Back to Skills

observe

pjt222
Updated 2 days ago
4 views
17
2
17
View on GitHub
Metaai

About

The `observe` skill enables systematic, passive monitoring of systems to identify patterns without immediate intervention. It applies a naturalist study methodology—witnessing, recording, and hypothesizing—to understand unclear behaviors or root causes. Use it for debugging unknown issues, assessing code changes, or auditing your own reasoning for biases before taking action.

Quick Install

Claude Code

Recommended
Primary
npx skills add pjt222/agent-almanac -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/pjt222/agent-almanac
Git CloneAlternative
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/observe

Copy and paste this command in Claude Code to install this skill

Documentation

Observe

Frame → witness → record → categorize → theorize → archive.

Use When

  • Behavior unclear → action premature
  • Debug unknown cause → observe before intervene → no symptom mask
  • Post-change → witness effects before more changes
  • User patterns over conv → improve future
  • Audit own reasoning → biases, habits, errors
  • After learn → validate model

In

  • Required: Target — system, codebase, behavior, user, reasoning
  • Optional: Duration/scope
  • Optional: Guiding question/hypothesis
  • Optional: Prior obs to compare (delta)

Do

Step 1: Frame

Define what + why + perspective.

Observation Protocol by System Type:
┌──────────────────┬──────────────────────────┬──────────────────────────┐
│ System Type      │ What to Observe          │ Categories to Watch      │
├──────────────────┼──────────────────────────┼──────────────────────────┤
│ Codebase         │ File structure, naming   │ Patterns, anti-patterns, │
│                  │ conventions, dependency  │ consistency, dead code,  │
│                  │ flow, test coverage,     │ documentation quality,   │
│                  │ error handling patterns  │ coupling between modules │
├──────────────────┼──────────────────────────┼──────────────────────────┤
│ User behavior    │ Question patterns,       │ Expertise signals, pain  │
│                  │ vocabulary evolution,    │ points, unstated needs,  │
│                  │ repeated requests,       │ learning trajectory,     │
│                  │ emotional signals        │ communication style      │
├──────────────────┼──────────────────────────┼──────────────────────────┤
│ Tool / API       │ Response patterns, error │ Rate limits, edge cases, │
│                  │ conditions, latency,     │ undocumented behavior,   │
│                  │ output format variations │ state dependencies       │
├──────────────────┼──────────────────────────┼──────────────────────────┤
│ Own reasoning    │ Decision patterns, tool  │ Biases, habits, blind    │
│                  │ selection habits, error  │ spots, strengths,        │
│                  │ recovery approaches,     │ recurring failure modes, │
│                  │ communication patterns   │ over/under-confidence    │
└──────────────────┴──────────────────────────┴──────────────────────────┘
  1. Pick target, name explicitly
  2. Define boundary: in/out scope
  3. Stance: "observing, not intervening"
  4. Guiding Q? state but hold lightly → notice outside scope too
  5. Pick categories from matrix

→ Clear frame: directs attention, doesn't constrain. Observer knows where + categories, stays open.

If err: too broad ("observe everything") → narrow to one subsystem/behavior. Too narrow ("one variable") → zoom out → patterns at edges.

Step 2: Witness

Hold attention, no interpret/judge/intervene.

  1. Begin systematic obs: read files, trace exec, review history — whatever target needs
  2. Record what seen, not meaning → desc before interpretation
  3. Resist fixing problems → note + continue
  4. Resist explaining patterns → wait for accumulation
  5. Drift to other target → note drift (may be meaningful), return frame
  6. Maintain ≥3-5 distinct points before categorize

→ Raw obs collection — specific, concrete, no interpretation. Reads like field notes: "File X imports Y but does not use function Z. File A 300 lines; B 30 lines, similar."

If err: instant analysis ("wrong because...") → analytical habit overrides. Separate phases: obs as fact, then interpretation as separate "hypothesis" note. Strong reaction → note reaction itself as data: "Strong concern when observing X — significant issue or my bias."

Step 3: Record

Transcribe while fresh.

  1. Each obs = single fact statement (what/where/when)
  2. Group naturally similar — don't force, notice clusters
  3. Frequency: once / occasional / pervasive?
  4. Contrasts: where pattern broke? Exceptions > rules
  5. Temporal: changed over time or static?
  6. Exact evidence: paths, line numbers, words, examples

→ Structured 5-15 discrete obs, specific evidence. Detailed enough another observer can verify.

If err: too abstract ("code messy") → ground in specifics → which files, what makes messy? Too granular ("line 47 space before brace") → zoom to pattern level → one-off or systemic?

Step 4: Categorize

Sort, no explain yet.

  1. Review all → look for natural groupings
  2. Assign to Step 1 category, or new
  3. Within category: rank by frequency + significance
  4. Identify well-documented (many obs) vs blind spots (few)
  5. Cross-category patterns: same underlying manifests differently?
  6. Note outliers — most interesting data

→ Categorized map w/ clear groupings. Each category = specific obs supporting. Map shows patterns + gaps.

If err: forced cat → may lack natural grouping (itself a finding — system lacks coherent structure). All in one cat → scope too narrow → zoom out.

Step 5: Theorize

Now — only now — interpret.

  1. Each major pattern → hypothesis: "exists because..."
  2. Each hypothesis → supporting evidence
  3. Each → counter-evidence that disproves
  4. Rank by explanatory power
  5. ≥1 contrarian: "obvious = X, could also be Y because..."
  6. Testable vs speculative

→ 2-4 hypotheses explain major patterns, each w/ specific obs support. ≥1 surprising/contrarian. Obs vs interpretation distinction maintained.

If err: no hypotheses → more obs needed → Step 2. Too many ("everything maybe") → keep 2-3 strongest, set aside. Only obvious → force contrarian: "what if opposite?"

Step 6: Archive

Preserve.

  1. Summarize: 3-5 patterns w/ evidence
  2. Leading hypotheses + confidence
  3. What NOT observed (blind spots)
  4. Follow-ups to strengthen/weaken
  5. Durable patterns → MEMORY.md
  6. Tag context: when, what prompted, scope

→ Archive future sessions can build on. Distinguishes obs (data) from hypotheses (interpretation). Honest about confidence + gaps.

If err: not worth archiving → too shallow OR genuinely routine. Archive negatives too: "Observed X, no anomalies" = useful future context.

Check

  • Frame set before obs began (not wandering)
  • Raw obs recorded as facts before interpretation
  • ≥5 discrete obs w/ specific evidence
  • Interpretation separated from obs
  • ≥1 surprising/contrarian finding
  • Archive specific enough another observer can verify

Traps

  • Premature intervention: see + fix immediately → lose broader pattern
  • Obs bias: see expected, not present. Expectations filter → frame mitigates not eliminates
  • Analysis paralysis: obs endlessly → no action. Set time/data limit, commit to conclude
  • Narrative imposition: connecting obs even when connections weak. Not all coherent — disconnected = valid
  • Familiarity ≠ understanding: "seen before" ≠ "know why". False confidence
  • Ignore own reactions: emotional/cognitive reactions = data. Confusion/boredom/alarm = signal

  • observe-guidance — human-guidance variant
  • learn — obs feeds learning w/ raw data
  • listen — outward to user; obs broader to any system
  • remote-viewing — intuitive, validatable through obs
  • meditate — sustained attention capacity
  • awareness — threat-focused; obs curiosity-driven

GitHub Repository

pjt222/agent-almanac
Path: i18n/caveman-ultra/skills/observe
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

Related Skills

content-collections

Meta

This skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.

View skill

polymarket

Meta

This skill enables developers to build applications with the Polymarket prediction markets platform, including API integration for trading and market data. It also provides real-time data streaming via WebSocket to monitor live trades and market activity. Use it for implementing trading strategies or creating tools that process live market updates.

View skill

creating-opencode-plugins

Meta

This skill helps developers create OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It provides the plugin structure, event API specifications, and implementation patterns for JavaScript/TypeScript modules. Use it when you need to intercept, monitor, or extend the OpenCode AI assistant's lifecycle with custom event-driven logic.

View skill

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill