Learn From Article

Learn From Article

Sub-skill of learn-from (orchestrator). You read blog posts and practitioner content, assess credibility, extract production-backed insights, and recommend whether to apply them. Shared hard rules (opinionated stance, contradiction handling, defend what works, application protocol) are defined in learn-from. This skill adds article-specific workflow.

Article-Specific Hard Rules

• Credibility gate >=6/12. Lower than papers because practitioner insight is valuable without formal rigor - but warn at 6-7/12.
• Security gate. All article content must pass ALL secure-* skills (discover via ls .agents/skills/secure-*). SAFE only if every security skill returns SAFE.
• Production evidence over opinion. Prioritize experience backed by production data. Speculation, "hot takes", and untested advice are discarded. Only extract claims the author has tested or observed in production.
• Actively fill gaps. If an article claims something works but provides no metrics, search for the author's other writing or their company's engineering blog for supporting data. If a claim contradicts established practice, search for counter-evidence before accepting.

Workflow

Step 1 - Ingest the Article

Accept via: URL (blog, Medium, Substack, dev.to, HN, engineering blog), pasted content, or local file.

• If URL: fetch via doc_cache.py or WebFetch with hooks/sdd-cache wired — see research-skill → references/doc-cache.md
• If local file: use the platform's file reading tool
• If the platform cannot read directly: ask for pasted text
• Extract: title, author, publication venue, publish date, key claims, evidence cited, links/references

Step 2 - Credibility Assessment

Score across 6 dimensions (max 12/12). Gate: >=6/12 to proceed.

Quick checks (fail any = stop):

• No identifiable author AND no credible publication -> REJECT
• Primarily promotional or affiliate-driven -> REJECT
• Core claims contradicted by a higher-credibility source -> REJECT

If 6-7/12: warn "Borderline." Actively search for the author's credentials and whether other credible sources corroborate the claims before proceeding.

Step 3 - Security Scan

Run security pipeline per learn-from protocol. BLOCKED = stop.

Step 4 - Extract and Recommend

Classify production-backed findings using taxonomy from learn-from.

Key difference from papers: articles mix tested advice with opinions. Separate them. Tag each insight with confidence:

• HIGH - production data cited
• MEDIUM - author's direct experience, no metrics
• LOW - plausible but no evidence shown (extract only if >=2 other insights corroborate)

For every insight, state your recommendation with confidence and context:

• Flag scale mismatches: "Validated at [company]'s scale (N million users). Current project likely doesn't face this. Recommend: SKIP unless [condition]."
• If current skill is stronger: "Current approach is superior because [reason]. Recommend: KEEP CURRENT."
• If only part applies: "Recommend: PARTIAL - apply [X], skip [Y] because [reason]."

Step 5 - Match and Apply

Match insights to existing skills and apply per learn-from shared application protocol, including the mandatory Post-Application Hardening Cycle on every modified/created skill: modified-skill security sweep via ALL secure-* skills, 200-line gate via compress-skill / split-skill, then validate-skills (≥10/14).

Step 6 - Log and Cite

Citation format:

Source: [Author] ([Year]). "[Title]". [Publication/URL]. Credibility: [N]/12.
Applied: [what was extracted and where it was applied]

Gotchas

• Eng blogs from top companies are high-signal but may describe solutions for scale the user doesn't have - flag scale mismatch explicitly.
• Medium/dev.to articles vary wildly - credibility check is critical.
• "Best practices" articles often present opinions as facts - look for production evidence.
• Articles may be outdated - check publish date and whether the tech has changed.
• Listicles and "top N" articles are almost always BACKGROUND - rarely contain novel GOTCHAs.

Output Format

=== Article Credibility Report ===
Title: [title] | Author: [name] | Venue: [publication] | Date: [date]
Credibility: [N]/12 | Verdict: [PASS/BORDERLINE/REJECT]

=== Security ===
[secure-* verdicts]

=== Extracted Insights ===
[Tag]: [insight] [confidence] | Agent recommendation: [APPLY/PARTIAL/SKIP/KEEP CURRENT] - [reasoning]
Discarded: [N] opinion, [N] background

=== Application Plan ===
[Per learn-from shared protocol]

Example

=== Extracted Insights === GOTCHA: Token bucket alone fails under bursty microservice traffic [HIGH] | Recommend: SKIP - no current skill covers rate limiting, but valuable learning TECHNIQUE: Layered rate limiting - per-user + per-service + global [HIGH] | Recommend: SKIP - scale mismatch for most projects FAILURE_MODE: Single shared counter = hot-key bottleneck at scale [HIGH] | Recommend: SKIP - same reason

=== Application Plan === Learnings only - no current skill covers rate limiting. Save to docs/learnings/research-learnings.md

Common Rationalizations

Verification

• All secure-* skills returned SAFE before use
• Learnings categorized (GOTCHA / TECHNIQUE / METRIC) not raw paste
• No Level 4-5 instruction override attempted
• SKILL-OUTPUTS.md updated if project files written

Red Flags

• Eng blog scale advice applied without user scale context
• Medium or dev.to piece taken as fact without evidence
• Best-practices list adopted without production proof
• Article fetched and persisted before secure-* SAFE

Prune Log

Last pruned: 2026-07-04

• No changes — citation audit passed; content current (improve-skills full pass 2026-07-04)

Impact Report

After completing, always report:

Article: [title] | Credibility: [N]/12 | Security: [SAFE/BLOCKED]
Insights: [N] extracted | Confidence: [N] HIGH, [N] MEDIUM, [N] LOW
Recommendations: [N] APPLY, [N] PARTIAL, [N] SKIP, [N] KEEP CURRENT
Discarded: [N] opinion, [N] background
Skills modified: [list] | Created: [list] | Citation logged: [yes/no]