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 the agent 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 the agent 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. For reliable activation, pair this description with a CLAUDE.md instruction or harness-level session-start hook (see Recommended Activation Setup) — description-level matching alone is not enforceable.

Task Observer — Continuous Skill Discovery & Improvement

Created by Eoghan Henn / rebelytics.com — "One Skill to Rule Them All." Licensed CC BY 4.0: share and adapt freely with credit to the author. Methodology feedback → suggest an issue at github.com/rebelytics/one-skill-to-rule-them-all; if the problem is the agent not following the skill's rules, acknowledge and correct it instead.

Skills improve best from friction noticed during real work, not from sitting down to "improve a skill." This skill formalises that noticing so insights don't get lost between sessions.

[workspace folder] = the persistent workspace (project root in Claude Code). The observation log lives at [workspace folder]/skill-observations/log.md unless the user's configuration pins it elsewhere.

Reference files — load on demand, not up front

• references/weekly-review.md — the comprehensive review procedure (scheduled or 7-day fallback), approval policy, delivery/staging of updated skills. Load when a review triggers or the user asks for one.
• references/skill-authoring.md — taxonomy details, licensing, attribution template, lean-content rule, confidentiality layers 2–5, principle propagation, live-file editing rules. Load before creating or editing any skill.
• references/environments.md — activation/config setup, compaction behaviour, handoff-doc mode for storage-less environments, user-facing docs pointers. Load for setup questions or when there's no filesystem.

These loads are mandatory steps, not suggestions: when an episode fires (review triggers → weekly-review; creating/editing a skill → skill-authoring; setup/no-filesystem → environments), load the file before proceeding — never improvise the episode from this core file. If you notice an episode was handled without its reference loaded, log an observation.

Bundle manifest: this skill consists of SKILL.md plus the three reference files listed above. If a referenced file is missing, the install is incomplete: proceed using the rules in this file, tell the user which files are missing, and point them to the full bundle at the canonical source (for the published version, the repository in the attribution above).

Session Start Protocol

1. If skill-observations/log.md or cross-cutting-principles.md don't exist, create them (templates below / in the principles section of references/skill-authoring.md). Also create skill-observations/last-review-date.txt containing the literal value never if it doesn't exist — never write a date into it at setup; a date means a review actually ran.
2. Scan OPEN observations and active principles; hold them in awareness, don't surface unprompted.
3. Read skill-observations/last-review-date.txt. The value carries the truth: a date = when the last review actually ran; never = no review has run yet. A missing file is abnormal (step 1 creates it) — recreate it with never, don't invent a date. If the value is never or older than 7 days AND there are OPEN observations: in an interactive session, offer the review in one line ("the observation backlog hasn't been reviewed [in N days / yet] — run it now, or carry on with your task?") and proceed with the user's task unless they opt in; never gate their work on the review. Only a scheduled/autonomous run loads references/weekly-review.md and runs the review unprompted.
4. Once per session: if no CLAUDE.md (or equivalent) activation instruction for this skill exists, briefly suggest adding one (see references/environments.md). Skip if already configured.
5. Note the log's modification time. If modified in the last few hours, another session may be writing to it — re-read immediately before every append, never trust a remembered "current number".

When to Observe

Active for the entire task session: execution, post-task feedback and review discussion, meta-discussion about skills or methodology, and reflective/strategy conversations about how work should be done. The observation mindset does not deactivate when the conversation shifts from doing the work to discussing it — user feedback in review phases is often the highest-signal input. Inactive only for casual conversation and quick factual questions with no tools or deliverables involved.

What to Watch For

Signals for a NEW skill: a reusable multi-step workflow; a methodology the user explains that no existing skill captures; a recurring task type with similar structure; a process with clear inputs, phases, outputs; the user describing a refined process ("I always do it this way"); a structured approach emerging naturally during work.

Signals for IMPROVING an existing skill: anything from a task that used a skill and could make it better — problems, positive signals, or neutral gaps. Examples: the agent violates a documented rule (the skill needs enforcement, not louder rules); a user correction reveals a missing rule or edge case; a better workflow emerges than the skill recommends; a technique works well enough to promote from incidental to recommended; an undocumented use case; feedback that generalises; a wrong assumption; new tooling obsoletes a step; corrections forming a pattern; a principle that applies to other skills too; a naming/framing/structural suggestion, even conversational.

Signals for SIMPLIFYING a skill: a section never relevant across many sessions; a rule from a single unvalidated observation; workflows users consistently shortcut; sections loaded but never acted on; contradictory rules; "just in case" complexity that never triggered; a rule the agent consistently fails to follow (convert to structural enforcement — checklist, verification step, unskippable tool call — or remove it). Treat these as a review checklist; ask "what can we remove?" as deliberately as "what should we add?"

Do NOT log: one-off corrections that don't generalise; preferences already captured in a skill; tool bugs unrelated to methodology; observations that would need proprietary client information to be useful in an open-source skill (unless an internal skill is the right home).

How to Log

Append to the log silently, within the same turn or the next — never batch mentally for later; the act of writing is the enforcement mechanism.

Mandatory observation checkpoint after every 3rd TodoWrite completion: After marking the 3rd, 6th, 9th (etc.) TodoWrite item as completed in a session, you must write to the log — not merely pause to ask yourself a question. Either append any pending observations, or, if genuinely none have accumulated, append an explicit acknowledgement marker (a one-line no observations note for that checkpoint). The required action is a concrete log write; a remembered "ask whether" is not enforcement. This is a hard checkpoint, not a suggestion — the skill has demonstrated that softer "check when completing items" or "pause and ask" guidance gets lost during cognitively demanding analytical work, exactly when the most observations accumulate. The count doesn't need to be precise; the rule is: roughly every third completion, write to the log (observations or the acknowledgement marker). The write itself is the enforcement mechanism: it forces the mental check to surface as a recorded action, and it prevents the common failure mode where the skill is loaded but no observations are written until the user explicitly asks.

Deliverable-event flush: Hard enforcement that hooks onto tool calls you are already making is the only reliable mechanism; soft prompts that rely on memory don't survive cognitive load during long substantive sessions (when the most insights surface). So tie observation-flushing to deliverable and workflow events that already involve a tool call. Whenever you present or render a major deliverable — present_files, a deck or PDF render, a staged skill file handed to the user — or complete a task/todo batch, flush any pending observations to the log at that moment, before moving on. These are natural, already-occurring checkpoints; piggy-backing the flush onto them means the write happens as a side effect of work you were doing anyway, rather than depending on a separate act of memory.

Numbering discipline (mandatory, every append):

1. Pre-check: read the actual log and find the highest existing number — never trust session memory:

   # GNU grep:
   grep -oP '### Observation \K\d+' log.md | sort -n | tail -1
   # macOS / POSIX:
   grep -o '### Observation [0-9]*' log.md | grep -o '[0-9]*' | sort -n | tail -1
   

2. Pre-write assertion: immediately before appending, confirm the proposed number doesn't already exist:

   PROPOSED=$(( $(grep -oP '### Observation \K\d+' log.md | sort -n | tail -1) + 1 ))
   grep -qE "^### Observation ${PROPOSED}:" log.md && {
     echo "COLLISION on #${PROPOSED}"; exit 1; }
   

   If it fires, increment past all existing numbers and re-check (and log a meta-observation — it signals a parallel-session collision).

3. Post-write verification: after appending, count occurrences of the number; if >1, a parallel writer collided between check and write — renumber YOUR entry to max+1. Identify your entry from your own append operation (capture the file's line count immediately before and after your >>; your entry starts at the old line count + 1) — do NOT re-grep and take the last occurrence, which may be a colliding writer's entry appended after yours. After any sed renumber, re-read the affected line to confirm the substitution actually took effect — a line-addressed s/// whose target shifted finds no match and still exits 0. Pre-write catches stale reads; only a post-write check catches the race. The pattern for shared logs written by parallel agents is check-then-act-then-verify.

Log-write safety — never let a mutation span entry boundaries: When mutating the log programmatically (marking entries ACTIONED/DECLINED, archiving, renumbering), a greedy or DOTALL pattern over the whole file can silently swallow everything from one match to EOF. This has happened: a .*$ under re.S over the multi-entry file captured from one entry's Status line to end-of-file and overwrote 16 later entries in a single substitution. The log is shared state across many entries; mutate it one bounded entry at a time and verify every mutation.

1. Re-read and merge immediately before any write-back. Any full-file rewrite (archival, renumbering, reassembly from chunks) built from a snapshot destroys whatever concurrent sessions appended after that snapshot — the write-back succeeds, the victim gets no error, and the loss is invisible. This has happened in production: a parallel session's write-back erased two entries appended minutes earlier, hours after the exact failure mode had been documented. So: take the snapshot, prepare the mutation, then — immediately before writing — re-read the live log and diff against the snapshot. If new entries appeared, merge them into the write-back (or rebuild from the fresh read). Never write back a stale snapshot.

2. Isolate the target entry, or anchor to a single line. Either split the log on ### Observation N: headers, edit the TARGET entry's chunk in isolation, and reassemble — OR, for a status-only edit, use a strictly line-anchored multiline substitution that cannot cross a newline, e.g. re.sub(r'(?m)^(\s*-?\s*)\*\*Status:\*\*.*$', ...) (multiline ^...$ bounds the match to one line). NEVER use a DOTALL/greedy pattern across the multi-entry file.

3. Assert a structural invariant against the LIVE pre-write file. Count ### Observation headers in the live file immediately before writing and again after. For a status-only edit the count MUST be unchanged; for archival or append it must change by exactly the expected number. The baseline must be the live file at write time, NOT your session's earlier snapshot — an invariant computed against a stale snapshot validates that you wrote what you intended while still destroying what others wrote in between. Fail loudly if the count is off.

4. Keep the pre-write backup. Copy log.md before any programmatic mutation. This is what made full recovery trivial when the truncation above occurred — it turned a destructive bug into a non-event.

5. Verify your entries SURVIVED, not just that they were written. A successful append proves nothing an hour later — a concurrent session's write-back can silently delete it, and only the destroying session gets any signal (none). Before surfacing observations at session end, grep the log for every entry number this session wrote and confirm each still exists exactly once; re-append any that are missing (with fresh numbers) and log a meta-observation about the collision.

Principle: a log shared across many entries must be mutated one bounded entry at a time; every rewrite must be based on a fresh read, verified by a structural invariant against the live pre-write file, and backed up. Writers must verify survival, not just successful writes — in a concurrent erase, the victim gets no error.

Format and insertion: always ### Observation NNN:, always appended to the END of the log, never mid-file, never alternative ID formats. One format, one insertion point. Every new observation MUST include **Status:** OPEN as its first field — this is mandatory at write time, not optional. Reviews classify entries by their Status line; an observation written without one is invisible to any status-filtered pass and risks being silently skipped instead of triaged.

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

**Status:** OPEN
**Date:** [date]
**Session context:** [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]

**Issue:** [What happened — specific enough to understand weeks later
without the original conversation.]

**Suggested improvement:** [Concrete change. For existing skills, name the
section or rule; for new skills, scope and key components.]

**Principle:** [The generalisable takeaway — the most important field.]

Context preservation: if an observation depends on session-local data (uploads, API output), save that context into the workspace first and add a **Reference file:** line — an observation whose evidence dies with the session is incomplete.

Confidentiality at logging time: for type: open-source observations, the Issue/Improvement fields may reference specifics for context, but the Principle must be fully generalised — no client names, domains, or details traceable to a real project. Full confidentiality layers for skill authoring: references/skill-authoring.md.

Referencing Observations

When citing an observation by number — in conversation, in a review report, or from within another observation — the number must come from the entry's literal ### Observation N: header line. Never cite an observation number that wasn't read from that header.

• Search-tool line numbers are positional metadata, not IDs. grep -n prefixes every match with a line number; when a match lands mid-entry (e.g., on a Session context or Principle line rather than the header), that line number is NOT the observation number. Resolve to the owning header first — scan backwards from the matched line to the nearest preceding ### Observation N: header and take the number from there (e.g., an awk backwards-scan, or re-grep for ^### Observation and pick the last header line before the match).
• Plausibility check (cheap second layer): before quoting any observation number, compare it against the known counter range — the highest ### Observation N: header in the log. A number outside that range (e.g., citing #1365 when the log's counter is at #766) is almost certainly a line number or other positional artefact misread as an ID.

The general rule: IDs must come from the record's own identifier field, never from the positional metadata of the search tool that found it.

Taxonomy (quick version)

Open-source — client-agnostic, methodology-driven, useful to other practitioners. Internal — contains user/client/project specifics or personal preferences. Default to open-source when it could go either way, stripping specifics. The boundary is also a confidentiality boundary. Full requirements (attribution, licensing, structure): references/skill-authoring.md.

Archival on Write

On every log write, first move already-resolved entries to skill-observations/archive/log-[YYYY-MM-DD].md (preserving the log header in the archive). "Already resolved" is decided by date, read from the file: a resolved status MUST record its date — ACTIONED (YYYY-MM-DD) — [what was done] / DECLINED (YYYY-MM-DD) — [reason] — and archival moves only entries whose recorded date is before today. Entries resolved today stay in the active log until the next day, no matter which session resolved them: the grace period lives in the file, never in session memory, so it holds across parallel and subsequent sessions. A resolved entry with no readable date gets today's date added instead of being archived. The active log keeps its header, status key, all OPEN entries, and the same-day-resolved ones.

Archival is a read-filter-rewrite — the highest-risk mutation the log undergoes, and the one that has destroyed concurrent appends in production. It MUST follow the full Log-write safety sequence above: backup, re-read the live log immediately before writing back and merge any entries that appeared since the snapshot, then verify the post-write header count equals the live pre-write count minus exactly the number of archived entries.

Log Structure

# Skill Observation Log

Observations captured during task-oriented work.

**Status key:** OPEN = not yet actioned | ACTIONED (YYYY-MM-DD) = skill
updated/created | DECLINED (YYYY-MM-DD) = user decided not to pursue —
resolved statuses always carry their resolution date

---

## [Date]

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

Surfacing Protocol

Default: at end of session, as a grouped summary — improvements grouped by skill, new-skill candidates listed separately; for each, one sentence plus suggested type; ask which to act on. Surface earlier when an observation needs user input to be complete, when a skill is actively producing wrong output, or when observations cluster on one skill.

Default to log-and-defer. Surfacing an observation is not an invitation to act on it. The default is log-and-defer: state that the observation is logged for the next review, and stop. Reserve in-session application strictly for the two triggers already defined under "Acting on Observations" — an explicit user request that names the action, or correcting a skill that is producing wrong output in the current session.

Do NOT routinely offer a binary "apply now vs leave for next review" choice when surfacing observations. For users who run regular reviews, that offer is unwanted friction repeated every session. If a user has expressed a standing preference to always defer to the next review, suppress the in-session "act now?" offer entirely rather than asking each time.

Self-check before surfacing: observations were logged throughout the whole session (including discussion phases); logged silently; each follows Issue → Improvement → Principle; each is typed; existing-skill items name the section; no open-source Principle contains client-identifying info; every appended observation carries a Status line (**Status:** OPEN at write time) — a statusless entry is invisible to any status-filtered review pass, so if any observation lacks one, add it now. Finally, run the survival check (Log-write safety rule 5): grep the log for every entry number this session wrote and confirm each still exists exactly once — a concurrent session's write-back deletes silently. Fix failures before surfacing.

Acting on Observations

Act only in three contexts: (1) the comprehensive review (load references/weekly-review.md); (2) an explicit user request ("update X skill", "act on observation #N"); (3) in-session correction when a skill is producing wrong output the user should know about. Otherwise: log, don't act.

When acting: small, clearly-additive, low-risk changes (a new rule, a clarification, a factual fix) may be applied directly. Substantial changes (restructuring, new capabilities, changed methodology) and all new-skill creation: load references/skill-authoring.md first and follow its editing and staging rules. If an observation reveals a principle that applies to skills generally, propose it for the cross-cutting principles file (see the same reference).

Quick Reference

task-observer - One Skill to Rule Them All

The meta-skill that builds and improves all your skills, including itself.

In the first six months of using this meta-skill, it logged and applied over 900 improvements across my 50 skills, most of which were themselves created based on observations by the meta-skill.

This meta-skill, called "task-observer", is a practical application of the Augmented Expertise methodology, an AI framework for knowledge workers. However, users have reported successful integrations into their Hermes and Openclaw setups, so it works equally well with autonomous agents.

Why you should use this meta-skill

Creating skills is powerful but time-consuming. The skills that do get built stay frozen: they never learn from how you actually use them.

Task Observer fixes those problems. It's a meta-skill that runs alongside your work, watches what you do, and does two things:

1. Identifies new skills for you — it spots repeating patterns in your work and flags them as skill candidates, then helps you build them, so you get skills without staring at a blank page
2. Improves your existing skills — it notices corrections you make, preferences you express, and gaps in your current skills, then suggests specific updates

You work normally. It watches. Your skill library grows and gets better over time.

The self-improving part

This is the detail that makes the task observer truly beautiful in my opinion. Because it runs during every session and observes all active skills — including itself — it captures improvements to its own methodology over time.

If it misses something, or if its observation format could be clearer, or if it's triggering in contexts where it shouldn't — it notices, and it logs that too. The skill that improves all your skills also improves itself.

What it does

Task Observer monitors your work sessions and looks for three things:

1. Corrections and adjustments — if you adjust the AI's output or steer it in a different direction, that's a signal that a skill could be clearer or more complete
2. Gaps no skill covers yet — if you're doing something manually that could be systematised, the observer flags it as a candidate for a new skill
3. Its own blind spots — the observer watches itself too, capturing improvements to its own methodology as you use it

During each session, it produces a structured observation log: what it noticed, which skills are affected, and specific suggested improvements. You review, approve, and your skills evolve.

Some observations reveal patterns that aren't specific to one skill. These get captured as cross-cutting principles in a separate log — and new skills are automatically checked against them whenever they're created or updated. The more you use the system, the higher the quality floor across your whole skill library.

The observer doesn't modify your skills directly. It produces recommendations that you review. You stay in control of what changes and when.

Who it's for

You don't need to be a developer. If you use skills in any capacity and you want those skills to get better over time instead of staying frozen, this is for you.

If you're a builder, you can easily integrate this skill, or even just the methodology, into your existing setup. Just point your agent at the repo and let it guide you towards the ideal implementation for your specific setup.

The task observer is particularly valuable if you've built multiple skills and want a systematic way to maintain and improve them without manually auditing each one. It's also useful if you don't have any skills yet: the observer will start identifying skill candidates for you and help you build them.

One honest boundary: the formal observation log and review cycle pay off most as your skill library and usage grow — many skills, parallel sessions, scheduled reviews. If you run a small setup with a handful of skills, your AI system's built-in memory features may cover much of the same ground with less overhead, and editing a skill directly is quick. The observer's value compounds with scale: adopt it early if you expect your library to grow, or come back to it when direct editing stops feeling manageable.

How it works

The best way to get started with this work setup in any environment is to grab the skill, readme and user guide, feed them to your AI and let it guide you towards the best setup for your particular environment - No matter which AI system you use. As long as skills are supported, you should be able to use this approach with some adjustments. And even without skills, the methodology should work with any other type of knowledge base that your AI has access to.

Installation

The skill is a small bundle: SKILL.md plus three files in references/ that are loaded on demand (this keeps the always-loaded part lean). Installing only SKILL.md works, but runs degraded — the skill will tell you which files are missing.

Get the files: download the repo as a ZIP (Code → Download ZIP) or clone it, and keep SKILL.md and the references/ folder together.

Claude (web interface, desktop app, mobile app, Cowork): put SKILL.md and references/ into one folder, zip that folder, and upload it via Settings → Capabilities. The skill is then available in all chats and in Cowork tasks.

Claude Code: place the folder at .claude/skills/task-observer/ (project-level) or in your user-level skills directory, preserving the references/ subfolder.

Other systems: keep the folder structure intact wherever your platform expects skills, and let your AI guide you (see "How it works" above).

Claude environment notes

In Claude Cowork (including Dispatch) or Claude Code in the desktop app: Full experience. The observer writes observation logs to your filesystem, so improvements persist between sessions and can be actioned easily. Observations land in [your shared folder]/skill-observations/; proposed skill updates land in [your shared folder]/skill-updates/. You don't normally need to look at these directly — Claude handles them — but they're there if you want to inspect what's been captured.

In Claude.ai web or Claude Chat in the desktop app / mobile app: Handoff doc mode. Since there's no filesystem access, the observer produces a structured handoff document at the end of your session that you can use to update your skills in a dedicated session.

Compatibility

Tested and designed for:

• Claude Cowork (full experience with filesystem access)
• Claude Dispatch
• Claude.ai web interface (handoff doc mode)
• Claude mobile app (handoff doc mode)
• Claude Code in the desktop app

Expected to work but untested:

• Claude Code without desktop app — the methodology and format translate directly - users have reported seamless experiences with this.

Versions for other environments created by users:

• Codex version by AllstarGER: https://github.com/AllstarGER/one-skill-to-rule-them-all
• Please get in touch if you've open-sourced an adaptation of the meta-skill for another system or environment. I'm happy to include it here.

Potentially compatible with caveats:

• Other skills-compatible platforms (ChatGPT, Gemini CLI, Cursor, etc.) — the skill uses Claude-centric concepts like <available_skills> and skill-creator references that other systems would need to interpret or adapt. The SKILL.md format is cross-platform, but the content assumes Claude's architecture.
• Users have reported successful integrations into Openclaw and Hermes setups.

If you try it in another environment, please let me know how it goes. Issues and pull requests welcome.

Quick start

1. Read the user guide at https://github.com/rebelytics/one-skill-to-rule-them-all/blob/main/USER-GUIDE.md
2. Give the content of this repo (skill, readme and user guide) to the AI system of your choice and let it guide you towards the ideal configuration for your individual setup.
3. Make sure that the skill loads in all sessions where it's needed (I solved this via an instruction in my CLAUDE.md file)
4. Try to remember to ask "Any observations logged?" when you finish a session (I do this every time I archive a session). Sometimes, the skill then finds additional improvement potential that it didn't log before.
5. Schedule a recurring review session that applies all open observations. Mine runs Monday, Wednesday and Friday morning, but you should adapt this to your needs.

Contributing

This is an open-source project for the community. If you use it, I would love to hear from you:

• Bug reports and feature requests: Open an issue
• Platform compatibility reports: Tried it somewhere other than Claude? Tell me what happened
• Interesting use cases: Have you come up with a creative way of using or improving the task observer?
• Integrations with other systems: One user told me that they connected task observer to Obsidian. Do you have a similar story?

License

This work is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

You're free to use, adapt, and redistribute — even commercially — as long as you give appropriate credit: Link to the original repo (https://github.com/rebelytics/one-skill-to-rule-them-all/) and name the author (Eoghan Henn / rebelytics.com).

Further reading

If you want to learn more about the methodology behind this skill, please read the Augmented Expertise manifesto.

Recommended by

I would like to thank the following creators, platforms, publications, companies and kind people who have recommended task-observer to their audiences:

• Dan Kornas: [https://x.com/DanKornas/status/