Which integrations are available for this server?

Enables a CI gate that blocks pull requests when the 'spider weave' check fails, preventing insecure code from being merged. Provides a shared pattern database backend using Postgres/Supabase for deduplication, anonymous contribution, and retrieval of verified bug patterns.

How do I use RECLUSE Spider MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@RECLUSE Spider MCP Server check this contract for known bug patterns" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

RECLUSE Spider MCP Server

by vinsenzo83

Overview Schema Related Servers Score Discussions

JavaScript

Local

🕸️ RECLUSE

npm

An autonomous detect→fix→verify loop for the bugs that hide in your system's seams. MCP server + GitHub Action + a shared bug-corpus.

The recluse bites the bugs that hide in the seams.

When you vibe-code, writing is fast but debugging is slow — you didn't write the code, so you don't know where it broke. And the bugs that actually bite in production usually aren't in one file; they live in the seams between subsystems: a value written in cents and read as dollars, an auth check that passes on NULL, an RLS policy that leaks every row, a SECURITY DEFINER function anyone can call because the grant went to PUBLIC.

RECLUSE traces those seams (producer → store → consumer), catches the mismatch with live evidence, dispatches a fixer, re-verifies — then pools every caught pattern into a shared corpus so the next project catches the same trap at round-zero.

→ Live page & corpus: https://eduverse-ai.app/recluse

What it does — a closed loop

WEB (detect) → CATCH (contract mismatch) → SPIDER (autonomous fix) → RE-WEAVE (verify) → loop until 🔴 = 0

WEB — fan out read-only QC agents with different viewpoints (security, data/payment, integration contracts). One reviewer's blind spot is another's catch.
CATCH — where a producer/consumer contract tears, report it with the exact coordinate (file:line, constraint, RLS policy) and a severity.
SPIDER — dispatch a fixer agent per finding into an isolated git worktree; several fix in parallel without collision. Only the orchestrator merges — detectors stay read-only.
RE-WEAVE — re-run the fixed strand and its neighbors to prove no regression. Loop until clean.

The orchestration (fan-out, dedup, dispatch, re-weave) is deterministic code — a plain loop, not model-driven. Only the spiders (detect/fix) are LLM calls.

What it caught (real, on a live app)

Run against a production Next.js + Supabase/Postgres app, it found privilege-escalation bugs a static review had passed:

a SECURITY DEFINER function callable by anon, because REVOKE EXECUTE ... FROM anon is a no-op when the grant is to PUBLIC;
a plpgsql guard bypassed by NULL three-valued logic — NULL OR false is NULL, which IF NOT treats as false, so the guard's exception never fired;
a SECURITY DEFINER view that let anon write through it, bypassing RLS on the base table.

All three only reproduced against a live endpoint — reading the source called them safe. Each is now a built-in detection signal (spider_signals) with the exact query to reproduce it — alongside curriculum order-integrity (band inversion / cyclic prerequisites), per-user-limit anonymous farming, stream-budget 502s, and event producer↔consumer contract drift.

Related MCP server: acta-mcp

Quickstart

① MCP server — for your editor (Claude, Cursor, any MCP client)

One line, no clone — published on npm:

{ "mcpServers": { "recluse": {
  "command": "npx",
  "args": ["-y", "recluse-mcp"],
  "env": { "SPIDER_CORPUS_API": "https://eduverse-ai.app/api/corpus" }
} } }

Tools: spider_plan · spider_classify_tier · spider_checklist · spider_signals (detection query/grep per bug class) · spider_record_pattern (local + shared contribution) · spider_pull_corpus (collective pull, verified-first). Resources: spider://checklist · spider://queries · spider://corpus · spider://blackbox.

Knowledge is built into the server — the checklist, contract queries, and a seed corpus of live-verified bug classes ship inside the package, so it works standalone. If you also run the spiderweb-qc skill, its richer reference files are picked up automatically (set SPIDER_REF_DIR).

② GitHub Action — gate every PR (blocks on 🔴)

# .github/workflows/recluse.yml
name: RECLUSE
on:
  pull_request:
    branches: [main]
jobs:
  weave:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with: { fetch-depth: 0 }
      - uses: vinsenzo83/recluse@v1.1
        with: { base: origin/${{ github.base_ref }} }
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}

Without a key it runs in advisory mode (known-trap checklist, non-blocking). With ANTHROPIC_API_KEY it does a real weave on the diff, blocks on 🔴, and contributes new patterns to the corpus.

Spider tiers — resource by criticality

Not every path deserves Opus. Tier by risk:

Spider	Model	Scope	Verification
🕷️ King	Opus	payments · auth · GDPR · data loss · secrets	adversarial multi-vote + re-weave
🕸️ Mid	Sonnet	business logic · contracts · i18n · gating	single + one refute pass
🐜 Baby	Haiku	docs · dead code · lint	single pass

So you're not paying Opus rates to check a comment — and the paths where a false negative is expensive get the most scrutiny.

The shared corpus

Every caught bug is distilled into a stack-keyed pattern (detection signal + fix principle) and pooled. A new project checks known traps at round-zero — the more it's used, the thicker the net.

Scrub-first: file paths, code literals, secrets (keys / JWT / high-entropy), emails, URLs, IPs are stripped before anything is shared; anything still suspicious is rejected. Only the generalized technique leaves your machine.
Anonymous: contributors/projects are stored as irreversible hashes — no identity, no raw text.
Consensus: a pattern needs 3 independent contributors before it's marked verified.

Corpus API (base = `SPIDER_CORPUS_API`)

POST /patterns {klass,name,signal,fix,tags?,severity?,tier?} → scrub · dedup · trust accrual (rate-limited per IP)
GET /patterns?tags=postgres,payment&class=unit&verified=1&limit=50 → pull the corpus (verified=1 = consensus-verified only, for production use)

Contributing is open — pick a contributor token, POST. No signup, no key required (the token is your anonymous identity, not a credential).

Honest limitations

Early (v0.1.1), best-tested on Next.js / Supabase / Postgres — other stacks have thinner coverage until the corpus fills in.
Running it costs tokens. The tool is free to use, but a full weave spawns model calls (Opus on critical paths). It's not free to run.
Not open-source. The MCP client is on npm and the repo is source-available, but not OSI-licensed; the corpus backend is a hosted, closed service. A free tool, not an open one.
"Live evidence only" is the rule, not a proof. The model can still be wrong — the re-verify step is the guard, not a guarantee.

Principles

No fabrication (live evidence only) · scrub-first (zero code/secret leakage) · opt-in & anonymous · detection = read-only, fixes = isolated spiders.

Repo layout

src/index.js       # MCP server (stdio)
cli/recluse.mjs    # CI weave CLI
action.yml         # GitHub Action

The shared corpus is a hosted service (https://eduverse-ai.app/api/corpus); its backend (schema, scrub pipeline, ingestion) is closed-source.

License

UNLICENSED (proprietary). Free to use; not open-source.

Available Tools

6 tools

spider_checklistA

차원별 전수 체크리스트(증거 없이 PASS 금지)를 반환. 파일 부재 시 내장 체크리스트 + 세션 실증 그물코(권한상승·순서·비용·이벤트) 병합.

ParametersJSON Schema

Name	Required	Description	Default
`dimension`	No	인증/데이터/단위/끊긴고리/권한경계/폴백/런타임/순서/비용/이벤트 등

TDQS

A3.6/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, description provides behavioral context: fallback to built-in checklist when file is missing, and merging with session verification data (privilege escalation, order, cost, events). However, it does not mention side effects, auth requirements, or whether the tool is read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The single sentence conveys the tool's purpose and key behavior efficiently, though it is somewhat dense and could benefit from clearer structure or bullet points for readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and one optional parameter, the description explains the output nature (checklist with evidence) and fallback logic. However, it does not clarify that the dimension parameter is optional or what happens when omitted, leaving some ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% but description adds enumeration of dimension values (인증/데이터/단위/끊긴고리/권한경계/폴백/런타임/순서/비용/이벤트 등) that are not present in the schema, enhancing parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it returns a per-dimension full checklist requiring evidence, and merges built-in checklist with session verification meshes. It is specific about the resource and verb, and distinguishes from sibling tools like spider_plan or spider_classify_tier by focusing on checklist generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives such as spider_plan or spider_signals. Usage is only implied from the description, with no conditions, prerequisites, or exclusions stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

spider_classify_tierA

finding을 King/Mid/Baby 거미로 분류하고 모델·검증표수를 반환. 수정 거미 급파 전 호출. 비용·권한상승·순서무결성 경로 반영.

ParametersJSON Schema

Name	Required	Description	Default
`area`	Yes	영역 키워드(payment, definer, budget, order, event 등)
`severity`	No

TDQS

A3.5/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It mentions that the tool reflects cost, privilege escalation, and order integrity path, which adds specific behavioral context. However, it does not clarify if the tool modifies data (though '분류' and '반환' suggest read-only) or provide details on side effects or security implications.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the main action, and contains no unnecessary words. Every sentence provides value: purpose, when to use, and behavioral context. Excellent conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description gives the core purpose and usage context but lacks details on the return format (model/verification key count) and does not fully explain how parameters like 'severity' influence behavior. Given the absence of output schema and annotations, more completeness would be beneficial.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 50% (only 'area' has a description). The tool description does not add any extra meaning beyond what the schema provides; it does not explain the 'severity' parameter or elaborate on the usage of 'area'. This leaves the undocumented parameter unclear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action: classifying findings into King/Mid/Baby spiders and returning model/verification counts. It also mentions the context of use (before dispatching modified spiders), providing a specific verb and resource. However, it does not explicitly distinguish this from siblings, though the siblings have different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool ('수정 거미 급파 전 호출' - call before dispatching modified spiders). It does not provide when-not-to-use or alternatives, but the context is clear and the sibling tools are distinct in function.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

spider_planB

대상에 대한 거미줄 검증 계획을 반환: 던질 거미(차원·등급·모델), 우선 점검할 학습 패턴, 절대원칙. 라운드 시작 시 호출.

ParametersJSON Schema

Name	Required	Description	Default
`target`	Yes	검증 대상(레포/기능/배포 범위)
`thorough`	No	true면 거미 수↑·다수결 강화

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must disclose behavioral traits. It does not mention side effects, idempotency, rate limits, or authentication needs, leaving significant gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the purpose. It is concise without being terse, though it could be more efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so the description should describe the return structure. It lists components but lacks detail on format or fields, leaving the agent with an incomplete picture.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with both parameters described. The description adds high-level context (e.g., 'spiders to throw') but does not significantly enhance meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns a verification plan for a target, specifying components like spiders, learning patterns, and principles. It distinguishes itself from siblings by focusing on planning rather than classification or checklist.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It mentions 'call at round start,' providing timing context, but lacks explicit guidance on when not to use or alternatives among sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

spider_pull_corpusA

공유 corpus(집단 거미 두뇌)에서 검증된 패턴을 가져온다. verified 우선·tag/klass 필터. 원격 미설정/실패 시 내장 corpus로 graceful 폴백. 라운드 시작 시 알려진 함정 우선 점검용.

ParametersJSON Schema

Name	Required	Description
`tags`	No	스택 태그 쉼표(postgres,nextjs,payment 등)
`klass`	No
`limit`	No
`verified`	No	true(기본)=합의검증된 패턴 우선. false=미검증 포함

TDQS

A3.8/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses key behaviors: verified priority, filtering by tag/klass, and graceful fallback to internal corpus. However, with no annotations, it omits details like authentication, rate limits, or response format, which are important for a pull operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with two sentences that front-load the purpose and then provide additional details. No extraneous words or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 4 parameters, no output schema, and no annotations, the description covers the core functionality but lacks details on return format, pagination, or the nature of the remote corpus, leaving some gaps for a fully autonomous agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 50% (tags and verified have descriptions, klass and limit do not). The description does not add any new parameter semantics beyond what is already in the schema, failing to compensate for the undocumented parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's verb 'pull' and resource 'shared corpus', and specifies filtering by tag/klass and verified priority, distinguishing it from siblings like spider_record_pattern which presumably records patterns.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It specifies a concrete use case: 'for checking known traps at round start'. However, it does not explicitly state when not to use it or provide alternatives, leaving some ambiguity about broader applicability.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

spider_record_patternB

이번 라운드에 잡은 버그를 학습 corpus에 1줄 패턴으로 증류 추가(다음 라운드에 먼저 점검). 실제로 잡은 것만.

ParametersJSON Schema

Name	Required	Description
`fix`	Yes	수정 원칙
`hit`	Yes	적중 예시(프로젝트·날짜·file:line)
`name`	Yes	짧은 이름
`tags`	No	스택 태그 쉼표(postgres,stream,curriculum 등)
`klass`	Yes	버그 클래스(단위/제약·끊긴고리·권한경계 등)
`signal`	Yes	탐지 신호 — 어떤 쿼리/grep/코드위치로 잡는가

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavior. It mentions adding to a corpus and being checked later, but it does not describe side effects (e.g., overwrite behavior, deduplication), input validation, return values, or error conditions. The description is too minimal for a tool with 6 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with two short sentences, no wasted words, and immediately front-loads the action. It earns its place without any fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (6 parameters, no output schema, no annotations, multiple siblings), the description lacks important context: it does not mention prerequisites (e.g., an existing corpus), the format or structure of the one-line pattern, what the tool returns, or how it integrates with the sibling tools. It is insufficient for a developer to fully understand usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with each parameter having a clear Korean description. The tool description adds no additional meaning or context beyond what the schema already provides, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: to distill and add bug patterns caught in the current round to a learning corpus as one-line patterns, to be checked first in the next round. It uses specific verbs and resources, and the restriction 'only actually caught ones' adds clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use (after catching bugs in the round) but provides no guidance on when not to use or how this tool differs from siblings like spider_checklist, spider_signals, or spider_pull_corpus. No explicit alternatives or exclusions are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

spider_signalsA

실증 버그 클래스의 탐지 신호(라이브 쿼리/grep)와 수정 원칙을 반환. klass/tier/tag로 필터. 계획 후 이 신호를 그대로 실행해 증거를 수집하라.

ParametersJSON Schema

Name	Required	Description
`tag`	No	스택 태그(postgres, stream, curriculum 등)
`tier`	No
`klass`	No	버그 클래스 부분일치(권한경계/순서무결성/비용공격/이벤트계약/단위 등)

TDQS

A3.9/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool returns detection signals and fix principles, implying a read-only operation. However, it does not detail output format, side effects, or prerequisites, limiting transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences in Korean, tightly packed with purpose and usage guidance. No redundant information; every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 3 optional parameters, no output schema, and no annotations. The description covers what it returns and when to use it, but omits output structure and behavioral details like idempotency or rate limits. Adequate for basic use but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 67% (tag and klass have descriptions; tier lacks one). The description adds that parameters are filters, which aligns with schema. However, the missing description for tier enum values (king/mid/baby) is a gap that the description does not fill.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns detection signals and fix principles for empirical bug classes, with filtering by klass/tier/tag. It distinguishes from siblings like spider_plan (planning) and spider_pull_corpus (pulling corpus) by emphasizing execution after planning.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'after planning, execute these signals as-is to collect evidence,' providing clear context for when to use the tool. It also mentions filtering parameters. However, it does not explicitly exclude use cases or name alternatives among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

6 tool updatesv0.2.0Jul 30, 2026
- First observedspider_checklist
- First observedspider_classify_tier
- First observedspider_plan
- First observedspider_pull_corpus
- First observedspider_record_pattern
- First observedspider_signals

Install Server

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TDQS

A3.7/5.0

Disambiguation5/5

Each tool targets a distinct step in the spider verification workflow: planning, classification, checklist, signals collection, pattern recording, and corpus retrieval. There is no overlap in functionality.

Naming Consistency4/5

All tools use the 'spider_' prefix and underscore separators, but some names are verb_noun (e.g., spider_plan, spider_record_pattern) while others are noun-only (e.g., spider_checklist, spider_signals). This minor inconsistency prevents a perfect score.

Tool Count5/5

With 6 tools, the set is well-scoped for a specialized verification server. Each tool earns its place without being redundant or excessive for the domain.

Completeness4/5

The tools cover the core lifecycle: planning, classification, checklists, signal detection, pattern recording, and corpus access. A minor gap could be a dedicated analysis or reporting tool, but the workflow is largely complete.

Maintenance

ActivitySlowing

ResponsivenessSyncing

Resources

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