ai-devblog: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

Turn coding, debugging, research, architecture, migration, deployment, or product work into a clear, evidence-backed technical post in an existing publishing system. Use when Codex should reconstruct primary evidence, decide whether the material deserves a note or article, align on the intended reader, choose among field-report, explainer, reversal, origin-story, hands-on, or evidence-led argument forms, write and edit the piece, inspect its rendered presentation, and publish it at the requested visibility. Pair with blog-system-design for changes to the shared blog system.

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AI Devblog

Write a technical story that changes what a particular reader understands, believes, or can do. Keep the empirical honesty of a good engineering report, but make comprehension and human interest the organizing priorities.

Use this order of attention:

route the genre → align with the reader → choose the angle → explain → support with evidence → edit → cold-read → present → publish

For substantial work, read the human-writing review and the story modes before proposing an angle. Use these writers as structural inspiration, not as personas to imitate.

Use blog-system-design with this skill when the task changes the shared blog index, taxonomy, article shell, typography, navigation, search, responsive behavior, or reusable components. A post must not silently redesign its publication.

Route the material

First decide whether a devblog is the right container:

Put a routine shipped fact in a changelog or release note.
Put durable project onboarding, commands, API guidance, and contributor instructions in a README; use ai-readme when available.
Put an exhaustive roadmap, product plan, design specification, API reference, or component inventory in documentation.
Continue here for reconstructed technical work, a tested argument, a consequential origin story, or a mechanism worth teaching.

Apply an interestingness gate. A shipped capability alone is not enough. The material needs at least one reader payoff: a non-obvious finding, consequential decision, useful technique, measurable result, corrected belief, surprising failure, illuminating mechanism, or reusable model. Recommend a smaller form when the work does not earn a post.

Choose one primary story mode:

Field report: a change, incident, migration, or measured result.
Short lab note: one useful experiment, release, artifact, or strange fact.
Mechanism explainer: a system made understandable through one example.
Incident or reversal: a reasonable belief that reality disproved.
Evidence-led argument: several concrete cases that revise a common belief.
Hands-on argument: a stance earned through a small reproducible exercise.
Origin story: turning points that explain why a system has its current shape.
Product announcement: what changed, why it helps, the hidden hard part, and the exact constraint.

A post may borrow a secondary move, but do not turn every available fact into an omnibus article. See story modes for structures, title families, and Dan Luu/Fly.io-inspired angle prompts.

Choose a weight that fits the idea:

Note: about 300–800 words; one narrow finding and little ceremony.
Standard: about 800–2,000 words; the default for a developed story.
Feature: over 2,000 words only for a stated editorial reason; consider splitting work that grows beyond roughly 4,000 words.

These are editing signals, not quotas. Never inflate a useful note or compress a mechanism until it no longer makes sense.

Align with the reader

Before a standard or feature post, determine what the skill user expects the reader to be, know, and want. If this is not already explicit, ask one compact batch in the align-me shape:

State each belief as a numbered decision.
Give lettered, mutually exclusive choices with concrete consequences.
Recommend one choice for each decision.
End with Reply approve all to accept 1A, 2B, 3A, or give changes such as 2C. Then wait.

Use the reader-and-angle checkpoint for the exact questions. Default to a smart adjacent engineer who knows the domain but not the project and wants a useful mental model or decision. Do not ask what the user has already answered. For a short note with an obvious readership, state the inferred assumptions briefly and proceed.

Then write privately:

Before: what the reader probably believes or cannot yet do.
After: what the evidence should make them believe, understand, or try.
Spine: the one question the post answers.
Not this post: two or three tempting facts that belong elsewhere.

For a standard or feature post, offer two or three genuinely different angles unless only one honest angle exists. Keep each option compact: title, promise, and tradeoff. Recommend one and wait. Never hide a single thesis behind cosmetic title variants.

Establish the evidence boundary

Reconstruct the work before writing:

Inspect the relevant source, diff, issue, transcript, experiment, incident, or primary external research.
Identify exact revisions, versions, data, configuration, dates, and environment when they affect the claim.
Re-run or read the decisive tests and measurements when reasonably cheap.
Separate source change, commit, push or merge, deployment or migration, and live user-facing verification.
Preserve failed attempts, unfavorable results, reversals, and uncertainty when they explain the final conclusion.
Identify evidence that challenges the preferred thesis, especially for an evidence-led argument.

Distinguish what this agent observed from repository history, another agent's work, human decisions, and inference. Attribute material ideas and firsthand observations. Do not invent motives, reactions, or a first-person experience.

When coding-agent threads exist, use them to recover the real prompt, surprise, failed assumption, and decision sequence. Match a thread by commit, file, command, and timestamp before using keyword similarity. Private threads may inform causality, but quote or screenshot them only after checking disclosure, secrets, identities, private paths, customer data, and internal architecture.

Use the minimum proof that changes a skeptical reader's mind. Judge evidence by fit, directness, scope, freshness, and explanatory value. Link exact source, official documentation, original issues or papers, tests, deployment records, and public results near the claim they support. Put exhaustive logs, methods, and provenance in a linked artifact or appendix.

Do not promote a green build, HTTP 200, commit, upload, or public URL into proof of a different claim. Match numeric precision to the decision: round human durations and measurements unless extra precision changes the conclusion.

Address the elephant

Treat the title and deck as one honest promise. Choose the family that fits the story: result, stance, reversal, distinction, paradox, mechanism, imperative, question, or origin. Between the title, deck, and first paragraph, name the central subject and stakes plainly.

If the post is about a Program Database, say Program Database. If it is about SQLite, a product failure, a commercial interest, or a controversial recommendation, say that. Do not bury the lede behind an abstract principle or mystery hook. Cleverness may sharpen a clear subject; it may not conceal one.

Open according to the mode:

use an artifact or consequence for a field report;
use the disputed premise for an argument;
use the visible result for a tutorial or lab note;
use the admission and current consequence for a reversal;
use the present constraint or decisive turn for an origin story.

Name the system and why it matters by the end of the first paragraph. State the bottom line by the third. Label proposals and unshipped plans explicitly.

Follow the destination's existing front matter, author, avatar, and AI assistance conventions. Do not invent a persona or add a new metadata model for one post. Use a real publication date and a few durable, specific tags.

Explain at the reader's altitude

Default to a smart adjacent engineer. Declare at most three concepts the post may assume. Define project-specific nouns on first use and give plain behavior before an abbreviation or formal term. A link may deepen an explanation; it cannot replace one.

When the mechanism is unfamiliar or the draft makes a causal jump, use as many of these rungs as the reader needs:

observable consequence;
smallest concrete example;
plain-language model;
precise technical term;
implementation detail;
important edge case.

The first three are the usual minimum. Skip a later rung when it does not help the intended reader. Prefer one recurring request, row, trace, failure, or fixture that gains detail over several disconnected examples. Before a command, code excerpt, table, transcript, or screenshot, tell the reader what question it answers; afterward, interpret what matters.

While outlining a mechanism, ask: What should the reader be able to see that is hard to understand from sentences? Plan the representation alongside the explanation, using the visual-language reference. Do not postpone this decision until presentation polish.

Build a private causal outline that connects the starting condition, mechanism, consequence, evidence, objection, and limitation. Let the selected story mode determine the public section order. Add a heading when the argument turns, not because a fixed number of paragraphs elapsed.

Sound like a thoughtful person

Use plain technical prose: concrete subjects, active verbs, consistent terms, and one main idea per sentence. Humor, analogy, fragments, and first-person reactions may earn their place by clarifying an idea, revealing the author's judgment, establishing rapport, or controlling pace. Keep firsthand experiences and reactions grounded in supplied material. Don't manufacture personality or let rhetoric widen the claim.

Make sentences easy to read once. Use ordinary words for actions, even when the subject is technical. Prefer we couldn't change the setting to the configuration lacked mutability. Keep technical terms when they name something precisely; don't make the surrounding language technical by association.

Give the reader one manageable thought at a time. State what happened before attaching qualifications. When a sentence contains an event, a reaction, and a consequence, consider letting them arrive separately.

Let sentence length follow its job. Longer sentences can establish circumstances or explain a mechanism. Short sentences can land a discovery, judgment, or consequence. Fragments can carry an authentic afterthought. Don't alternate lengths mechanically or make every sentence punchy.

Use paragraph breaks where the reader should pause. Don't immediately explain away a sentence that already landed. Prefer simple connections—but, so, because, when—when they express the actual relationship. Read the paragraph aloud: it should sound like someone explaining something they understand, not delivering prepared conclusions. See the line-edit example for how wording and pacing work together.

Preserve what Forge and OverGrid do well: empirical honesty, real limitations, unfavorable measurements, exact artifacts, and corrected assumptions. Tone down their recurring AI-shaped habits:

internal nouns and architecture before the reader knows the concrete system;
repeated not X, but Y, X is not Y, and perfectly balanced reversals;
titles that default to We, a product name, a percentage, or a slogan;
`

View source ↗

File metadata

name: ai-devblog
description: Turn coding, debugging, research, architecture, migration, deployment, or product work into a clear, evidence-backed technical post in an existing publishing system. Use when Codex should reconstruct primary evidence, decide whether the material deserves a note or article, align on the intended reader, choose among field-report, explainer, reversal, origin-story, hands-on, or evidence-led argument forms, write and edit the piece, inspect its rendered presentation, and publish it at the requested visibility. Pair with blog-system-design for changes to the shared blog system.

View original text

---
name: ai-devblog
description: Turn coding, debugging, research, architecture, migration, deployment, or product work into a clear, evidence-backed technical post in an existing publishing system. Use when Codex should reconstruct primary evidence, decide whether the material deserves a note or article, align on the intended reader, choose among field-report, explainer, reversal, origin-story, hands-on, or evidence-led argument forms, write and edit the piece, inspect its rendered presentation, and publish it at the requested visibility. Pair with blog-system-design for changes to the shared blog system.
---

# AI Devblog

Write a technical story that changes what a particular reader understands,
believes, or can do. Keep the empirical honesty of a good engineering report,
but make comprehension and human interest the organizing priorities.

Use this order of attention:

> route the genre → align with the reader → choose the angle → explain →
> support with evidence → edit → cold-read → present → publish

For substantial work, read
[the human-writing review](references/human-writing-review.md) and
[the story modes](references/story-modes.md) before proposing an angle. Use
these writers as structural inspiration, not as personas to imitate.

Use `blog-system-design` with this skill when the task changes the shared blog
index, taxonomy, article shell, typography, navigation, search, responsive
behavior, or reusable components. A post must not silently redesign its
publication.

## Route the material

First decide whether a devblog is the right container:

- Put a routine shipped fact in a changelog or release note.
- Put durable project onboarding, commands, API guidance, and contributor
instructions in a README; use `ai-readme` when available.
- Put an exhaustive roadmap, product plan, design specification, API reference,
or component inventory in documentation.
- Continue here for reconstructed technical work, a tested argument, a
consequential origin story, or a mechanism worth teaching.

Apply an interestingness gate. A shipped capability alone is not enough. The
material needs at least one reader payoff: a non-obvious finding, consequential
decision, useful technique, measurable result, corrected belief, surprising
failure, illuminating mechanism, or reusable model. Recommend a smaller form
when the work does not earn a post.

Choose one primary story mode:

- **Field report:** a change, incident, migration, or measured result.
- **Short lab note:** one useful experiment, release, artifact, or strange fact.
- **Mechanism explainer:** a system made understandable through one example.
- **Incident or reversal:** a reasonable belief that reality disproved.
- **Evidence-led argument:** several concrete cases that revise a common belief.
- **Hands-on argument:** a stance earned through a small reproducible exercise.
- **Origin story:** turning points that explain why a system has its current
shape.
- **Product announcement:** what changed, why it helps, the hidden hard part,
and the exact constraint.

A post may borrow a secondary move, but do not turn every available fact into
an omnibus article. See [story modes](references/story-modes.md) for structures,
title families, and Dan Luu/Fly.io-inspired angle prompts.

Choose a weight that fits the idea:

- **Note:** about 300–800 words; one narrow finding and little ceremony.
- **Standard:** about 800–2,000 words; the default for a developed story.
- **Feature:** over 2,000 words only for a stated editorial reason; consider
splitting work that grows beyond roughly 4,000 words.

These are editing signals, not quotas. Never inflate a useful note or compress
a mechanism until it no longer makes sense.

## Align with the reader

Before a standard or feature post, determine what the skill user expects the
reader to **be**, **know**, and **want**. If this is not already explicit, ask
one compact batch in the `align-me` shape:

1. State each belief as a numbered decision.
2. Give lettered, mutually exclusive choices with concrete consequences.
3. Recommend one choice for each decision.
4. End with `Reply approve all to accept 1A, 2B, 3A, or give changes such as
2C.` Then wait.

Use [the reader-and-angle checkpoint](references/angle-review.md) for the exact
questions. Default to a smart adjacent engineer who knows the domain but not
the project and wants a useful mental model or decision. Do not ask what the
user has already answered. For a short note with an obvious readership, state
the inferred assumptions briefly and proceed.

Then write privately:

- **Before:** what the reader probably believes or cannot yet do.
- **After:** what the evidence should make them believe, understand, or try.
- **Spine:** the one question the post answers.
- **Not this post:** two or three tempting facts that belong elsewhere.

For a standard or feature post, offer two or three genuinely different angles
unless only one honest angle exists. Keep each option compact: title, promise,
and tradeoff. Recommend one and wait. Never hide a single thesis behind cosmetic
title variants.

## Establish the evidence boundary

Reconstruct the work before writing:

1. Inspect the relevant source, diff, issue, transcript, experiment, incident,
or primary external research.
2. Identify exact revisions, versions, data, configuration, dates, and
environment when they affect the claim.
3. Re-run or read the decisive tests and measurements when reasonably cheap.
4. Separate source change, commit, push or merge, deployment or migration, and
live user-facing verification.
5. Preserve failed attempts, unfavorable results, reversals, and uncertainty
when they explain the final conclusion.
6. Identify evidence that challenges the preferred thesis, especially for an
evidence-led argument.

Distinguish what this agent observed from repository history, another agent's
work, human decisions, and inference. Attribute material ideas and firsthand
observations. Do not invent motives, reactions, or a first-person experience.

When coding-agent threads exist, use them to recover the real prompt, surprise,
failed assumption, and decision sequence. Match a thread by commit, file,
command, and timestamp before using keyword similarity. Private threads may
inform causality, but quote or screenshot them only after checking disclosure,
secrets, identities, private paths, customer data, and internal architecture.

Use the minimum proof that changes a skeptical reader's mind. Judge evidence by
fit, directness, scope, freshness, and explanatory value. Link exact source,
official documentation, original issues or papers, tests, deployment records,
and public results near the claim they support. Put exhaustive logs, methods,
and provenance in a linked artifact or appendix.

Do not promote a green build, HTTP 200, commit, upload, or public URL into proof
of a different claim. Match numeric precision to the decision: round human
durations and measurements unless extra precision changes the conclusion.

## Address the elephant

Treat the title and deck as one honest promise. Choose the family that fits the
story: result, stance, reversal, distinction, paradox, mechanism, imperative,
question, or origin. Between the title, deck, and first paragraph, name the
central subject and stakes plainly.

If the post is about a Program Database, say `Program Database`. If it is about
SQLite, a product failure, a commercial interest, or a controversial
recommendation, say that. Do not bury the lede behind an abstract principle or
mystery hook. Cleverness may sharpen a clear subject; it may not conceal one.

Open according to the mode:

- use an artifact or consequence for a field report;
- use the disputed premise for an argument;
- use the visible result for a tutorial or lab note;
- use the admission and current consequence for a reversal;
- use the present constraint or decisive turn for an origin story.

Name the system and why it matters by the end of the first paragraph. State the
bottom line by the third. Label proposals and unshipped plans explicitly.

Follow the destination's existing front matter, author, avatar, and AI
assistance conventions. Do not invent a persona or add a new metadata model for
one post. Use a real publication date and a few durable, specific tags.

## Explain at the reader's altitude

Default to a smart adjacent engineer. Declare at most three concepts the post
may assume. Define project-specific nouns on first use and give plain behavior
before an abbreviation or formal term. A link may deepen an explanation; it
cannot replace one.

When the mechanism is unfamiliar or the draft makes a causal jump, use as many
of these rungs as the reader needs:

1. observable consequence;
2. smallest concrete example;
3. plain-language model;
4. precise technical term;
5. implementation detail;
6. important edge case.

The first three are the usual minimum. Skip a later rung when it does not help
the intended reader. Prefer one recurring request, row, trace, failure, or
fixture that gains detail over several disconnected examples. Before a command,
code excerpt, table, transcript, or screenshot, tell the reader what question
it answers; afterward, interpret what matters.

While outlining a mechanism, ask: **What should the reader be able to see that is hard to understand from sentences?** Plan the representation alongside the explanation, using [the visual-language reference](references/visual-language.md). Do not postpone this decision until presentation polish.

Build a private causal outline that connects the starting condition, mechanism,
consequence, evidence, objection, and limitation. Let the selected story mode
determine the public section order. Add a heading when the argument turns, not
because a fixed number of paragraphs elapsed.

## Sound like a thoughtful person

Use plain technical prose: concrete subjects, active verbs, consistent terms,
and one main idea per sentence. Humor, analogy, fragments, and first-person
reactions may earn their place by clarifying an idea, revealing the author's
judgment, establishing rapport, or controlling pace. Keep firsthand experiences
and reactions grounded in supplied material. Don't manufacture personality or
let rhetoric widen the claim.

**Make sentences easy to read once.** Use ordinary words for actions, even when
the subject is technical. Prefer `we couldn't change the setting` to `the
configuration lacked mutability`. Keep technical terms when they name something
precisely; don't make the surrounding language technical by association.

Give the reader one manageable thought at a time. State what happened before
attaching qualifications. When a sentence contains an event, a reaction, and a
consequence, consider letting them arrive separately.

Let sentence length follow its job. Longer sentences can establish circumstances
or explain a mechanism. Short sentences can land a discovery, judgment, or
consequence. Fragments can carry an authentic afterthought. Don't alternate
lengths mechanically or make every sentence punchy.

Use paragraph breaks where the reader should pause. Don't immediately explain
away a sentence that already landed. Prefer simple connections—`but`, `so`,
`because`, `when`—when they express the actual relationship. Read the paragraph
aloud: it should sound like someone explaining something they understand, not
delivering prepared conclusions. See the
[line-edit example](references/human-writing-review.md#sentence-level-pacing)
for how wording and pacing work together.

Preserve what Forge and OverGrid do well: empirical honesty, real limitations,
unfavorable measurements, exact artifacts, and corrected assumptions. Tone
down their recurring AI-shaped habits:

- internal nouns and architecture before the reader knows the concrete system;
- repeated `not X, but Y`, `X is not Y`, and perfectly balanced reversals;
- titles that default to `We`, a product name, a percentage, or a slogan;
- `

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
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
Stars/forks activity: 156 stars, 9 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.

Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: swyxio/skills
License: MIT
Version: 1.0.0
Last GitHub push: Sep 3, 2026
Registry updated: Sep 6, 2026
Instruction path: ai-devblog/SKILL.md @ 79df950293f8

Version reported in registry metadata; check source releases before relying on it.

Quality

69/100

Promising

Trust

62/100

Sandbox only

Agent access

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 install API LLM text format Open manifest Auto-resolve Plan

More details

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      "lastPushed": "14d since push",
      "license": "MIT",
      "repository": "https://github.com/swyxio/skills/tree/main/ai-devblog",
      "install": "npx skills add swyxio/skills --skill ai-devblog",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "research",
      "agent-skill"
    ],
    "known_risks": [
      "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",
      "Stars/forks activity: 156 stars, 9 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 77,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "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",
      "Stars/forks activity: 156 stars, 9 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 69,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "14d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "imbad0202-academic-research-skills",
      "name": "Academic Research Skills",
      "url": "https://www.openagentskill.com/skills/imbad0202-academic-research-skills",
      "stars": 38374,
      "install_command": "",
      "trust_score": 89,
      "audit_score": 91
    },
    {
      "slug": "yanliudesign-mono-color-skill",
      "name": "mono-color",
      "url": "https://www.openagentskill.com/skills/yanliudesign-mono-color-skill",
      "stars": 1919,
      "install_command": "npx skills add yanliudesign/mono-color-skill --skill mono-color",
      "trust_score": 85,
      "audit_score": 93
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "Financial research output is not financial advice; require human review before any live investment decision."
  ],
  "agent_contract": {
    "task_input": "Use ai-devblog in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 70/100 Manual review",
      "Audit: 77/100 Needs review",
      "Safety: 29/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "swyxio-ai-devblog (ai-devblog)",
      "install_command": "npx skills add swyxio/skills --skill ai-devblog",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "swyxio-ai-devblog",
      "task": "Use ai-devblog in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/swyxio-ai-devblog",
    "api": "https://www.openagentskill.com/api/agent/skills/swyxio-ai-devblog",
    "audit": "https://www.openagentskill.com/skills/swyxio-ai-devblog/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=swyxio-ai-devblog&task=Use%20ai-devblog%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-devblog%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ai-devblog%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/swyxio-ai-devblog/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/swyxio-ai-devblog"
  }
}

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Creator: swyxio
Source: swyxio/skills
Indexed by: OpenAgentSkill community index

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