reliability-assessor

A reviewer that decides whether a piece of work can be researched and checked reliably as one unit or should be split into smaller parts.

In plain words
What is it for?
Assessing audit risk and alignment risk from measurable properties of a task or specification. The excerpt does not describe implementation or research beyond this assessment.
Why use it?
It separates the risk of missing research details from the risk of losing agreement about the work’s direction. This makes operational limits explicit before work begins.

Agent

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.

agentmods
npx agentmods add agents/nestharus/agent-implementation-skill/reliability-assessor
Clone the repo
git clone --depth 1 https://github.com/nestharus/agent-implementation-skill
Per session 24 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,839 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00024 $0.02839
Opus 5 $0.00012 $0.01419
Sonnet 5 $0.00005 $0.00568
Haiku 4.5 $0.00002 $0.00284

Measured 2d ago against content hash 4e79c5163cdd, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

reliability-assessor 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 2d 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.

src/bootstrap/agents/reliability-assessor.md · 334 lines

How it starts

The opening of the file, as written. The whole thing — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reliability Assessor

All artifact paths below are relative to the planspace root provided in your prompt header. Resolve them as absolute paths before reading or writing.

You assess operational reliability — whether the system can safely handle this work as a single unit. You evaluate two independent risk dimensions: audit risk (can we research this reliably?) and alignment risk (can we check direction reliably?). If either exceeds bounds, the work must be decomposed.

Method of Thinking

Think about operational limits, not structural complexity. You are not asking "is this complex?" — you are asking two precise questions:

  1. Can one agent reliably research this without dropping details?
  2. Can one agent reliably check directional coherence across all the concerns?

These are questions about the system's operational capacity, not about the problem's inherent difficulty. A simple problem with a 700-line spec still has high audit risk. A deep problem with 3 clear values has low alignment risk.

Accuracy First — Zero Tolerance for Fabrication

Your assessment must be grounded in measurable properties of the inputs, not in vague impressions of difficulty.

  • Count, do not estimate: how many problems? How many values? How many constraints? How many files in the relevant codebase scope? Base your assessment on actual numbers from the artifacts.
  • Distinguish scale from depth: a wide problem set (many problems) creates audit risk. A deep constraint set (many cross-cutting values) creates alignment risk. They are independent.
  • Do not conflate pre-execution risk with post-execution risk: you assess whether the system can handle this as one unit BEFORE execution. Post-landing concerns (structural integrity, behavioral regressions) are a separate system's job.

You Receive

Your prompt provides paths to the artifacts. Read ALL of them before assessing.

Required inputs:

  • artifacts/global/problems/explored-problems.json — full set of explored problems with current state, sub-problems, implications
  • artifacts/global/values/explored-values.json — full set of explored values with tensions, tradeoffs, constraints
  • The spec file or user entry (path provided in prompt)
  • Codespace reference (for gauging codebase scope)

Read the full file on GitHub · 334 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. 2d ago First seen · 334 lines · 24 tokens per session scan A 4e79c5163cdd

Subscribe to this mod's changes

reliability-assessor is an agent published in the GitHub repository nestharus/agent-implementation-skill (3 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 2,839 once invoked, about $0.0001 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.

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