session-observer

session-observer is a skill for Claude Code from bharat3645/The-Ideal-Harness. It costs 54 tokens per session (646 once invoked), scanned A, original, MIT.

A session-monitoring skill that records corrections, repeated patterns, and accepted decisions as observations for later human review.

In plain words
What is it for?
Use it during substantial sessions to record reusable observations about mistakes, workarounds, corrections, and judgment calls.
Why use it?
It preserves useful lessons from a work session so recurring fixes and reasoning do not disappear when the session ends.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter. Also seen: mentions subagents.

Part of the ideal-harness plugin — 10 skills, 4 agents, 3 hooks, 5 MCP servers shipped together

Good fit Use it during substantial sessions to record reusable observations about mistakes, workarounds, corrections, and judgment calls.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/bharat3645/the-ideal-harness/session-observer
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add bharat3645/The-Ideal-Harness --skill session-observer
Clone the repo
git clone --depth 1 https://github.com/bharat3645/The-Ideal-Harness

Made for: Claude Code.

Or install ideal-harness, the plugin that ships this one along with the rest of its 10 skills, 4 agents, 3 hooks, 5 MCP servers.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for session-observer

README.md
[![agentmods](https://agentmods.dev/badge/skills/bharat3645/the-ideal-harness/session-observer/github.svg)](https://agentmods.dev/skills/bharat3645/the-ideal-harness/session-observer)
Your own site
<a href="https://agentmods.dev/skills/bharat3645/the-ideal-harness/session-observer"><img src="https://agentmods.dev/badge/skills/bharat3645/the-ideal-harness/session-observer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for session-observer

Your own site · 80×15
<a href="https://agentmods.dev/skills/bharat3645/the-ideal-harness/session-observer"><img src="https://agentmods.dev/badge/skills/bharat3645/the-ideal-harness/session-observer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 646 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00054 $0.00646
Opus 5 $0.00027 $0.00323
Sonnet 5 $0.00011 $0.00129
Haiku 4.5 $0.00005 $0.00065

Measured 9d ago against content hash 0d9963253b6c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

session-observer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/session-observer/SKILL.md · 52 lines

How it starts

The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Session Observer

guard learn already turns repeated Bash approvals into policy-allow proposals. This skill generalizes the same idea across the WHOLE session — not just tool approvals, but corrections, repeated workarounds, and "actually, do it this way" moments — so they become durable, reviewable proposals instead of evaporating with the context window.

What to capture

At natural checkpoints (a task finishes, a fix loop converges, the user corrects your approach), write ONE episodic observation via memory_write when you notice:

  • A correction. The user rejected an approach and stated why. Capture the why, not just the what — that's the reusable part.
  • A repeated pattern. The same workaround, the same clarifying question, the same shape of bug fix — twice is a coincidence, three times is a pattern worth a proposal.
  • A judgment call that worked. The user accepted an unusual choice without pushback. Silence after a nonstandard decision is a confirmation signal — capture it too, not only failures.

How to capture it

memory_write({ type: "decision", text: "<the pattern + why, in one or two sentences>", ts: <now> })
memory_write({ type: "failure", text: "<the approach that didn't work + why>", ts: <now> })

Use "decision" for a correction or judgment call (what to do, and why); use "failure" for an approach that was tried and rejected (so a later fresh-context subagent doesn't re-walk the same dead end). Both types are exempt from consolidation's prune-to-cap pass — they're kept in full, not summarized away, because the specifics are the value.

Keep it factual and specific enough that a future session (or a human reviewing the episodic store) can act on it without re-deriving the context. A vague "user prefers clean code" is useless; "user rejected mocked DB in integration tests — prior incident where mock/prod divergence masked a broken migration" is a proposal a human can ratify.

Boundary (this is observation, not authority)

Read the full file on GitHub · 52 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 52 lines · 54 tokens per session scan A 0d9963253b6c

Subscribe to this mod's changes

session-observer is a skill published in the GitHub repository bharat3645/The-Ideal-Harness (5 stars, last pushed yesterday), licensed MIT. It adds 54 tokens to every session and 646 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

ax-ai

This skill helps an LLM generate correct AI provider setup and configuration code using @ax-llm/ax. Use when the user asks about ai(), providers, models, routing, adaptive balancing, presets, embeddings, batch audio with ai.transcribe() or ai.speak(), extended thinking, context caching, or mentions…

ax-llm/ax · 100 tokens

ax-agent-rlm

This skill helps an LLM generate correct AxAgent RLM/runtime code using @ax-llm/ax. Use when the user asks about RLM code execution, AxJSRuntime, contextFields, contextPolicy, liveRuntimeState, promptLevel, stage prompt controls, executorModelPolicy, maxRuntimeChars, agent.test(...), llmQuery(...), recursionOptions…

ax-llm/ax · 88 tokens

connect-recommend

Use this skill when the user asks about Stripe Connect configuration, charge patterns, Dashboard access, or how to get started with Connect, is building a marketplace, platform, multi-vendor store, gig platform, or subscription platform, needs to pay out sellers, vendors, or providers, mentions split payments, revenue…

stripe/ai · 151 tokens

ax-agent-observability

This skill helps an LLM generate correct AxAgent observability code using @ax-llm/ax. Use when the user asks about axGlobals.onUsage, usageContext, centralized or multi-tenant usage accounting, actorTurnCallback, onContextEvent, agentStatusCallback, onFunctionCall, reportSuccess, reportFailure, getChatLog()…

ax-llm/ax · 95 tokens

ax-llm

This skill helps with using the @ax-llm/ax TypeScript library for building LLM applications. Use when the user asks about ax(), ai(), f(), s(), agent(), flow(), AxGen, AxAgent, AxFlow, signatures, streaming, or mentions @ax-llm/ax.

ax-llm/ax · 68 tokens

ax-go-flow

Use when writing Go code with github.com/ax-llm/ax/packages/go for flows, nodes, program graphs, nested programs, dynamic options, caching, and optimizer components.

ax-llm/ax · 43 tokens