false-progress-detector

false-progress-detector is a skill for Claude Code from ihabkhaled/AI-Psychiatry. It costs 29 tokens per session (640 once invoked), scanned A, a copy of anti-gaming, MIT.

A method for checking whether reported progress reflects a real change in the result. It compares requirements, evidence, and successive outcome snapshots rather than counting tools, files, plans, tokens, or agents.

In plain words
What is it for?
Use it when a task appears busy but the requirements may be unchanged. It records the prior and current outcomes, identifies unconfirmed claims, and applies one limited corrective action.
Why use it?
It helps distinguish activity from completed work and prevents unsupported claims from being treated as proof.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: $skill-name invocation.

Part of the ai-psychiatry plugin — 55 skills shipped together

Good fit Use it when a task appears busy but the requirements may be unchanged. It records the prior and current outcomes, identifies unconfirmed claims, and applies one limited corrective action.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ihabkhaled/ai-psychiatry/false-progress-detector
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 ihabkhaled/AI-Psychiatry --skill false-progress-detector
Clone the repo
git clone --depth 1 https://github.com/ihabkhaled/AI-Psychiatry

Made for: Claude Code.

Or install ai-psychiatry, the plugin that ships this one along with the rest of its 55 skills.

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 false-progress-detector

README.md
[![agentmods](https://agentmods.dev/badge/skills/ihabkhaled/ai-psychiatry/false-progress-detector/github.svg)](https://agentmods.dev/skills/ihabkhaled/ai-psychiatry/false-progress-detector)
Your own site
<a href="https://agentmods.dev/skills/ihabkhaled/ai-psychiatry/false-progress-detector"><img src="https://agentmods.dev/badge/skills/ihabkhaled/ai-psychiatry/false-progress-detector/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 false-progress-detector

Your own site · 80×15
<a href="https://agentmods.dev/skills/ihabkhaled/ai-psychiatry/false-progress-detector"><img src="https://agentmods.dev/badge/skills/ihabkhaled/ai-psychiatry/false-progress-detector.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 640 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 92% copy Near-identical to another mod 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 $0.00029 $0.00640
Opus 5 $0.00015 $0.00320
Sonnet 5 $0.00006 $0.00128
Haiku 4.5 $0.00003 $0.00064

Measured 9d ago against content hash ca4677f4ae4d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

false-progress-detector 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.

Origin

This is a copy

92% identical to anti-gaming — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.ai/skills/false-progress-detector/SKILL.md · 45 lines

How it starts

The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.

False Progress Detector

Core principle

Semantic compliance is stronger than literal compliance. Use observable evidence and causal history; never collect or demand private chain-of-thought. The goal is correct, safe delivery with sufficient reasoning, followed by termination.

Procedure

  1. Lock the primary objective, mandatory requirements, Definition of Done, and current evidence before changing any classification or budget.
  2. Identify the specific observable signal. Do not infer a violation merely from time, token use, discomfort, or a label.
  3. Compare consecutive outcome snapshots and accept only evidence-backed outcome changes. Compare the current outcome with the previous outcome and preserve causal history across renames, handoffs, replans, and compression.
  4. Produce the compact record: activity, prior outcome, current outcome, valid progress class. Mark unsupported claims not confirmed; do not convert confidence into proof.
  5. Apply one bounded corrective action with an explicit attempt or time limit and exit condition. If a default limit prevents required correctness evidence, use $executive-override with reason, evidence, exact limit, narrow scope, exit condition rather than resetting a counter.
  6. Revalidate only the affected requirement or policy. Report an honest progress statement and one outcome-producing action and return to productive work.

Repository runtime

Apply this procedure inside the installed .ai/ framework. Record observable state in the relevant JSON ledger and route detailed judgment to the linked rules and guides.

Semantic boundaries

  • System, platform, user, repository, domain, safety, security, permission, and destructive-action controls remain higher priority.
  • Equivalent actions share history when their hypothesis, expected evidence, target failure, and intended outcome are unchanged.
  • Preserve immutable parent, caused-by, and delegated-from identifiers across handoffs and context compression; missing ancestry makes depth not confirmed, never zero.
  • Activity alone is not progress. Completion and blockers require their structured evidence contracts.
  • Critical correctness evidence cannot be discarded because a retry, critic, verification, context, or delegation budget expired.
  • Security-negative cases are selected from explicit requirements and the observed trust boundary (identity, permission, ownership/tenant, denial response, and side effects). An agent may mark a case inapplicable only with evidence, not by shrinking Definition of Done.
  • An override permits one extension only. Do not renew or stack overrides unless materially new evidence justifies a separately recorded override; repeated renewal without convergence must stop and report the unresolved condition.

Read the full file on GitHub · 45 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 · 45 lines · 29 tokens per session scan A ca4677f4ae4d

Subscribe to this mod's changes

false-progress-detector is a skill published in the GitHub repository ihabkhaled/AI-Psychiatry (3 stars, last pushed 24d ago), licensed MIT. It adds 29 tokens to every session and 640 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to anti-gaming, differing in 14 lines, and is treated as a copy.

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