agent-decision-receipts

REVIEW · 66
Registry indexed

Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven la

Verified installs0
Stars24.8K
Version1.0.0
Quality91/100 · Excellent
Trust66/100 · Sandbox only
Audit83/100 · Needs review

Supply asset profile

Research and knowledge work

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Browse track

Scenario

Research agents

I need my agent to research a topic, compare sources, and produce a concise report.

Agent fit

Claude Code + CLI + Codex

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add alirezarezvani/claude-skills --skill agent-decision-receipts

Maintenance

fresh

Pushed today

Risk

Needs review

Dependency or permission surface needs review

GitHub quality

25K

91/100 Quality · 74/100 Trust

Coverage tags

ResearchResearch agentsagent-skill

Review notes

Dependency or permission surface needs review · Permission surface may require sandboxing

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Excellent
91

High-confidence pick with strong adoption and healthy maintenance signals.

Trust

Sandbox only
66

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

Audit

Needs review
83

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Human review before install

Run only in a sandbox and compare close alternatives before using it for real work.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

25K GitHub stars

Repo activity

25K stars, 3.5K forks

Maintenance

Pushed today

License

MIT

Install

npx skills add alirezarezvani/claude-skills --skill agent-decision-receipts

Install safety

standard package or runtime install path

Permission surface

secrets or environment access, shell or command execution

Agent outcomes

No agent outcome data yet

Docs

Strong README/SKILL.md context

Risk summary

Review before production

  • The skill relies on an external package (openagentontology) for cryptographic operations; while this is clearly documented, the package's security posture is not independently verified by this review.
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is declared
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

Open JSON

Suited tasks

  • Research agents workflows
  • Claude Code teams
  • teams that value GitHub adoption signals
  • Search sources

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add alirezarezvani/claude-skills --skill agent-decision-receipts
Policy
review
Human review
yes

Trust and risk

Trust
66/100
Audit
83/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add alirezarezvani/claude-skills --skill agent-decision-receipts

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • The skill relies on an external package (openagentontology) for cryptographic operations; while this is clearly documented, the package's security posture is not independently verified by this review.
  • No OpenAgentSkill engagement data yet
  • High-risk permission hints: Shell or command execution, Secrets or environment access

Agent safety v2

39/100 · Avoid automatic install

Experimentalreview

Sparse or mixed signals. Useful for discovery, but not for autonomous installation.

Test manually in an isolated workspace and compare against safer alternatives.

Resolve via API

high

Shell or command execution

Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

high

Secrets or environment access

Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.

  • High-risk permission hints: Shell or command execution, Secrets or environment access
  • Dependency or permission surface needs review

Install targets

Install this skill in your agent workflow

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

skill install

OpenAgentSkill CLI

Resolve policy, run the source installer safely, and report a verified install receipt.

$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install alirezarezvani-agent-decision-receipts

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use agent-decision-receipts in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20agent-decision-receipts%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/alirezarezvani-agent-decision-receipts/install
Install command: npx skills add alirezarezvani/claude-skills --skill agent-decision-receipts
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

Use agent-decision-receipts for this task. Review https://www.openagentskill.com/api/skills/alirezarezvani-agent-decision-receipts/install, then install with: npx skills add alirezarezvani/claude-skills --skill agent-decision-receipts

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

100/100

Research agents

Platforms

Claude Code

Audit report

Needs review · 83/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Primary pick for Research agents

Use this as a leading candidate, then validate the README and install path in your own agent stack.

100
Readiness
Adopt
Stage

Role in stack

Primary pick

Primary fit

Research agents

Trust label

Production-ready

Install path

Command ready

Use when

  • Research agents workflows
  • Claude Code teams
  • teams that value GitHub adoption signals

Evidence

  • 24,795 GitHub stars
  • recent repository activity
  • install command or GitHub repo available
  • 91/100 quality profile

review first

  • The skill relies on an external package (openagentontology) for cryptographic operations; while this is clearly documented, the package's security posture is not independently verified by this review.
  • No OpenAgentSkill engagement data yet

Implementation path

  1. 1Install it in a sandbox agent and run one Research agents task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Sandbox only

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

66
OpenAgentSkill Trust Score

GitHub adoption

PASS

25K GitHub stars

Stars/forks activity

PASS

25K stars, 3.5K forks; issue activity unavailable in current metadata

Recent maintenance

PASS

Pushed today

License clarity

PASS

MIT

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Large GitHub adoption signal
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • The skill relies on an external package (openagentontology) for cryptographic operations; while this is clearly documented, the package's security posture is not independently verified by this review.
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Run only in a sandbox and compare close alternatives before using it for real work.

Quality profile

Excellent candidate for agent workflows

High-confidence pick with strong adoption and healthy maintenance signals.

91
GitHub stars
25K
Freshness
Today
Install ready
Yes
License
MIT
Review before install: The skill relies on an external package (openagentontology) for cryptographic operations; while this is clearly documented, the package's security posture is not independently verified by this review.

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

Overview

--- name: "agent-decision-receipts" description: "Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion." ---

# Agent Decision Receipts

## Overview

A log says an action happened. A **receipt is tamper-evident**: it records who, what, and under which policy, and it is signed, so any later edit breaks the signature. This skill mints one for a consequential agent action and verifies it later from the certificate alone: no database, no network, no trusting the issuer.

The crypto is not in this skill. It is the open-source **OpenAgentOntology** receipt primitive (Apache-2.0), which signs every receipt with Ed25519 **and** the post-quantum legs ML-DSA-65 (FIPS 204) + SLH-DSA (FIPS 205) when the post-quantum backend is installed. This skill is the decision layer: when to mint, what to put in, how to verify. One install, no per-skill crypto.

**Three decisions, nothing else:**

1. **Does this action need a receipt?** — side-effecting + consequential + later-provable = yes. 2. **Mint the receipt** — build the action manifest, sign it with the OAO primitive. 3. **Verify it** — recompute the hash, check each signature leg, from the cert alone.

This skill is **NOT log analysis.** Logs describe what happened and can be silently edited. A receipt is minted before/at execution and breaks if edited. Use logs for debugging; use receipts for evidence.

This skill is **NOT a hosted notary.** It mints a LOCAL, self-signed receipt anyone can verify offline. Cross-organization verification (one org proving to another) is a separate hosted service, out of scope here.

This skill is **NOT a legal opinion.** It produces evidence shaped to support FRE 902(13)/(14)-style certification and EU AI Act Article 12 record-keeping. Whether a given receipt is admitted is a question for counsel.

## Quick Start

```bash # Install the open-source receipt primitive (Apache-2.0). Add [pq] for the post-quantum legs. pip install "openagentontology[pq]"

# 1. Build + validate an action manifest (stdlib only, no crypto, no network) python scripts/build_action_manifest.py --agent my-deploy-agent --operation deploy \ --target prod/api --policy "EU AI Act Art 12" --out action.json

# 2. Mint the receipt over it (Ed25519 + post-quantum legs) python -c "import json,openagentontology.receipt as r; \ print(json.dumps(r.mint_receipt(json.load(open('action.json')), decision='ACTION_GOVERNED')))" > receipt.json

# 3. Verify from the cert alone (no DB, no network) python -c "import json,openagentontology.receipt as r; \ print(r.verify_receipt(json.load(open('receipt.json'))))" # -> {'ok': True, 'sig_ok': True, ... 'reason': 'verified from the cert alone via: ed25519, ml_dsa, slh_dsa'} ```

> **Dependency note.** This skill delegates the signing to `openagentontology` (Apache-2.0, opt-in `pip install`). The script shipped here is stdlib-only and adds no repo dependency; the package is installed by the operator (BYO-library pattern). If it is not installed, the build step still works — only minting/verifying require it.

## Core Workflow

The three decisions below are the skill: decide whether to receipt, mint, then verify.

## Decision 1: Does this action need a receipt?

Mint a receipt when the action is **all three** of:

| Test | Mint if... | |------|-----------| | Side-effecting | it writes, sends, deploys, deletes, pays, grants access, or changes external state | | Consequential | a wrong call costs money, breaks compliance, or harms a person | | Later-provable | someone (auditor, insurer, regulator, court, counterparty) may ask "what did the agent do and why?" |

Read-only, reversible, trivial actions do **not** need a receipt. Receipt everything and the signal drowns; receipt nothing and the one call that mattered cannot be proven.

High-signal triggers (mint by default): `deploy`, `delete`, `pay`/`wire`/`refund`, `grant_access`, `export`/`egress`, `approve`/`deny` a claim, any model decision that affects a person under a high-risk AI system.

## Decision 2: Mint the receipt

The action manifest is any ASCII-safe dict describing what the agent did. Four keys are **required** — `build_action_manifest.py` rejects the manifest (exit 2) if any is missing. Two more are added automatically:

| Key | Required? | What it carries | |-----|-----------|-----------------| | `agent_id` | **required** | the acting agent | | `operation` | **required** | the verb (deploy / delete / pay / decide / ...) | | `target` | **required** | what it acted on | | `policy` | **required** | the rule that governs it (e.g. "EU AI Act Art 12", "internal change-control") | | `inputs_hash` | auto-added | a hash of `--inputs`, so the full payload need not be stored in the clear (defaults to the hash of empty when `--inputs` is omitted) | | `decision_label` | auto-added | the receipt decision label (defaults to `ACTION_GOVERNED`) |

`mint_receipt(manifest, decision=...)` hashes the full manifest into the receipt evidence, signs the canonical body, and returns a receipt that carries: `evidence_hash`, `signature_b64` (Ed25519), and — when `[pq]` is installed — `ml_dsa_signature_b64` + `slh_dsa_signature_b64`. Each leg signs the same bytes; any one verifying proves authenticity.

> See [references/receipt-fields.md](references/receipt-fields.md) for the full receipt schema and the post-quantum rationale.

## Decision 3: Verify it

`verify_receipt(receipt)` recomputes `sha256(canonical(evidence))`, compares it to `evidence_hash`, then checks every signature leg it has a backend for. It returns `{ok, hash_ok, sig_ok, legs, reason}`. A single edited byte anywhere in the action breaks `hash_ok`; a forged signature breaks the leg. Verification needs only the receipt — no call back to the issuer.

This is the property that makes it evidence: a reviewer who distrusts the issuer can still confirm the receipt is intact and authentic, entirely offline.

## Anti-Patterns

- **Receipt the log, not the decision.** Minting a receipt over a log line written after the fact proves nothing. Mint at the point of action, over the action. - **Storing the signing key next to the receipts.** If the key is compromised, signatures mean nothing. Treat the key like any signing secret; never commit it. - **Ed25519-only when the post-quantum legs are available.** A receipt is long-lived evidence. Sign it once with the post-quantum legs (ML-DSA-65 + SLH-DSA) so it stays verifiable if a future quantum computer could break Ed25519. Install `[pq]`. - **Putting raw secrets or PII in the manifest.** The manifest is hashed into evidence and is recoverable from the receipt. Carry hashes (`inputs_hash`), not the cleartext. - **Calling it "admissible."** It is evidence shaped to *support* FRE 902(13)/(14)-style certification. Admissibility is a court's decision, not the tool's claim. - **Faking a signature when crypto is missing.** The primitive emits an explicit `unsigned` flag instead. Never present an unsigned receipt as signed.

## Cross-References

- `ra-qm-team/skills/eu-ai-act-specialist/` — decide the AI system's risk tier and Article 12 obligations; this skill mints the per-action record those obligations require. - `ra-qm-team/skills/iso42001-specialist/` — the AI management-system controls; receipts are the per-decision evidence those controls call for. - OpenAgentOntology (Apache-2.0): the open receipt primitive this skill drives — `pip install "openagentontology[pq]"`.

Technical details

Version
1.0.0
License
MIT
Last updated
Aug 22, 2026
Published
Aug 22, 2026

Decision snapshot

Primary pick

100
Ready
Adopt
Stage

24,795 GitHub stars

Audit

Install review

Install and adoption review

83
Needs review
Security
69/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Growth loop

Share kit

X

Scenario-led draft for agent-decision-receipts, ready for a manual X post.

Curator note
agent-decision-receipts: Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy,...

24.8K stars

https://www.openagentskill.com/skills/alirezarezvani-agent-decision-receipts?ref=x
Open X draft
Optional reply with install command
Listing + install path for agent-decision-receipts:
https://www.openagentskill.com/skills/alirezarezvani-agent-decision-receipts?ref=x

Install: npx skills add alirezarezvani/claude-skills --skill agent-decision-receipts

Listing source

Registry indexed

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

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Owner claim

Claim this skill listing

This Registry indexed listing is attributed to alirezarezvani but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

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Author

A

alirezarezvani

@alirezarezvani

Platform fit

Health signals

GitHub stars
24.8K
Quality score
54/100
Last GitHub push
Aug 22, 2026
Framework hints
Unknown
OpenAgentSkill views
0
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Sandbox only

66
  • GitHub adoption25K GitHub starsPASS
  • Stars/forks activity25K stars, 3.5K forks; issue activity unavailable in current metadataPASS
  • Recent maintenancePushed todayPASS
  • License clarityMITPASS
  • README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
  • Dependency/runtime riskcommand execution surface, credential or environment accessFIX