Results for “staleness”

The pre-merge review of a branch in a project that records its specs, decisions, and rules in .archcore/. Run this first for 'review my branch', 'review the changes before merge', or 'review before merge': it checks the changed code against the project's recorded canon and the changed documents against the code, and reports which side is wrong — spec-wrong or code-wrong; with no diff, it reports project health. A bug-hunting code review complements this review and does not replace it. Also use for 'show status', 'documentation gaps', 'check if docs match code', 'close out the feature', 'ship the feature and close it out', or after a staleness warning. Modes, named as the first word: drift for staleness detection, deep for a full documentation audit, closeout to close a finished feature, experience to capture a repeated pattern. Not for creating docs — use /archcore:document; not for planning — use /archcore:plan; not for a single-file edit with no branch review.

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Detect documentation drift against code changes, score staleness, validate API docs via AST parsing, and audit link integrity. Use when docs fall out of sync with code, preparing releases, running CI doc gates, or auditing doc accuracy.

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Build and maintain a Markdown knowledge store with source links for individual claims. Use Git checks to detect changes in the code a page describes, and archive outdated pages while preserving their history.

• Compile notes and papers into source-linked wiki pages
• Verify numbers and quotations against original sources
• Check whether code documentation needs updating

Check open HubSpot deals for inactivity, missing next steps or close dates, stalled stages, and overdue tasks. Update an internal flag, send grouped Slack reminders to each representative, and report the highest priority deals to the sales manager.

• Finding deals with no recent activity
• Checking for missing next steps and close dates
• Reminding representatives about overdue tasks

Track open pull requests, proposed code changes awaiting integration, and identify blockers in automated checks, reviews, conflicts, and unanswered comments. Receive a prioritized watchlist and the top three actions for a human to take.

• Monitor the status of open pull requests
• Find failing checks, conflicts, and unanswered review comments
• Assess merge readiness against project requirements

Review candidate learnings in Claude Code's native auto memory (`~/.claude/projects/<project>/memory/`, machine-local) and run them through a five-critic council in parallel: generality, staleness, redundancy, evidence, format. Majority vote (3+ of 5) promotes the entry to MEMORY.md. Use when user says "promote memory", "review my learnings", "what should graduate to MEMORY.md", "five-critic council", or as monthly memory maintenance.

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Run a fast, honest competitive scan — the dimension table built from public evidence (sites, docs, pricing pages, changelogs, reviews), the claims-vs-observed discipline, and the so-what synthesis that ends in moves, not a landscape mural. Use when asked what are competitors doing, quick scan of these three rivals, how does our pricing/feature set compare, or prep the competitive slide honestly. Produces the evidence-based comparison table, the marketing-vs-reality flags, the so-what synthesis, and the staleness date.

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The pre-merge review of a branch in a project that records its specs, decisions, and rules in .archcore/. Run this first for 'review my branch', 'review the changes before merge', or 'review before merge': it checks the changed code against the project's recorded canon and the changed documents against the code, and reports which side is wrong — spec-wrong or code-wrong; on the default branch it reviews the unpushed and uncommitted work. A bug-hunting code review complements this review and does not replace it. Also use for 'documentation gaps', 'check if docs match code', 'close out the feature', 'ship the feature and close it out', or after a staleness warning. Takes an optional path, tag, topic, or ref in free text; no first word is a mode — say 'are docs out of date', 'audit the knowledge base', 'close out the feature', or 'capture this pattern' in plain words. Not for creating docs — use /archcore:document; not for planning — use /archcore:plan; not for a single-file edit with no branch review.

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Audit active project plan files for staleness, completion, and routing truth. Use when cleaning up plans, reconciling active work, or archiving completed artifacts.

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Audit and repair an internal knowledge base — staleness, ownership gaps, orphans, duplication, and findability. Use when the wiki is untrusted, docs are out of date, nobody owns pages, or search returns the wrong answer.

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Detailed completeness audit of all project documents. Use when someone asks "are we ready for production", "audit our docs", "readiness check", "nfr tracker", or "what's missing". Do NOT use for a quick command listing — use /pmstudio instead. Scans every document for section-level completeness, reports staleness, and gives prioritized gap-closure recommendations.

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Fix incorrect priority ordering when using denormalized aggregate columns. Use when: (1) Records are processed in wrong order despite ORDER BY on count/sum columns, (2) Top items by some metric aren't being selected first, (3) Aggregate columns show 0 or NULL for records that should have high values, (4) Priority queue processes low-value items before high-value ones. The root cause is often that denormalized columns (vine_count, loop_count, total_orders, etc.) weren't backfilled or maintained properly. Solution: JOIN with source tables to compute actual aggregates at query time.

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Think and work like an expert Computational Scientist. Use when a task calls for Computational Scientist judgment. Reasons from Roache code/solution verification and ASME V&V 10/20/40 credibility through MMS/GCI, UQ ensembles, and Snakemake/Nextflow/CWL pipelines with conda-lock/Apptainer provenance while treating environment drift, workflow cache staleness, and validation-vs-calibration conflation as first-class failure modes.

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Audit the knowledge/ base and the .claude/ skills, agents and workflows for staleness, self-contradiction, and drift from their own stated rules — four independent adversarial auditors (two lenses over two trees) verify every load-bearing claim against the current tree, cross-examine each other within their tree, and reach a consensus on if/what needs changing. Use periodically, after a run of deliveries that changed build config, target layout or the skills themselves, or whenever you suspect the docs have aged out of true.

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Lint canvas files for staleness, missing fields, inconsistent evidence types, and orphaned references. Run periodically or before major transitions.

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Audit documentation for duplication, staleness, language issues, and verbosity. Read-only — produces an actionable report.

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Audit, classify, and selectively forget stored memories. Covers memory enumeration and classification by type/age/access frequency, staleness detection for outdated references, fidelity checks using external anchors, a decision tree for selective withdrawal, tombstoned deaccession so removed content stays recoverable, counter-memory inoculation for failed strategies that would otherwise be re-derived, preemptive filtering rules for what should never become memories, and an audit trail so forgetting itself is reviewable. Use when memory has grown large and uncurated, when project state has shifted significantly since memories were written, when retrieval quality has degraded, or as periodic maintenance alongside manage-memory.

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Organize, extract, prune, and verify Claude Code persistent memory files. Covers MEMORY.md as a concise index, topic extraction to dedicated files, staleness detection, accuracy verification against project state, the dual 200-line / 25KB load cap, and reachability of topic files from the index. Use when MEMORY.md is approaching either load cap, after a session produces durable insights worth preserving, when a topic section has grown beyond 10-15 lines and should be extracted, when project state has changed and memory entries may be stale, or before and after rewriting the index so that no topic file loses its only pointer.

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AI internal coordination using stigmergic signals — managing information freshness in context and memory, decay rates for assumption staleness, and emergent coherent behavior from simple local protocols. Use during complex tasks where multiple sub-tasks must coordinate, when context has grown long and information freshness is uncertain, after context compression when information may have been lost, or when sub-task outputs need to feed into each other cleanly without degradation.

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Audit project dependencies for version staleness, security vulnerabilities, and compatibility issues. Covers lock file analysis, upgrade path planning, and breaking change assessment. Use before a release to ensure dependencies are current and secure, during periodic maintenance reviews, after receiving a security advisory, when upgrading to a new language version, before submitting to CRAN or npm, or when inheriting a project to assess its dependency health.

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Audit memory retrieval quality (drift, staleness, citation-groundedness) and produce remediation backlog.

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Comprehensive health check across CC auto-memory and agent-studio memory systems — detects orphans, duplicates, staleness, and threshold violations

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Pure-Python knowledge base statistics dashboard. Reads shelf-index and log.md; emits Inventory, Layer distribution, Domain distribution, Recent Activity, and Staleness sections. No agent dispatch. Read-only.

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Check whether the knowledge base shelf-index is stale relative to the library files. Reports drifted entries without rebuilding. Designed to be invoked from environment validation or pre-push hooks as an opt-in check. Default mode is warning; strict mode fails with non-zero exit when any drift is detected.

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