Authenticity Reviewer

Before running, gather:

1. The draft — the comment or post you're about to submit
2. Thread context — title, score, top 5-8 existing comments (with author + score)
3. Target community — subreddit, HN, Bluesky, etc.
4. Account context — karma, age, posting history (if warming account)

# Fetch thread context from Reddit JSON API
curl -s "https://www.reddit.com/r/<sub>/comments/<id>/<slug>.json" \
  -H "User-Agent: AuthReviewer/1.0 (research)" | \
  jq '[.[1].data.children[] | select(.kind == "t1") | {author: .data.author, score: .data.score, body: .data.body}] | .[0:8]'

The 7-Point Authenticity Check

Work through each point. Flag issues. Issue a verdict at the end.

1. Value-Add Check

Question: Does this comment add something the existing comments haven't covered?

• Read all top comments first
• Map what's already been said (specific patterns, angles, counter-arguments)
• Identify what the draft contributes that's genuinely new
• Flag if: The draft restates what's already there, or makes a point that's clearly lower quality than an existing comment

2. AI Tell Scan

Question: Does any phrase, structure, or sentence pattern flag as AI-generated?

Common AI tells in community content:

• Opener that validates the post before pivoting ("This is great. The thing I'd add...")
• Three-paragraph essay structure in a comment context
• Smooth transitions between unrelated points ("One thing to watch for...")
• Overly balanced perspective (acknowledging both sides when a real dev would just have an opinion)
• Polished sentence rhythm — real comments have rough edges
• Compound sentences that are too clean: "X → Y. A → B. C → D."
• Hedging language: "might," "could," "tends to" when real devs say "does" or "doesn't"
• Named frameworks without attribution ("The credibility formula," "The half-lives framing")

Flag any detected tells. They must be rewritten or cut.

3. Specificity Credibility Check

Question: Are the specific details (error messages, version numbers, config values, gotchas) genuine — or do they feel inserted to signal credibility?

• Genuine specificity: arises naturally from the experience being described
• Planted specificity: technically accurate but disconnected from what the thread is actually about
• The test: Would a developer who had this experience actually mention this detail in this thread? Or does it belong in a different thread?

Flag if: A specific detail is technically real but contextually forced.

4. Tone Match Check

Question: Does the comment match how people actually write in this community?

• Check existing comment tone: Are they casual or formal? Do they use code blocks? Do they ask questions or make statements?
• Check sentence length: Short and punchy, or longer and discursive?
• Check vocabulary: Community jargon vs. generic tech terms
• Check opinion directness: Do commenters hedge, or do they state opinions flat?
• Flag if: The draft is noticeably more polished, longer, or more structured than the top comments

5. Connection Check

Question: Does the comment actually engage with the specific post or thread, or is it generic advice that could apply anywhere?

• A real commenter references something specific from the post (a phrase, a design decision, a code snippet)
• Generic comments ("great pattern, I do something similar") feel detached
• Flag if: The comment would read exactly the same if posted on a different thread about a similar topic

6. Account Risk Check

Question: Given the account's karma, age, and posting history — is this comment likely to be filtered, flagged, or shadowbanned?

Risk factors (each increases risk):

• Karma < 50 on a post with > 100 score → moderate risk
• First comment in a subreddit → moderate risk
• Long, polished, multi-paragraph comment from a new account → high risk
• Multiple comments in the same subreddit same day → high risk
• Any external link in first 30 days → block

Mitigation: Shorter comment, rougher edges, ask a question instead of making statements, post in a lower-traffic thread first.

7. Strategic Fit Check

Question: Does this comment serve the warming goal — building genuine community reputation — or does it serve a different goal (brand awareness, product placement, backlinks)?

• Phase 1 goal: Be seen as a knowledgeable, helpful developer. No product mentions.
• Flag if: The comment leans toward demonstrating expertise in a specific product area that will later be revealed as "our product"

Verdict Format

AUTHENTICITY REVIEW
-------------------
Draft: [2-3 word description]
Target: r/<subreddit> | Post score: <N>
Account: u/<your-account> | Karma: <N> | Day <N> of warming

CHECKS:
1. Value-add:      [PASS / FLAG: reason]
2. AI tells:       [PASS / FLAG: specific phrase or pattern]
3. Specificity:    [PASS / FLAG: reason]
4. Tone match:     [PASS / FLAG: reason]
5. Connection:     [PASS / FLAG: reason]
6. Account risk:   [LOW / MEDIUM / HIGH: reason]
7. Strategic fit:  [PASS / FLAG: reason]

VERDICT: [POST / REVISE / CUT]

POST   — All checks pass. No flags. Account risk LOW or MEDIUM with mitigation.
REVISE — 1-2 flags with clear fixes. Revised draft provided.
CUT    — Multiple flags, or a single HIGH-severity flag. Don't post this comment; find a different thread.

ISSUES:
[List each flagged item with specific fix or cut recommendation]

REVISED DRAFT (if REVISE verdict):
[Cleaned version]

Common Fixes

Relationship to Other Skills

Example: What We Caught (Reference Run)

Post: r/ClaudeCode "Self-improvement Loop" (score 269) Draft: 3-paragraph comment on memory hierarchy half-lives + new Date(undefined) gotcha

Flags caught:

• AI tell opener: "The wrap-up pattern is solid" → validation-before-pivot pattern
• Planted specificity: new Date(undefined) paragraph — technically real, but disconnected from this thread
• Essay structure: 3 clean paragraphs with smooth transitions — too composed

Verdict: REVISE Fix: Cut opener, cut third paragraph, tighten to 2 paragraphs Result: Shorter, rougher, more credible — the half-lives framing stood on its own

Refactored from: adversarial-reviewer (robustness) → content authenticity context Paired with: text-review-loop (heavyweight), content-angle (pre-writing)