shadow-frog-meditate

shadow-frog-meditate is a skill for Claude Code, Codex from microsoft/ShadowFrog. It costs 81 tokens per session (3,077 once invoked), scanned A, original, MIT.

A cleanup tool for a `.shadow/` code knowledge base. It finds repeated or conflicting notes and combines or resolves them by checking the code.

In plain words
What is it for?
Use it to scan shadow files, remove duplicates, merge overlapping discoveries, and investigate conflicts across individual files and shared notes.
Why use it?
Old notes can become noisy or contradictory, making it harder for coding agents to find reliable information.

Skill for Claude CodeCodex ✓ vendor

Written for no agent in particular: nothing here depends on one. Also seen: reads .claude/ paths; mentions subagents.

Good fit Use it to scan shadow files, remove duplicates, merge overlapping discoveries, and investigate conflicts across individual files and shared notes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/microsoft/shadowfrog/shadow-frog-meditate
About the project

ShadowFrog is a suite of coding-agent skills that maintains a file-backed knowledge base of tacit information about a codebase, such as fragile dependencies, untested invariants, and hard-to-notice edge cases. It helps coding agents preserve lessons from code reading, experiments, and conversations across future work. The catalogue contains skills that create, update, explore, organize, and view this shadow knowledge base.

microsoft/ShadowFrog · 30 stars · on GitHub

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 microsoft/ShadowFrog --skill shadow-frog-meditate
Clone the repo
git clone --depth 1 https://github.com/microsoft/ShadowFrog

Made for: Claude Code, Codex.

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 shadow-frog-meditate

README.md
[![agentmods](https://agentmods.dev/badge/skills/microsoft/shadowfrog/shadow-frog-meditate/github.svg)](https://agentmods.dev/skills/microsoft/shadowfrog/shadow-frog-meditate)
Your own site
<a href="https://agentmods.dev/skills/microsoft/shadowfrog/shadow-frog-meditate"><img src="https://agentmods.dev/badge/skills/microsoft/shadowfrog/shadow-frog-meditate/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 shadow-frog-meditate

Your own site · 80×15
<a href="https://agentmods.dev/skills/microsoft/shadowfrog/shadow-frog-meditate"><img src="https://agentmods.dev/badge/skills/microsoft/shadowfrog/shadow-frog-meditate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 81 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,077 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 302
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
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 $0.00081 $0.03077
Opus 5.5 $0.00032 $0.01231
Sonnet 5.5 $0.00016 $0.00615
Haiku 4.5 $0.00008 $0.00308

Measured 29d ago against content hash 32c832d4a735, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

shadow-frog-meditate 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 29d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (meditate-repair.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/shadow-frog-meditate/SKILL.md · 295 lines

How it starts

The opening of the file, as written. The whole thing — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ShadowFrog Meditate

Shadow hygiene — deduplicate, merge, and resolve conflicts across the entire .shadow/ knowledge base. Prerequisite: .shadow/ exists with discoveries.

Phase 1: Scan

Use parallel subagents to scan the shadow. Each subagent handles a batch of shadow files.

Scope Optimization

Not every file needs scanning. To reduce cost:

  • Skip files with 0-1 discoveries — they can't have internal duplicates
  • Focus on files modified since last meditate — check _meta/state.json last_update_at against file modification times
  • Always scan files with 5+ discoveries — highest duplicate risk

For the first meditate after a large dream run, most files will need scanning.

Per-File Scan

For each per-file shadow (e.g., src/auth.py.md):

  1. Read all discoveries under each ## symbol heading
  2. Compare discoveries under the same symbol by behavioral meaning, not wording:
    • Duplicate: same behavioral claim, different wording
    • Near-duplicate: one discovery is a subset/refinement of the other
    • Conflict: the two discoveries make contradicting claims
    • Distinct: genuinely different insights — no action needed
  3. Record each finding as a structured action (see below)

Overlapping Also involves: refs can help identify related claims.

Scan Output Format

Subagents must output findings as one JSON object per line so the orchestrator can auto-apply resolutions. This is critical for automation — prose recommendations require manual interpretation.

{"action": "merge", "file": "src/auth.py.md", "symbol": "authenticate_user", "keep": "- silently returns None on expired tokens...", "remove": "- returns None when token expires...", "merged": "- authenticate_user() silently returns None on expired tokens instead of raising. 3 of 7 callers don't check.\n  _(verified, source: exploration, citation_score: 0)_", "reason": "duplicate: same claim, different wording"}
{"action": "merge", "file": "src/db.py.md", "symbol": "connect", "keep": "- connection pool exhaustion...", "remove": "- pool runs out...", "merged": "...", "reason": "near-duplicate: first extends second"}
{"action": "conflict", "file": "src/auth.py.md", "symbol": "validate_token", "entry_a": "- raises ValueError...", "entry_b": "- returns False...", "resolution": "verified_a", "reason": "code inspection: line 42 raises ValueError"}
{"action": "conflict", "file": "src/cache.py.md", "symbol": "invalidate", "entry_a": "...", "entry_b": "...", "resolution": "escalate", "reason": "both claims have evidence, needs user input"}
{"action": "move_to_cross", "file": "src/auth.py.md", "symbol": "validate_token", "entry": "- all validators share...", "cross_slug": "shared-validation-pattern", "reason": "cross-scope: involves 4 files"}

Read the full file on GitHub · 295 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 29d ago Changed · +4 lines 32c832d4a735
  2. 1mo ago Changed · -38 lines d758b8f5c17f
  3. 1mo ago First seen · 329 lines · 81 tokens per session scan A e3ee2df106bb

Subscribe to this mod's changes

shadow-frog-meditate is a skill published in the GitHub repository microsoft/ShadowFrog (30 stars, last pushed 6d ago), licensed MIT. It adds 81 tokens to every session and 3,077 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.

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