms the user, defeats the purpose of "single most valuable action"Pick ONE. Mention a second only as "after that"
Vague recommendations"Process inbox" gives no actionable starting pointName the specific file, note, or command
Ignoring task stackUser-set priorities exist for a reasonAlways check ops/tasks.md first
Repeating the same recIf the user did not act on it, recommending it again is naggingDeduplicate via next-log.md
Recommending maintenance too earlyA 5-note vault does not need health checksScale recommendations to vault maturity
Cognitive outsourcingMaking all decisions for the userRecommend and explain — never execute

© agenticnotetaking, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skill-sources/next of agenticnotetaking/arscontexta.

  • SKILL.md
  • skill.json

Open the folder on GitHubat commit 2acfd5c

Compare with similar skills

Next 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.

Next compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Next this skillagenticnotetaking/arscontexta3.5k—~4.9kAutomated safety check: NotesMIT
Process Inboxtelegramdesktop/tdesktop33k2 repos~4.5kAutomated safety check: PassGPL-3.0
Continuetelegramdesktop/tdesktop33k2 repos~9.4kAutomated safety check: PassGPL-3.0
Garden Inboxpaperclipai/paperclip98k—~1.1kAutomated safety check: PassMIT
Career-Ops Gmail Lead Plugincareer-ops-hq/career-ops74k—~233Automated safety check: NotesMIT
Skill CompassEvol-ai/SkillCompass2161 repos~3.1kAutomated safety check: PassMIT

Similar skills

  • Process Inbox

    telegramdesktop/tdesktop

    Process the local ignored ai-tdesktop inbox into durable, independently testable Telegram Desktop task records while task execution worktrees remain active.

    33k GitHub starsUsed in 2 repos~4.5k tokens
    Productivity & AutomationAuto-check passed
  • Continue

    telegramdesktop/tdesktop

    Continue autonomous Telegram Desktop development from the shared ai-tdesktop repository.

    33k GitHub starsUsed in 2 repos~9.4k tokens
    Productivity & AutomationAuto-check passed
  • Garden Inbox

    paperclipai/paperclip

    Scan a Paperclip user's Mine inbox, classify reversible archive candidates, request checkbox confirmation, and archive only accepted selections.

    98k GitHub stars~1.1k tokensUpdated today
    Productivity & AutomationAuto-check passed
  • Career-Ops Gmail Lead Plugin

    career-ops-hq/career-ops

    Pulls job leads from a Gmail label into the career-ops pipeline, extracting job URLs from DMARC-passing emails and de-duplicating against existing leads.

    74k GitHub stars~233 tokensUpdated today
    Productivity & AutomationAuto-check: notes
  • Skill Compass

    Evol-ai/SkillCompass

    Evaluate skill quality, find the weakest dimension, and apply directed improvements.

    216 GitHub starsUsed in 1 repo~3.1k tokens
    Productivity & AutomationAuto-check passed
  • Atomicmail

    Atomic-Mail/atomic-mail-agentic

    Read and write email through the Atomic Mail from an AI agent.

    266 GitHub starsUsed in 1 repo~2k tokens
    Productivity & AutomationAuto-check passed

More from agenticnotetaking/arscontexta

All 25 skills in this repo
  • Graph

    agenticnotetaking/arscontexta

    Interactive knowledge graph analysis. An agent skill from agenticnotetaking/arscontexta.

    3.5k GitHub starsUsed in 1 repo~4.9k tokens
    Auto-check: notes
  • Learn

    agenticnotetaking/arscontexta

    Research a topic and grow your knowledge graph. An agent skill from agenticnotetaking/arscontexta.

    3.5k GitHub starsUsed in 1 repo~1.9k tokens
    Auto-check: notes
  • Recommend

    agenticnotetaking/arscontexta

    Get research-backed architecture advice for your knowledge system.

    3.5k GitHub starsUsed in 1 repo~5.1k tokens
    Auto-check passed
  • Stats

    agenticnotetaking/arscontexta

    Show vault statistics and knowledge graph metrics. An agent skill from agenticnotetaking/arscontexta.

    3.5k GitHub starsUsed in 1 repo~3.1k tokens
    Auto-check: notes
  • Help

    agenticnotetaking/arscontexta

    Contextual guidance and command discovery. An agent skill from agenticnotetaking/arscontexta.

    3.5k GitHub stars~3.3k tokensUpdated 7 mo ago
    Auto-check: notes
  • Pipeline

    agenticnotetaking/arscontexta

    End-to-end source processing -- seed, reduce, process all claims through reflect/reweave/verify, archive.

    3.5k GitHub stars~2.4k tokensUpdated 7 mo ago
    Auto-check: notes

Questions about Next

What does Next do?

Surface the most valuable next action by combining task stack, queue state, inbox pressure, health, and goals. Next is an agent skill from agenticnotetaking/arscontexta. Surface the most valuable next action by combining task stack, queue state, inbox pressure, health, and goals.

When should I use Next?

Next fits situations like: what should I do; tasks that involve Email management.

How do I install Next in Claude Code?

Run `npx skills add agenticnotetaking/arscontexta --skill next -a claude-code`. Or copy the skill folder (skill-sources/next in agenticnotetaking/arscontexta) into .claude/skills/next in your project. Claude Code loads it when a task matches its description.

How do I install Next in Codex?

Run `npx skills add agenticnotetaking/arscontexta --skill next -a codex`. Or copy the skill folder (skill-sources/next in agenticnotetaking/arscontexta) into .agents/skills/next in your project. Codex loads it when a task matches its description.

Can I use Next in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agenticnotetaking/arscontexta --skill next -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/next, .gemini/skills/next, .github/skills/next and .opencode/skills/next in your project.

What does Next need to run?

Going by SKILL.md and its folder, Next needs the command-line tools its instructions call (jq). Its frontmatter pre-approves these tools: Read, Grep, Glob, Bash.

Does Next access the network?

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.

Is Next safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Next use?

Next is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Next use?

About 4.9k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Next?

Skills that share tags, products or a category with Next: Process Inbox (telegramdesktop/tdesktop, 33k stars), Continue (telegramdesktop/tdesktop, 33k stars), Garden Inbox (paperclipai/paperclip, 98k stars) and Career-Ops Gmail Lead Plugin (career-ops-hq/career-ops, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Next?

agenticnotetaking (a GitHub organization) maintains it in agenticnotetaking/arscontexta, which has 3,492 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on February 24, 2026.

Source: agenticnotetaking/arscontexta on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.