Combined verification — recite (description quality via cold-read prediction) + validate (schema compliance) + review (health checks).

MITAuto-check passedTesting & QA

Install Verify

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

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

GitHub CLI
$ gh skill install agenticnotetaking/arscontexta verify --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/verify .claude/skills/verify && 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
verify
GitHub stars
3.5k
Token cost
~5.7k tokens
SKILL.md length
2,696 words
Files
2
Skills in repo
25
Repo updated
First seen
Licence
MIT

At a glance

Combined verification — recite (description quality via cold-read prediction) + validate (schema compliance) + review (health checks).

  • Works in 7 steps: INDEX FRESHNESS CHECK → RECITE (cold-read prediction test) → VALIDATE (schema check) → …
  • Verify note quality
  • SKILL.md covers Runtime Configuration (Step 0…, EXECUTE NOW, Anti-Shortcut Warning and philosophy, plus 12 more sections
  • Calls jq

What it does

Verify is an agent skill from agenticnotetaking/arscontexta. Combined verification — recite (description quality via cold-read prediction) + validate (schema compliance) + review (health checks). Use as a quality gate after creating notes or as periodic maintenance. Triggers on "/verify", "/verify [note]", "verify note quality", "check note health".

Its SKILL.md is about 5.7k 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 Testing & QA, covering Quality gates and Regulatory compliance. 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

  • Verify note quality
  • Check note health

Example prompts

  • “/verify”
  • “/verify [note]”
  • “verify note quality”
  • “/verify”

Requirements

  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, mcp__qmd__vector_search

Workflow steps

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

  1. INDEX FRESHNESS CHECK
  2. RECITE (cold-read prediction test)
  3. VALIDATE (schema check)
  4. REVIEW (per-note health checks)
  5. APPLY FIXES
  6. Compile Results
  7. Update task file and capture observations

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
    • Write
    • Edit
    • Grep
    • Glob
    • mcp__qmd__vector_search

    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

Verify loads about 5.7k tokens when it runs. Until then it costs about 74 tokens; SKILL.md has 2,696 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~74
When it runs · the whole SKILL.md, loaded when a task matches
~5.7k

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 passed

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.

SKILL.md

The full file from agenticnotetaking/arscontexta at commit 2acfd5c, republished under its MIT licence (© agenticnotetaking). 2,696 words, ~5,722 tokens.

Download SKILL.mdSave it as .claude/skills/verify/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
verify
description
Combined verification — recite (description quality via cold-read prediction) + validate (schema compliance) + review (health checks). Use as a quality gate after creating notes or as periodic maintenance. Triggers on "/verify", "/verify [note]", "verify note quality", "check note health".
allowed-tools
Read, Write, Edit, Grep, Glob, mcp__qmd__vector_search
user-invocable
true
context
fork

Runtime Configuration (Step 0 — before any processing)

Read these files to configure domain-specific behavior:

  1. ops/derivation-manifest.md — vocabulary mapping, platform hints

    • Use vocabulary.notes for the notes folder name
    • Use vocabulary.note / vocabulary.note_plural for note type references
    • Use vocabulary.verify for the process verb in output
    • Use vocabulary.topic_map for MOC references
    • Use vocabulary.templates for the templates folder path
    • Use vocabulary.cmd_reflect for redirect when missing connections found
  2. ops/config.yaml — processing depth, verification settings

    • processing.depth: deep | standard | quick
    • processing.verification.description_test: true | false
    • processing.verification.schema_check: true | false
    • processing.verification.link_check: true | false

If these files don't exist, use universal defaults.

Processing depth adaptation:

DepthVerification Behavior
deepFull verification: cold-read prediction, complete schema check, exhaustive link verification, MOC coverage, orphan risk analysis, content staleness detection, bundling analysis
standardBalanced: cold-read prediction, schema check, link verification, MOC coverage
quickBasic: schema check, link verification only. Skip cold-read prediction and health analysis

EXECUTE NOW

Target: $ARGUMENTS

Parse immediately:

  • If target contains a note name: verify that specific note
  • If target contains --handoff: output RALPH HANDOFF block at end
  • If target is "all" or "recent": verify recently created/modified notes
  • If target is empty: ask which note to verify

Anti-Shortcut Warning

Before marking verification as passed, you MUST complete ALL four categories:

  1. COMPLETE description quality test — cold-read the title + description, predict what the note contains, compare against actual content. A description that merely restates the title FAILS.

  2. COMPLETE schema validation — check ALL required fields from the template schema, verify ALL enum values are valid, confirm ALL constraints are met. A single missing required field FAILS.

  3. COMPLETE link verification — confirm ALL wiki links in the note resolve to existing files. A single dangling link FAILS.

  4. COMPLETE {DOMAIN:topic map} integration — verify the note appears in at least one {DOMAIN:topic map}'s Core Ideas section with a context phrase. A note with no {DOMAIN:topic map} mention FAILS.

Do NOT declare success after checking only one or two categories. ALL FOUR must pass.

Execute these steps IN ORDER:

Step 0: INDEX FRESHNESS CHECK

Before any retrieval tests, verify the semantic search index is current:

  1. Try mcp__qmd__vector_search with a simple test query to confirm MCP availability
  2. If MCP is unavailable (tool fails or returns error), try qmd CLI (qmd status) to confirm local CLI availability
  3. If either MCP or qmd CLI is available, proceed to Step 1
  4. If neither MCP nor qmd CLI is available: note "retrieval test will be deferred" and proceed — do NOT let index issues block verification

The index freshness check prevents false retrieval failures on recently created notes. If the index is stale, retrieval test results should be interpreted with that context.

Step 1: RECITE (cold-read prediction test)

CRITICAL: Do NOT read the full note yet. Only read frontmatter.

This step tests whether the title + description alone enable an agent to predict the note's content. The cold-read constraint is the entire point — reading the note first contaminates the prediction.

1. Read ONLY title + description

Use Read with a line limit to get just the first few lines of frontmatter. Extract:

  • Title (the filename without .md)
  • Description (the description field)

Do NOT scroll past the frontmatter closing ---.

2. Form prediction

Before reading further, write out what you expect:

  • Core argument: what claim does this note make?
  • Mechanism: what reasoning or evidence does it use?
  • Scope: what boundaries does the argument have?
  • Likely connections: what other notes would it reference?

Write this prediction explicitly in your output. It must be specific enough to be wrong.

3. Read full note content

NOW read the complete note. Compare against your prediction.

4. Score prediction accuracy (1-5)

ScoreMeaningThreshold
5Perfect — description fully captured the argumentPass
4Strong — minor details missed, core predictedPass
3Adequate — general area right, missed key aspectsPass (minimum)
2Weak — significant mismatch between prediction and contentFAIL
1Failed — note argued something different than expectedFAIL

Passing threshold: 3 or above.

5. Run semantic retrieval test

Test whether the description enables semantic retrieval:

  • Tier 1 (preferred): mcp__qmd__vector_search with query = "[the note's description text]", collection = "{vocabulary.notes_collection}", limit = 10
  • Tier 2 (CLI fallback): qmd vsearch "[the note's description text]" --collection {vocabulary.notes_collection} -n 10
  • Tier 3: if both MCP and qmd CLI are unavailable, report "retrieval test deferred (semantic search unavailable)" — do NOT skip silently

Check where the note appears in results:

  • Top 3: description works well for semantic retrieval
  • Position 4-10: adequate but could improve
  • Not in top 10: flag — description may not convey the note's meaning

Why vector_search specifically: Agents find notes via semantic search during reflect and reweave. Testing with keyword search tests the wrong retrieval method. Full hybrid search with LLM reranking compensates for weak descriptions — too lenient. vector_search tests real semantic findability without hiding bad descriptions behind reranking.

6. Draft improved description if needed

If prediction score < 3:

  • Diagnose the failure: too vague? missing mechanism? wrong emphasis? restates title?
  • Draft an improved description that would score higher
  • If you have Edit tool access: apply the improvement

7. Combined scoring

Prediction ScoreRetrieval RankSuggested Action
4-5top 3Description works — no changes needed
3-4top 5Adequate — minor improvements possible
3+6-10Investigate — passes prediction but weak retrieval
anynot in top 10Flag for review — description may not enable retrieval
< 3anyFAIL — description needs rewriting
Step 2: VALIDATE (schema check)

Read the template that applies to this note type. Determine the template by checking:

  • Note location (e.g., {DOMAIN:notes}/ uses the standard note template)
  • Type field in frontmatter (if present, may indicate a specialized template)

If the vault has templates with _schema blocks, read the _schema from the relevant template for authoritative field requirements. If no _schema exists, use the checks below as defaults.

Required fields (FAIL if missing):

FieldRequirementSeverity
descriptionMust exist and be non-emptyFAIL
Topics footer or topics fieldMust reference at least one {DOMAIN:topic map}FAIL

Description constraints (WARN if violated):

ConstraintCheckSeverity
LengthShould be ~50-200 charactersWARN
FormatSingle sentence, no trailing periodWARN
ContentMUST add NEW information beyond titleWARN
Semantic valueShould capture mechanism, not just topicWARN

How to check "adds new info": Read the title, read the description. If the description says the same thing in different words, it fails this check. A good description adds: mechanism (how/why), scope (boundaries), implication (what follows), or context (where it applies).

YAML validity (FAIL if broken):

CheckRuleSeverity
Frontmatter delimitersMust start with --- and close with ---FAIL
Valid YAMLMust parse without errorsFAIL
No unknown fieldsFields not in the templateWARN

Domain-specific field enums (WARN if invalid):

If the note has fields with enumerated values (type, category, status, etc.), check them against the template's _schema.enums block. Each invalid enum value produces a WARN.

Relevant notes format (WARN if incorrect):

ConstraintCheckSeverity
FormatArray with context: ["[[note]] -- relationship"]WARN
Relationship typeShould use standard types: extends, foundation, contradicts, enables, exampleINFO
Links existEach referenced note must exist as a fileWARN

Topics format (FAIL if invalid):

ConstraintCheckSeverity
FormatArray of wiki links: ["[[topic]]"]FAIL
Links existEach {DOMAIN:topic map} must exist as a fileWARN

Composability (WARN if fails):

CheckRuleSeverity
Title testCan you complete "This note argues that [title]"?WARN
SpecificityIs the claim specific enough to disagree with?WARN
Step 3: REVIEW (per-note health checks)

Run these 5 checks on the note:

1. YAML frontmatter integrity

  • File starts with ---, has closing ---
  • YAML parses without errors
  • No duplicate keys

2. Description quality (independent of recite)

  • Description is present and non-empty
  • Description adds information beyond the title
  • Description is not just the title rephrased

3. {DOMAIN:topic map} connection

  • Note appears in at least one {DOMAIN:topic map}'s Core Ideas section
  • How to check: grep for [[note title]] in files that serve as {DOMAIN:topic map}s
  • The note's Topics footer references a valid {DOMAIN:topic map}
  • A note with no {DOMAIN:topic map} mention is orphaned — FAIL

4. Wiki link density

  • Count outgoing wiki links in the note body (not just frontmatter)
  • Expected minimum: 2 outgoing links
  • If < 2: flag as sparse — the note is not participating in the graph
  • Sparse notes should be routed to /reflect for connection finding

5. Link resolution

  • Scan ALL wiki links in the note — body, frontmatter relevant_notes, and Topics
  • For each [[link]], confirm a matching file exists in the vault
  • Exclude wiki links inside backtick-wrapped code blocks (single backtick or triple backtick) — these are syntax examples, not real links
  • A single dangling link = FAIL with the specific broken link identified

Deep-only checks (when processing.depth = deep):

6. Orphan risk assessment

  • Count incoming links: grep for [[note title]] across all .md files
  • If 0 incoming links: AT RISK — note exists but nothing references it
  • If 1 incoming link: LOW RISK — single point of connection
  • If 2+ incoming links: OK

7. Content staleness detection

  • Read the note's content and assess whether claims still seem current
  • Check if referenced concepts/tools/approaches have changed
  • Flag anything that reads as potentially outdated

8. Bundling analysis

  • Does the note make multiple distinct claims that could be separate notes?
  • Check: could you link to part of this note without dragging unrelated context?
  • If yes: flag for potential splitting
Step 4: APPLY FIXES

If you have Edit tool access, apply fixes for clear-cut issues:

Auto-fix (safe to apply):

  • Improved description if recite score < 3
  • Missing --- frontmatter delimiters
  • Trailing period on description
  • Missing Topics footer (if obvious which {DOMAIN:topic map} applies)

Do NOT auto-fix (requires judgment):

  • Bundled notes (splitting requires understanding the claims)
  • Content staleness (needs human review of factual accuracy)
  • Missing connections (use /reflect instead — connection finding is its own phase)
  • Ambiguous {DOMAIN:topic map} assignment (when note could fit multiple)
Step 5: Compile Results

Combine all checks into a unified report:

=== VERIFY: [note title] ===

RECITE:
  Prediction score: N/5
  Retrieval rank: #N (or "not in top 10" or "deferred")
  Description: [pass/improved/needs work]

VALIDATE:
  Required fields: [PASS/FAIL — detail]
  Description constraints: [PASS/WARN — detail]
  Topics format: [PASS/FAIL — detail]
  Optional fields: [PASS/WARN/N/A]
  Relevant notes: [PASS/WARN/N/A]
  Composability: [PASS/WARN]

REVIEW:
  Frontmatter: [PASS/FAIL]
  Description quality: [PASS/WARN]
  {DOMAIN:topic map} connection: [PASS/FAIL — which {DOMAIN:topic map}]
  Wiki links: N outgoing [PASS/WARN if < 2]
  Link resolution: [PASS/FAIL — broken links listed]

Overall: [PASS / WARN (N warnings) / FAIL (N failures)]

Actions Taken:
- [List of fixes applied, or "none"]

Recommended Actions:
- [List of suggested next steps, or "none"]
===
Show full SKILL.md (1,088 more words)Show less
Step 6: Update task file and capture observations
  • If a task file is in context (pipeline execution): update the ## Verify section with results
  • Reflect on the process: friction? surprises? methodology insights? process gaps?
  • If any observations worth capturing: create atomic note in the observations directory per the observation capture pattern
  • If --handoff in target: output RALPH HANDOFF block (see below)

START NOW. The reference material below explains philosophy and methodology — use to guide reasoning, not as output to repeat.


Verify

Combined verification: recite (description quality) + validate (schema compliance) + review (health checks). Three lightweight checks in one context window.

philosophy

verification is one concern, not three.

recite tests whether the description enables retrieval. validate checks schema compliance. review checks graph health. all three operate on the same note, read the same frontmatter, and together answer one question: is this {DOMAIN:note} ready?

running them separately meant three context windows, three subagent spawns, three rounds of reading the same file. the checks are lightweight enough (combined context ~15-25% of window) that they fit comfortably in one session while staying in the smart zone.

"the unit of verification is the {DOMAIN:note}, not the check type."

execution order matters

Recite MUST run first. The cold-read prediction test requires forming an honest prediction from title + description BEFORE reading the full note. If validate or review ran first (both read the full note), the prediction would be contaminated. Recite's constraint: predict first, read second.

Index freshness runs before everything. The retrieval test in recite depends on semantic search having current data. Without a freshness check, recently created notes produce false retrieval failures that obscure actual description quality issues.

After recite reads the full note, validate and review can run in any order since they both need the full content.

recite: description quality

the testing effect applied to vault quality. read only title + description, predict what the note argues, then check. if your prediction fails, the description fails.

why this matters: descriptions are the API of the vault. agents decide whether to load a note based on title + description. a misleading description causes two failure modes:

  • false positive: agent reads the note expecting X, wastes context on Y
  • false negative: agent skips the note because description doesn't signal relevance

both degrade the vault's value as a knowledge tool.

retrieval test rationale: agents find notes via semantic search during reflect and reweave. testing with BM25 keyword matching tests the wrong retrieval method. full hybrid search with LLM reranking compensates for weak descriptions — too lenient. vector_search tests real semantic findability without hiding bad descriptions.

validate: schema compliance

checks against the relevant template schema:

CheckRequirementSeverity
descriptionMust exist, non-emptyFAIL
topicsMust exist, array of wiki linksFAIL
description length< 200 charsWARN
description contentAdds info beyond titleWARN
description formatNo trailing periodWARN
domain enum fieldsValid values per template _schema.enumsWARN
relevant_notes formatArray with context phrasesWARN
YAML integrityWell-formed, --- delimitersFAIL
ComposabilityTitle passes "This note argues that [title]" testWARN

FAIL means fix needed. WARN is informational but worth addressing.

template discovery: The skill reads the template for the note type to get its _schema block. If no template exists or no _schema block is found, fall back to the default checks above.

review: per-note health

5 focused checks per note (not a full vault-wide audit):

  1. YAML frontmatter — well-formed, has --- delimiters, valid parsing
  2. Description quality — present, adds info beyond title, not a restatement
  3. {DOMAIN:topic map} connection — appears in at least one {DOMAIN:topic map}
  4. Wiki link count — >= 2 outgoing links (graph participation threshold)
  5. Link resolution — all wiki links point to existing files (full body scan, excluding backtick-wrapped examples)

plus 3 deep-only checks for comprehensive audits: 6. Orphan risk — incoming link count (is anything pointing here?) 7. Content staleness — does the content still seem accurate? 8. Bundling — does the note make multiple distinct claims?

common failure patterns

PatternSymptomFix
Title restated as descriptionRecite score 1-2, prediction trivially correct but content is richerRewrite description to add mechanism/scope
Missing {DOMAIN:topic map}Review fails MOC checkAdd to appropriate {DOMAIN:topic map} or create Topics footer
Dangling linksReview fails link resolutionRemove link, create the target note, or fix the spelling
Sparse note< 2 outgoing linksRoute to /reflect for connection finding
Schema driftEnum values not in templateUpdate note to use valid values, or propose enum addition

batch mode (--all)

When verifying all notes:

  1. Discover all notes in {DOMAIN:notes}/ directory
  2. For each note, run the full verification pipeline
  3. Produce summary report:
    • Total notes checked
    • PASS / WARN / FAIL counts per category
    • Top issues grouped by check type
    • Notes needing immediate attention (FAIL items)
    • Pattern analysis across failures

Performance note: In batch mode, the recite cold-read test runs honestly for each note. Do not "warm up" by reading multiple notes first — each prediction must be genuinely cold.

standalone invocation

/verify [note]

Run all three checks on a specific note. Full detailed report.

/verify --all

Comprehensive audit of all notes in {DOMAIN:notes}/. Summary table + flagged failures.

/verify --handoff [note]

Pipeline mode for orchestrator. Runs full workflow, outputs RALPH HANDOFF block.

handoff mode (--handoff flag)

When invoked with --handoff, output this structured format at the END of the session:

=== RALPH HANDOFF: verify ===
Target: [[note name]]

Work Done:
- Recite: prediction N/5, retrieval #N, [pass/fail]
- Validate: [PASS/WARN/FAIL] (N checks, M warnings, K failures)
- Review: [PASS/WARN/FAIL] (N checks, M issues)
- Description improved: [yes/no]

Files Modified:
- {DOMAIN:notes}/[note].md (description improved, if applicable)
- [task file path] (Verify section updated, if applicable)

Learnings:
- [Friction]: [description] | NONE
- [Surprise]: [description] | NONE
- [Methodology]: [description] | NONE
- [Process gap]: [description] | NONE

Queue Updates:
- Mark: verify done for this task
=== END HANDOFF ===

task file update

When a task file is in context (pipeline execution), update the ## Verify section:

markdown
## Verify
**Verified:** [UTC timestamp]

Recite:
- Prediction: N/5 — [brief reason]
- Retrieval: #N via MCP vector_search or CLI vsearch (or "deferred")
- Description: [kept/improved — brief note]

Validate:
- Required fields: PASS
- Description constraints: PASS (147 chars, adds mechanism)
- Topics: PASS (["[[topic]]"])
- Optional: [status]

Review:
- Frontmatter: PASS
- {DOMAIN:topic map} connection: PASS ([[topic]])
- Wiki links: N outgoing
- Link resolution: PASS (all resolve)

Overall: [PASS/WARN/FAIL]

Pipeline Chaining

Verify is the final pipeline phase. After verification completes:

  • manual: Output "Verified. Pipeline complete." — no next step
  • suggested: Output completion summary AND suggest marking task done in queue
  • automatic: Task marked done, summary logged to task file

If verification FAILS (recite score < 3 or any FAIL-level issue), do NOT mark done. Instead:

  • Output what failed and what needs fixing
  • Keep task at current_phase: "verify" for re-run after fixes

The chaining output uses domain-native vocabulary from the derivation manifest.

queue.json update (interactive execution)

When running interactively (NOT via orchestrator), YOU must execute queue updates:

bash
TIMESTAMP=$(date -u +"%Y-%m-%dT%H:%M:%SZ")
jq '(.tasks[] | select(.id=="TASK_ID")).status = "done" | (.tasks[] | select(.id=="TASK_ID")).completed = "'"$TIMESTAMP"'"' ops/queue/queue.json > tmp.json && mv tmp.json ops/queue/queue.json

The queue path uses the domain-native operations folder. Check ops/ or equivalent.

critical constraints

never:

  • read the note before forming the recite prediction (cold-read is the whole point)
  • auto-fix FAIL-level issues without flagging them in the report
  • skip the semantic retrieval test without reporting "deferred"
  • leave failures without suggested improvements
  • declare PASS after checking only some categories

always:

  • run recite FIRST (before validate/review — execution order is load-bearing)
  • be honest about prediction accuracy (inflated scores defeat the purpose)
  • suggest specific improved descriptions for score < 3
  • report all severity levels clearly (PASS/WARN/FAIL)
  • update task file if one is in context
  • capture observations for friction, surprises, or methodology insights

© 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/verify of agenticnotetaking/arscontexta.

  • SKILL.md
  • skill.json

Open the folder on GitHubat commit 2acfd5c

Compare with similar skills

Verify 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.

Verify compared with similar skills
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Fin Guru ChecklistAojdevStudio/Finance-Guru322—~382Automated safety check: PassCustom licence
Feature Plannerserendipity1004/cc-feature-implementer176—~2.4kAutomated safety check: PassNone
Ccg Workflowfengshao1227/ccg-workflow5.9k—~2.3kAutomated safety check: PassMIT
Conducty Checkpointrobertbarclayy/conducty176—~1.5kAutomated safety check: PassMIT
Mission Plannerjdforsythe/forge151—~3.5kAutomated safety check: PassMIT

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    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
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    agenticnotetaking/arscontexta

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

    3.5k GitHub stars~4.9k tokensUpdated 7 mo ago
    Auto-check: notes

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Questions about Verify

What does Verify do?

Combined verification — recite (description quality via cold-read prediction) + validate (schema compliance) + review (health checks). Verify is an agent skill from agenticnotetaking/arscontexta. Combined verification — recite (description quality via cold-read prediction) + validate (schema compliance) + review (health checks).

When should I use Verify?

Verify fits situations like: verify note quality; check note health.

How do I install Verify in Claude Code?

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

How do I install Verify in Codex?

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

Can I use Verify 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 verify -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/verify, .gemini/skills/verify, .github/skills/verify and .opencode/skills/verify in your project.

What does Verify need to run?

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

Does Verify 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 Verify safe to install?

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.

What licence does Verify use?

Verify 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 Verify use?

About 5.7k tokens (SKILL.md is roughly 23k 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 Verify?

Skills that share tags, products or a category with Verify: Fin Guru Checklist (AojdevStudio/Finance-Guru, 322 stars), Feature Planner (serendipity1004/cc-feature-implementer, 176 stars), Ccg Workflow (fengshao1227/ccg-workflow, 5.9k stars) and Conducty Checkpoint (robertbarclayy/conducty, 176 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Verify?

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.