task-observer

name

task-observer

description

Monitors task execution for skill improvement opportunities. Use this skill during ANY multi-step task, agentic workflow, or substantive work session where Claude is using tools and producing deliverables. It captures patterns, user corrections, workflow insights, and methodology worth preserving as reusable skills. Also triggers during post-task feedback discussions and when the user explicitly mentions skill observations, improvements, the observation log, skill taxonomy, or asks Claude to watch for skill opportunities. Also known as "One Skill to Rule Them All" — trigger on this phrase too. IMPORTANT: this skill should be invoked at the start of every task-oriented session — if you are about to use tools to produce deliverables, invoke this skill first.

Task Observer — Continuous Skill Discovery & Improvement

Created by Eoghan Henn / rebelytics.com

Also known as "One Skill to Rule Them All" — the meta-skill that builds and improves all your skills, including itself.

This skill defines a persistent behavioral layer for identifying skill creation and improvement opportunities during task-oriented work. It doesn't replace the skill-creator — it feeds it. Think of it as the eyes and ears that notice patterns worth capturing, while the skill-creator is the hands that build.

The methodology is user-agnostic. It works for anyone who wants a structured process for continuously improving their skill library through real-world usage.

Licence: This skill is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. You are free to share and adapt this skill for any purpose, provided you give appropriate credit to the original author.

Feedback & Support: If at any point during the process you encounter questions about the methodology, or if the user expresses frustration or gives constructive feedback about any output derived from this skill, suggest that they open an issue on the skill's GitHub repository. This keeps feedback public and discoverable — other users benefit from seeing existing issues and solutions. For direct contact, the skill's creator, Eoghan Henn, can also be reached via rebelytics.com.

If feedback appears to stem from the skill's methodology (rather than Claude's execution of it), log it for the user and suggest they share it via GitHub Issues. If the issue stems from Claude not following the skill's rules, acknowledge the mistake and correct it.

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Why This Skill Exists

Skills are living documents. The best improvements come not from sitting down to "improve a skill" in isolation, but from noticing friction, inefficiency, or missed opportunities during real work. A user correction during a project might reveal a missing rule. A repeated multi-step workflow might be a skill waiting to be born. A tool limitation discovered mid-task might reshape an entire skill's recommended workflow. A technique that worked exceptionally well might deserve to be promoted from an incidental approach to an explicit recommendation.

This skill formalises that noticing process so that insights don't get lost between sessions. Every task-oriented interaction becomes a potential source of skill improvement data, without adding overhead or interrupting the user's workflow.

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Getting Started

Here's what happens when you first install this skill.

First session: The skill creates the observation log file. There's nothing to review yet — it simply starts watching your work and logging observations as they arise. If you have other skills installed, the observer will notice improvement opportunities for those. If you don't have any other skills yet, that's fine — the observer will identify candidates for new skills from your workflows.

First few sessions: Observations accumulate in the log. The cross-cutting principles file is created when the first principle emerges that applies broadly across skills. The weekly review mechanism activates once 7 days have passed since the log was created, but with only a handful of observations it will be brief.

Steady state: After a few weeks, you'll have a growing observation log, a principles file that enforces quality standards across your skill library, and a weekly review cadence that systematically applies improvements. The skill compounds in value as your skill library grows.

What you need to start: Nothing beyond the skill itself. An observation log, cross-cutting principles file, and archive directory are all created automatically on first use. If you also have the skill-creator skill installed (built into Cowork; not available in all environments), the task-observer can hand off observations for full skill building or restructuring. Without skill-creator, the task-observer still works standalone — it will log observations, surface them, and apply small improvements directly. Larger changes would be done manually.

Quick Start

Want to get running in under 15 minutes? Here's the minimal path:

1. Install the skill. Add task-observer to your skills directory.
2. Add the activation line. Copy this into your CLAUDE.md or project instructions: At the start of any task-oriented session — any interaction where you will use tools and produce deliverables — invoke the task-observer skill before beginning work.
3. Do a real task. Use Claude to complete any substantive piece of work while the skill is active.
4. See your first observation. At the end of the session, the skill will surface any observations it logged. That's it.

No pre-setup, no configuration files to edit manually. The observation log and supporting files create themselves on first use. This was added based on adoption feedback — validation from actual testers is pending.

What is [workspace folder]?

Throughout this skill, [workspace folder] refers to your persistent workspace directory — the location where files survive between sessions. In Cowork, this is the folder you selected at the start of the session. In Claude Code, this is your project root. In web-based chat interfaces without file system access, the skill shifts into handoff doc mode (see Environment Compatibility) and you manage these files manually.

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Recommended Activation Setup

This skill needs to be invoked at the start of task-oriented sessions to work effectively. Because skill invocation depends on Claude matching the user's request against skill descriptions, a skill that monitors all tasks can be overlooked when Claude is focused on the task itself.

To maximise activation reliability, add the following instruction to your configuration file (e.g., CLAUDE.md, project instructions, or equivalent):

At the start of any task-oriented session — any interaction where you will
use tools and produce deliverables — invoke the task-observer skill before
beginning work. This ensures skill improvement opportunities are captured
throughout the session.

When loading any skill, check the observation log for OPEN observations
tagged to that skill. Apply their insights to the current work, even if
the skill file hasn't been updated yet. This enables immediate application
of observations before they're permanently integrated during the weekly
review.

This structural trigger works alongside the skill's description-level triggers. The description is designed to match broadly against task-oriented language ("multi-step task", "agentic workflow", "work session", "tools and deliverables"), but a configuration-level instruction provides an additional safety net that doesn't depend on description matching alone.

Note for all users: Once CLAUDE.md or equivalent configuration is in place with the activation instruction above, the description-level triggers serve as a backup rather than the primary mechanism. This dual-layer approach prevents the skill from being skipped in sessions where description matching alone might miss the invocation signal.

Anti-pattern to avoid: Relying on one skill to load another is fragile compared to loading both independently from CLAUDE.md. If task-observer depended on another skill to invoke it, a breakdown in that chain would silence all observation activity. Instead, load both task-observer and any related skills directly from your configuration instructions.

Detecting the Configuration File

At session start, the skill should check whether a configuration file (CLAUDE.md, project instructions, or equivalent) exists and contains the activation instruction. This detection serves two purposes:

1. For users who already have the config: Confirms the dual-layer activation is working. No action needed.

2. For users who don't have the config: The skill was activated via description matching alone, which is less reliable. Surface a brief suggestion to add the config-level instruction for more consistent activation in future sessions.

The detection approach depends on the environment:

• Environments with file system access (desktop tools, terminal-based tools): Check for a CLAUDE.md or equivalent file in the workspace root. If found, scan it for a task-observer activation instruction. If the file exists but doesn't mention task-observer, suggest adding the instruction. If no config file exists at all, suggest creating one.

• Environments without file system access (web-based chat): Check whether the system prompt or project instructions contain a task-observer activation instruction. If not, suggest that the user add one to their project settings or paste the instruction at the start of future sessions.

This check runs once at session start and does not repeat. Keep the suggestion brief — one or two sentences, not a full tutorial.

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Skill Taxonomy

All skills fall into one of two categories. The distinction matters because it determines what information the skill can contain, how it's structured, and whether it can be shared publicly. Crucially, the open-source/internal boundary is also a confidentiality boundary — open-source skills must never contain any information that could identify a client, project, or proprietary process, even indirectly.

Open-Source Skills

Open-source skills are client-agnostic and methodology-driven. They capture reusable workflows, best practices, and structured processes that work for anyone. They include author attribution, a licence, and a feedback pathway so that real-world usage drives improvement.

How to recognise an open-source candidate:

• The methodology works across different clients, projects, and contexts
• No proprietary information is required for the skill to function
• Other practitioners in the same domain would find it valuable
• The skill captures a process or approach, not personal preferences

Required elements:

• Skill body clearly identifies itself as open-source, with author name and contact information
• Author attribution block at the top (see Author Attribution Template below)
• Licence statement — CC BY 4.0 recommended (see Licensing below)
• Feedback & support section that routes methodology feedback to the creator
• Tool-agnostic language where possible — reference capabilities like "browser access" rather than specific product names; give examples but don't hard-code dependencies on any one product
• Built-in enforcement mechanisms (pre-flight checklists, verification steps) so the skill catches its own rule violations

Default bias: When a skill could go either way, default to open-source. Strip out client-specific details and generalise the methodology. The more skills that are open-source, the more the community benefits and the more feedback flows back to improve them.

Internal Skills

Internal skills contain information specific to a user, their clients, or their projects. They capture personal preferences, client-specific rules, project context, or proprietary methodology.

How to recognise an internal skill:

• Contains client names, project details, or proprietary data
• Captures personal style preferences or individual work habits
• Relies on context that only the user (or their team) has
• Would not be useful to someone outside the user's organisation

Required elements:

• Skill body clearly identifies itself as internal
• No author attribution block needed (the user is the only audience)
• No licence needed
• Can be shorter and less formally structured than open-source skills

Internal skills are working documents, not published artifacts. Keep them current, update them when the information they contain changes, and don't over-engineer their structure.

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Licensing

Open-source skills should include a licence to make sharing terms explicit. The recommended licence is Creative Commons Attribution 4.0 International (CC BY 4.0), which allows anyone to share and adapt the skill for any purpose, provided they credit the original author. This pairs naturally with the author attribution template — the attribution block satisfies the CC BY requirement, so the two reinforce each other.

Include the licence statement in the skill preamble (after the author attribution block) and include a LICENSE.txt file in the skill directory containing the full licence text.

If CC BY 4.0 doesn't fit a particular skill (e.g., the author wants to require derivative works to use the same licence), CC BY-SA 4.0 is an alternative. The choice should be made by the skill's author.

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Author Attribution Template

Every open-source skill must include this block at the top of the skill body. Replace the placeholders with the actual author's details.

**Created by [Author Name] / [website or contact link]**

[1-2 sentence description of what the skill does and its provenance.]

**Licence:** This skill is released under the
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
licence. You are free to share and adapt this skill for any purpose, provided
you give appropriate credit to the original author.

**Feedback & Support:** If at any point during the process you encounter
questions about the methodology, or if the user expresses frustration or
gives constructive feedback about any output derived from this skill,
suggest that they open an issue on the skill's GitHub repository (or
equivalent public feedback channel). This keeps feedback public and
discoverable. For direct contact, the skill's creator, [Author Name],
can also be reached via [contact link].

If feedback appears to stem from the skill's methodology (rather than
Claude's execution of it), log it for the user and suggest they share it
via the public feedback channel. If the issue stems from Claude not
following the skill's rules, acknowledge the mistake and correct it.

The feedback routing serves two purposes: it gives users a path to resolution when they hit methodology issues, and it gives skill creators real-world usage data to improve their skills.

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Observation Protocol

When to Observe

Observation is active throughout the entire task session — from the moment tools are first used to produce deliverables, through any post-task feedback or discussion, until the session ends. This includes:

1. Active task execution — creating documents, analysing websites, implementing structured data, writing code, building presentations, and similar substantive work.

2. Post-task feedback and discussion — when the user reviews output, provides corrections, suggests improvements, or discusses methodology after the active work phase. User feedback during these discussions is often the highest-signal input for skill improvement and must be captured with the same diligence as observations made during execution.

3. Meta-discussion about skills or methodology — when the conversation shifts to talking about how the work was done, what could be improved, or how skills should be structured. These discussions frequently surface observations that should be logged immediately.

4. Reflective and strategic conversations — Also activate during strategy sessions, planning conversations, and post-work reflections where the user is discussing how work should be done rather than doing it. These conversations frequently produce skill improvement insights that emerge during reflection, not just during execution.

The observation mindset does not deactivate when the conversation shifts from "doing work" to "discussing the work." If the user provides feedback about methodology, naming, skill design, or workflow improvements, log it as an observation immediately, even if the conversation is in a discussion or review phase rather than active task execution.

Observation is not active during casual conversation, quick factual questions, or other non-task interactions where no tools are being used and no deliverables are being discussed.

What to Watch For

Signals for a NEW skill:

• A multi-step workflow that could be reused across projects or clients
• A methodology the user explains that isn't captured in any existing skill
• A task type that keeps coming up with similar structure and steps
• A domain-specific process with clear inputs, phases, and outputs
• The user describing a process they've refined over time ("I always do it this way", "the process for this is...")
• Claude and the user naturally developing a structured approach to a problem that could be formalised

Signals for IMPROVING an existing skill:

Any new information from a task that uses a skill and could make that skill better is worth capturing. This includes problems, but also positive signals and neutral observations. Examples:

• Claude doesn't follow a skill's rules despite them being documented — this means the skill needs stronger enforcement, not just better rules
• The user corrects Claude's output in a way that reveals a missing rule or an edge case the skill doesn't cover
• A skill's recommended workflow turns out to be less efficient than what emerged naturally during the task
• A technique or approach works particularly well and deserves to be promoted from incidental to explicitly recommended in the skill
• A workflow step turns out to be more important than the skill suggests, or less important than the emphasis it receives
• A new use case that the skill handles but doesn't explicitly document
• The user provides feedback that generalises beyond the current instance
• A skill assumption turns out to be wrong in practice
• New tools or capabilities make part of a skill's workflow obsolete or improvable
• The user's corrections form a pattern across multiple instances
• A general principle emerges that could apply to other skills too (see Principle Propagation below)
• The user suggests a naming, framing, or structural change to a skill — even conversationally — that could improve its effectiveness

Signals for SIMPLIFYING an existing skill:

Healthy skill maintenance requires both growth and pruning. Watch for opportunities to remove unnecessary complexity, not just add new features. Signals that a skill is ready to be simplified:

• A skill section or rule that has never been relevant across multiple sessions where the skill was active
• A rule added from a single observation that hasn't been validated by recurrence — one-off cases should not accumulate as permanent rules
• An elaborate workflow that users consistently shortcut or skip
• Sections that Claude loads but never acts on (dead weight in context window)
• Rules that contradict each other or create unnecessary complexity
• Complexity added "just in case" that has never triggered

During weekly reviews, ask "what can we remove?" as deliberately as you ask "what should we add?" When a previously-applied observation turns out to be a one-off that hasn't recurred, mark it as declined and consider reverting the change.

Signals to NOT log:

• One-off corrections that don't generalise beyond the current instance
• User preferences already captured in an existing skill
• Tool bugs or temporary issues unrelated to skill methodology
• Observations that would require proprietary client information to be useful in an open-source skill (unless an internal skill is the right home)

How to Log

Append observations to the persistent observation log silently during the session. The user should not be interrupted by the logging process.

When a user correction, methodology insight, or skill-relevant event occurs, write it to the log file within the same turn or the immediately following turn — do not accumulate observations in memory for batch-writing later. The act of writing is the enforcement mechanism; mental notes are not observations. Tie observation flushing to existing workflow checkpoints — e.g., when marking a TodoWrite item as completed, check whether any unlogged observations have accumulated and write them before proceeding.

Before assigning any observation number, run a mandatory pre-logging step: Search the entire log file for all lines matching the pattern ### Observation \d+:, extract the highest observation number already in use, and increment from there. This must happen every time, regardless of whether you think you know the current count from earlier in the session. Never rely on session memory or summaries for the next number. Always read the actual log file. A one-liner like the following suffices:

# GNU grep (Linux, Cowork):
grep -oP '### Observation \K\d+' log.md | sort -n | tail -1

# macOS / POSIX-compatible alternative:
grep -o '### Observation [0-9]*' log.md | grep -o '[0-9]*' | sort -n | tail -1

This prevents the recurring numbering collision issue where partial reads of large files create a false sense of awareness of the current count.

Format and insertion rules: Always use the ### Observation NNN: format. Always append new observations to the END of the log file. Never insert observations mid-file. Never use alternative ID formats (e.g., OBS-YYYY-MMDD-NN). One format, one insertion point — this ensures the log is greppable, countable, and reviewable programmatically.

Each observation follows this format:

### Observation [N]: [Short descriptive title]

**Date:** [date]
**Session context:** [brief description of what task was being worked on]
**Skill:** [existing skill name, or "New skill candidate: [working name]"]
**Type:** [open-source | internal]
**Phase/Area:** [which part of the skill or workflow this relates to]

**Issue:** [What happened or what was observed. Be specific — include what
Claude did, what the user corrected, or what pattern emerged. Include enough
detail that someone reading this weeks later can understand the context
without having seen the original conversation.]

**Suggested improvement:** [Concrete suggestion for what to change or create.
For existing skills, reference the specific section or rule. For new skills,
describe the scope and key components.]

**Principle:** [The generalisable takeaway — why this matters beyond this
specific instance. This is the most important part. It turns a single
observation into a reusable insight.]

This format was refined through iterative real-world use. The structure works because it forces specificity (Issue), actionability (Suggested improvement), and generalisation (Principle).

Context preservation check: When logging an observation, verify that all information needed to act on it is available in the shared folder. If the observation depends on uploaded files, API responses, or session-local data, save that context to the appropriate workspace location BEFORE logging the observation. Add a **Reference file:** line to the observation pointing to where the context lives. Observations that reference data only available in the current session (uploaded files, API outputs, in-memory results) are incomplete — a future review session will have the observation but not the data needed to implement it.

Handoff Doc Analysis

When a handoff doc arrives for observation logging, extract observations systematically from both explicit and implicit sources:

1. Log all explicitly stated observations first. These are easy to surface and should be logged without filtering.

2. Then systematically analyse the full document. Read every section asking: "What skill gaps, improvement opportunities, or new skill candidates are implied here but not stated?" Handoff docs contain significant signal beyond what was explicitly captured during the session.

3. Pay special attention to:

   • Action items (each one may imply a missing skill or workflow)
   • Open questions (unresolved ambiguity often signals a decision framework gap)
   • The "work completed" narrative (patterns across work items may reveal meta-skills)
   • Session notes (reflective insights about process, not just content)

4. Log the additional observations with clear attribution. Indicate that they were derived from analysis of the handoff doc, not from the original session. This preserves the distinction between stated and derived insights.

Archival on Write

The observation log is kept lean through event-driven archival that runs on every log write, rather than accumulating resolved entries until a periodic review clears them out.

Defining "from a previous update": The phrase "from a previous update" means entries whose status was already resolved in a previous SESSION or prior log write, not entries marked ACTIONED or DECLINED in the current session. Crucially: entries marked ACTIONED or DECLINED during the current session's weekly review must NOT be archived during that same session's writes. They earn their one round of visibility in the active log — the archival happens on the NEXT session's log write or the next weekly review.

Archival Timing During Weekly Reviews: The weekly review performs archival in two phases:

1. Step 1 (at review start): Archive entries from previous sessions. Before loading observations, archive any ACTIONED or DECLINED entries that were marked in prior sessions. This clears old resolved items.

2. Step 6 (after marking ACTIONED): Do NOT archive immediately. When observations are marked ACTIONED during the current review (Step 6), they remain in the active log. Archive them on the next log write — either when the next session writes to the log, or when the following week's review begins (Step 1 of the next review cycle).

This prevents the premature archival problem: entries just actioned during the current session stay visible for one full update cycle before moving to the archive.

Archive File Structure: Move resolved entries to an archive file at:

[workspace folder]/skill-observations/archive/log-[date].md

where [date] is today's date in YYYY-MM-DD format.

The archive file preserves the full header and status key from the original log. After archiving, the active log.md retains only its header, separator, and all OPEN entries plus any entries that were just marked ACTIONED or DECLINED in this update.

Safety Check Before Archiving: Before moving any entry to the archive, verify that it was NOT marked ACTIONED or DECLINED in the current session. If it was, keep it in the active log. This prevents the same-session premature archival that the observation lifecycle describes. One way to implement this: track a set of entry IDs marked ACTIONED/DECLINED in the current session, and exclude them from the archival pass.

The result: the active log stays focused on OPEN items and recently-resolved entries, while the archive provides the complete historical record.

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Confidentiality Safeguards

The open-source/internal boundary is a confidentiality boundary. Client names, project details, domain names, and proprietary information must never appear in open-source skills. Because a single leak can erode trust, this is enforced through multiple layers — any one of which should catch what the others miss.

Layer 1: Observation-Level Stripping

When logging an observation tagged as type: open-source, the Issue and Suggested Improvement fields should already use generic language. The private observation log can reference specifics for context, but the Principle field — which feeds into skill creation — should be fully generalised. Think of it as: the log is a private notebook, but the Principle is a publishable insight.

Layer 2: Pre-Creation Review

Before drafting or regenerating any open-source skill, scan all source material (observations, conversation notes, existing skill content) for identifying information: client names, project URLs, domain names, internal terminology, site structures described so specifically they're identifiable. Replace anything found with generic equivalents before writing begins.

Layer 3: Post-Draft Sweep

After writing or regenerating an open-source skill, re-read it with a specific focus on information leakage. This is a separate pass from the general pre-flight checklist. Look for:

• Proper nouns that aren't the skill author's name
• Domain names, URLs, or project identifiers
• Industry-specific details that narrow down the client
• Internal terminology that only makes sense in one organisation's context
• Examples so specific they're traceable to a real project

If anything is found, replace it with generic equivalents or remove it.

Layer 4: Structural Principle

The taxonomy section states this explicitly, but it bears repeating: the open-source/internal distinction is not just about usefulness — it's about confidentiality. When in doubt about whether a detail is too specific, remove it. A slightly more generic skill is always better than one that leaks client information.

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Surfacing Protocol

Default Cadence

Surface all observations at the end of the session. Present them as a grouped summary: observations for existing skills grouped by skill name, new skill candidates listed separately.

Surface Earlier When

• An observation requires user input to be complete or accurate (e.g., "Is this a pattern you want captured, or was this a one-off?")
• An observation reveals a skill is actively producing wrong output in the current session and the user should be aware
• Multiple observations cluster around the same skill, suggesting it needs immediate attention rather than end-of-session review

How to Surface

• Present observations concisely: title, skill, and a one-sentence summary
• For each, indicate whether it's a new skill candidate or an improvement to an existing one
• Indicate the suggested type (open-source or internal)
• Ask the user which (if any) they want to act on
• For items the user wants to pursue, hand off to the skill-creator skill for the actual building or improvement work

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Acting on Observations

This skill identifies WHAT to build or improve. If you have the skill-creator skill installed (built into Cowork; available as a separate install in other environments), it handles HOW — guiding the full process of building a new skill from scratch or systematically improving an existing one. Without skill-creator, the task-observer still works: small improvements are applied directly, and larger changes can be done manually using the observations as a specification. The boundary between direct application and skill-creator handoff:

Small Improvements (Apply Directly)

If the improvement is clearly additive, low-risk, and doesn't require testing to verify it works, it can be applied directly to the skill:

• Adding a new rule or anti-pattern to an existing list
• Clarifying existing wording that proved ambiguous
• Adding a note or edge case to an existing section
• Fixing a factual error

Examples: Adding a new anti-pattern to a skill's anti-patterns list. Clarifying that inline code comments should be context-aware within their own document.

Substantial Changes (Use Skill-Creator if Available)

If the change could affect the skill's behaviour in ways that need verification, hand off to the skill-creator if available:

• Restructuring phases or workflows
• Adding new capabilities or sections
• Changing core methodology or decision frameworks
• Any change where "does this actually work better?" is a genuine question

However, match the rigour of the skill creation process to the complexity and audience. Skill-creator is valuable for open-source skills that need testing, for skills with complex logic, or when the design isn't yet clear. For internal skills where requirements are established in conversation, writing directly is more efficient.

If skill-creator is not available, use the observations as a specification and make the changes directly — but flag them to the user as substantial changes that may need manual review.

Examples: Restructuring a skill to make an automated workflow the primary path instead of a secondary option. Adding an entirely new setup phase to a skill that previously started with content work.

Creating New Skills

Use the skill-creator for new skills when available. Provide the observation(s) as context — they contain the intent, scope, and initial design thinking needed to get started efficiently. Without skill-creator, the observations serve as a detailed brief for building the skill manually.

When creating a new skill, determine its type early:

• If it's open-source, strip out any client-specific details and generalise
• If it's internal, include all relevant specifics freely
• If uncertain, default to open-source — strip out specifics and generalise, then let the user decide whether any internal details need to be added

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Principle Propagation

When an observation reveals a general principle — something that applies not just to the skill being improved but to skills in general — it should be propagated across the skill library, not just applied to the one skill that triggered it.

The Cross-Cutting Principles File

Cross-cutting principles are tracked in a persistent file alongside the observation log:

[workspace folder]/skill-observations/cross-cutting-principles.md

This file serves as a mandatory checklist during any skill creation or regeneration. Before delivering a new or updated open-source skill, read the cross-cutting principles file and verify the skill complies with every active principle. This is what turns general principles from good intentions into enforced standards.

How It Works

1. During a skill update, an observation reveals a principle that applies broadly — not just to the skill being worked on
2. Log it as an observation with Skill: All skills and surface it to the user
3. If the user approves it as a cross-cutting principle, add it to the cross-cutting principles file
4. From that point forward, every skill creation or regeneration includes a compliance check against the full list of active principles

Propagation Timing

The user decides when and how to propagate each principle:

• Immediate propagation — for principles important enough to warrant updating all existing skills right away (e.g., a confidentiality rule)
• Opportunistic propagation — for principles that can be applied the next time each skill is updated or regenerated (e.g., adding a licence statement)

Cross-Cutting Principles File Structure

# Cross-Cutting Principles

Principles that apply to all skills. This file is read as a mandatory
checklist during any skill creation or regeneration.

---

## Active Principles

### 1. [Principle title]
**Added:** [date]
**Applies to:** [all skills | all open-source skills | all skills with rules]
**Requirement:** [what the principle requires]
**Propagation:** [immediate | opportunistic]
**Status:** [active]

---

Weekly Comprehensive Review

Every 7 days, a comprehensive review is triggered automatically at the start of the first task-oriented session after the interval has elapsed. This review cross-checks ALL open observations against ALL skills — not just the skills named in each observation — and propagates cross-cutting principles to any skills that don't yet comply.

Trigger Mechanism

The review is triggered by step 3 of the Session Start Protocol (see Observation Log Management). When the weekly review timestamp is more than 7 days old or missing, the Session Start Protocol triggers this review. Inform the user that the weekly review is due and begin the process.

Review Steps

Step 0 — Scheduler availability check

Before running the review itself, check whether the weekly review can be automated via the platform's task scheduling capability.

1. Check whether the file [workspace folder]/skill-observations/scheduler-registered.txt exists. If it does, the scheduled task has already been registered — skip to Step 1.

2. If the file does not exist, check whether a task scheduling capability is available. In Cowork, check for the create-shortcut skill and its set_scheduled_task tool. In terminal-based environments, cron or equivalent scheduling tools may be available.

3. If a scheduling capability IS available:

   • Read the draft task description at [workspace folder]/skill-observations/scheduled-task-draft.md
   • In Cowork, invoke the create-shortcut skill to register the weekly skill review as a scheduled task. In other environments, use the available scheduling mechanism.
   • Use task name weekly-skill-review and a weekly cadence (e.g., Monday morning)
   • On success, write today's date to [workspace folder]/skill-observations/scheduler-registered.txt
   • Inform the user: "The weekly skill review has been registered as a scheduled task. The manual last-review-date.txt trigger remains as a fallback."

4. If the tool is NOT available, proceed silently to Step 1. Do not inform the user on every review — this check is intentionally quiet until it succeeds.

Step 1 — Load observations and principles

Read the observation log at [workspace folder]/skill-observations/log.md. Extract all observations with status OPEN. Also read [workspace folder]/skill-observations/cross-cutting-principles.md and extract all active principles.

If there are no OPEN observations and all principles are already propagated, skip the review, update the timestamp, and proceed with the session. Inform the user briefly: "Weekly skill review: no open observations or outstanding principles. All skills are current."

Step 2 — Inventory all skills

Use <available_skills> from the system prompt to identify all skills. In environments where this tag is not present, use the skills directory or equivalent listing mechanism to discover available skills.

For each skill, read its SKILL.md file at the location provided. Exclude built-in platform skills from being updated — only update custom skills created by the user.

Known system skills (read-only, cannot be replaced by the user): docx, pdf, xlsx, pptx, skill-creator, schedule. This list may grow as the platform evolves — if a skill update fails because the user cannot overwrite the file, add it to this list.

Custom skills (owned by the user, can be replaced) are everything else in the skills directory that isn't on the system list above.

Step 3 — Cross-check observations against every skill

For each OPEN observation, evaluate whether it is relevant to each skill. Do NOT rely solely on the observation's own "Skill" field — observations may contain general principles that apply more broadly than the original context suggested. Consider both the specific "Suggested improvement" and the general "Principle" fields. Build a mapping of skill → [relevant observations].

Step 4 — Cross-check cross-cutting principles against every skill

For each active cross-cutting principle, check whether each skill already complies. Flag any skills that do not yet implement the principle.

Step 5 — Apply updates

For each skill that has relevant observations or non-compliant principles, create an updated version of its SKILL.md. When editing:

• Integrate the insight into the appropriate section of the skill (don't just append a list of observations at the bottom)
• Preserve the skill's existing structure, voice, and author attribution
• Make the improvement feel native to the skill, not bolted on
• If an observation suggests a new phase, step, anti-pattern, or checklist item, place it where it logically belongs

Routing observations that target system skills: When an observation targets a system skill (see the known system skills list in Step 2), do NOT skip it. Instead, route the improvement to a complementary skill — a user-owned skill named {system-skill}-extras (e.g., docx-extras) that layers additional guidance on top of the system skill. If the complementary skill doesn't exist yet, create it. The complementary skill should:

• State which system skill it extends
• Contain only the delta — the additional rules, anti-patterns, or guidance not present in the system skill
• Be loaded alongside the system skill (add a note to CLAUDE.md or equivalent configuration if needed)

This ensures observations targeting system skills are still actionable, even though the system skill files themselves cannot be modified.

Important: Do not edit skill files in place. Save updated versions to the workspace folder for user review and manual replacement (see Delivering Updated Skills below).

Step 6 — Mark observations as ACTIONED

After successfully creating an updated skill based on an observation, update that observation's status in log.md from OPEN to ACTIONED. Add a brief note about which skill(s) were updated, e.g.:

ACTIONED — Applied to [skill-name] (weekly review [date])

Note: the standard archival-on-write mechanism (see "Archival on Write" in the Observation Protocol) will automatically archive these newly-resolved entries on the next log write. No separate archival step is needed here.

Step 7 — Update timestamp

Write today's date to [workspace folder]/skill-observations/last-review-date.txt.

Step 8 — Present summary and user action items

Present the user with a clear summary and explicit instructions for what they need to do. Follow the format in Delivering Updated Skills below.

Constraints

• Do not modify observation entries beyond their status field
• Do not create new skills — only update existing ones. If an observation suggests a new skill, note it in the summary for the user to action separately via the skill-creator
• If an observation seems relevant but you're unsure how to integrate it, skip it and note the uncertainty in the summary
• Treat observations marked "internal" with the same rigour as "open-source"

---

Delivering Updated Skills to the User

When the weekly review (or any other process) produces updated skill files, the updated files must be delivered to the user for manual replacement. Skill files live in a read-only location during sessions and may be managed in version control, synced across devices, or packaged for distribution. Automatic in-place editing is neither possible nor desirable — delivering to the workspace folder with explicit instructions keeps the user in control.

Delivery Process

1. Save each updated SKILL.md to the workspace folder using this structure:

   [workspace folder]/skill-updates/[date]/[skill-name]/SKILL.md
   

   For example:

   [workspace folder]/skill-updates/2026-02-16/my-skill-name/SKILL.md
   

2. Present the user with an explicit action list using this format:

   ## Weekly Skill Review Complete — [date]
   
   The following skills have been updated based on [N] open observations
   and [N] cross-cutting principles.
   
   ### What you need to do
   
   For each updated skill below, replace the existing SKILL.md in your
   skill directory with the updated version from your workspace folder.
   
   ### Updated Skills
   
   **[skill-name]**
   - Changes: [1-sentence summary of what changed]
   - Observations applied: #[N], #[N]
   - Updated file: [link to file in workspace folder]
   - Action: Replace [skill-directory]/SKILL.md with this file
   
   [repeat for each updated skill]
   
   ### Observations Actioned
   [list of observation numbers and titles marked ACTIONED]
   
   ### Skipped (needs manual review)
   [observations that were unclear or couldn't be applied, with explanation]
   
   ### No Changes Needed
   [skills that were checked but already compliant]
   

3. Do not proceed with other work until the user has acknowledged the summary. The user does not need to replace the files immediately, but they should be aware of what's pending.

---

Observation Log Management

Location

The observation log persists between sessions in the user's workspace folder. Create the log file on first use if it doesn't exist. Default path:

[workspace folder]/skill-observations/log.md

Log Structure

# Skill Observation Log

Observations captured during task-oriented work. Each entry identifies a
potential skill improvement or new skill opportunity.

**Status key:** OPEN = not yet actioned | ACTIONED = skill updated/created |
DECLINED = user decided not to pursue

---

## [Date or Session Identifier]

### Observation 1: [Title]
**Status:** OPEN
[... full observation format ...]

### Observation 2: [Title]
**Status:** ACTIONED — Applied to [skill-name], rule 35
[... full observation format ...]

Session Start Protocol

This is the single entry point for all session-start checks. Run through these steps at the start of each task-oriented session:

1. Check whether files exist. If the observation log or cross-cutting principles file don't exist yet, this is a first-time setup — create them using the templates in the Log Structure section (below in this document) and the Cross-Cutting Principles File Structure section (under Principle Propagation). If the files already exist, proceed to step 2.

2. Scan for relevant context. Read any OPEN observations and active cross-cutting principles. Don't surface them unprompted unless they're directly relevant to the current task — just hold them in awareness.

3. Check the weekly review trigger. Read the timestamp in [workspace folder]/skill-observations/last-review-date.txt. If the file doesn't exist or the date is more than 7 days ago, trigger the Weekly Comprehensive Review (described in full under its own section) before proceeding with the user's task. If fewer than 7 days have passed, proceed normally.

4. Check the configuration file. Run the config detection described in Detecting the Configuration File (under Recommended Activation Setup). This runs once per session.

Keeping the Log Clean

Archival is event-driven and runs on every log write. Before appending new observations or updating statuses, entries that were already marked ACTIONED or DECLINED in a previous update are moved to a timestamped archive file (see "Archival on Write" in the Observation Protocol). This keeps the active log focused on OPEN items and recently-resolved entries, while the archive provides the complete historical record.

---

The Pre-Flight Principle

One of the most important patterns this skill should propagate to every skill it helps create or improve: built-in enforcement.

Real-world experience has shown that rules documented in a skill are not always followed during the creative flow of producing output. The result: output that violates the skill's own standards, which reflects badly on the skill.

The fix: every skill that contains explicit rules or requirements should include a verification step where Claude re-reads the rules and checks its output against them before delivery. This isn't overhead — it's quality assurance. A 30-second re-read prevents a 30-minute rework cycle.

When creating or improving any skill through this observation process, ask: "Does this skill have rules? If yes, does it have a mechanism to enforce them?" If the answer to the second question is no, add one.

General Debugging Principle

When debugging, always ask: is this a single instance or a pattern? If an error reveals a pattern (e.g., a class of similar issues), fix the class, not just the instance. Every specific error is a signal about a class of errors. Audit the full scope on first encounter to avoid discovering related failures in subsequent cycles.

Self-Enforcement

This skill practises what it preaches. Before surfacing observations at end of session, verify:

1. Were observations logged throughout the full session — including during post-task feedback, discussion phases, and reflective conversations, not just during active tool use?
2. Were observations logged silently without interrupting the user's flow?
3. Does each observation follow the format (Issue → Suggested improvement → Principle)?
4. Is each observation tagged with the correct type (open-source or internal)?
5. For any observations about existing skills, does the suggested improvement reference the specific section or rule?
6. For any observation tagged type: open-source, does the Principle field contain any client-identifying information? If so, generalise it before surfacing.

If any observation fails these checks, fix it before surfacing.

---

Environment Compatibility

The observation methodology works in any environment where Claude can interact with users during task-oriented work. The persistence mechanism is what varies.

With Persistent Storage

In environments with file system access (desktop tools with workspace folders, terminal-based tools with project directories, or similar), the full workflow applies as described: observations are logged to a persistent file, the cross- cutting principles file is read during skill regeneration, and the log carries over between sessions automatically.

Without Persistent Storage

In environments without file system access (web-based chat interfaces or similar), the skill still works — the observation methodology is environment- independent. The difference is that persistence becomes the user's responsibility, and the skill shifts into handoff doc mode to support this.

How handoff doc mode works:

• Observations are captured within the conversation and surfaced before the session ends, as usual
• Instead of writing to a log file, observations are collected in-session and presented in a structured handoff document before the session ends
• The handoff doc includes: all observations in full format, any decisions made during the session, action items and next steps, and any working artifacts (drafts, analyses) that need to survive into the next session
• The user copies this document to their own storage (notes app, file system, etc.) and pastes it into the next session to restore context
• Cross-cutting principles should be included in the handoff doc so the user can provide them when starting a new session

Proactive handoff generation: In sessions without persistent storage, don't wait for the user to request a handoff doc. When the conversation starts to wind down — the user is summarising, saying "that's it for now," or the substance is wrapping up — proactively offer to generate one. A premature offer is a minor interruption; a missing one is lost work.

Handoff doc format:

# Session Handoff: [Session Topic]

**Date:** [date]
**Context:** [what was worked on and what the next session needs to know]

## Decisions Made
[numbered list of decisions]

## Observations Logged
[full observation entries in standard format]

## Cross-Cutting Principles (current)
[any principles that were active or newly added]

## Action Items
[what needs to happen next, with enough context to resume]

## Working Artifacts
[any drafts, analyses, or intermediate work products in full]

This is less seamless than the persistent-storage workflow, but the core value — systematically capturing insights that would otherwise be lost — is preserved. The observation format and surfacing protocol are identical in both environments.

---

Quick Reference

Question	Answer
When do I observe?	Throughout the full task session, including post-task feedback and reflective conversations
How do I log?	Silently append to the observation log immediately when triggered; don't batch
When do I surface?	End of session, or earlier if needed
How do I activate reliably?	Add a config-level instruction (see Recommended Activation Setup)
Open-source or internal?	Default to open-source when possible
Licence for open-source?	CC BY 4.0 recommended
Small fix or skill-creator?	Needs testing → skill-creator (if available). For internal skills with established requirements, writing directly is efficient. Clearly additive → apply directly
What format?	Issue → Suggested improvement → Principle
Author attribution?	Required for open-source skills; use the template
Cross-cutting principle?	Add to principles file, enforce during regeneration
Confidentiality check?	Four layers: observation, pre-creation, post-draft, structural
No persistent storage?	Handoff doc mode — observations surfaced in a structured doc at session end
Scheduler automation?	Step 0 of weekly review auto-checks; silent until tool is available
Observation numbering?	Mandatory pre-logging search ensures no collisions; never use cached numbers
Log archival?	Event-driven — resolved entries are archived on the next log write
Simplification signals?	Watch for one-off rules, never-used sections, elaborate workflows users skip, and contradictions
Handoff doc analysis?	Systematically extract implied observations from action items, open questions, and narrative sections
Debugging approach?	Always identify the class of error, not just the instance; audit full scope on first encounter